WO2024209565A1 - Terminal and base station - Google Patents

Abstract

This terminal comprises: a control unit that uses a learning model which is classified into a plurality of categories in accordance with a parameter related to the learning model; and a transmission unit that notifies a base station of the categories as information pertaining to the learning model which can be used by the control unit.

Description

Terminals, base stations

This disclosure relates to terminals and base stations that use learning models.

The 3rd Generation Partnership Project (3GPP) is developing specifications for the 5th generation mobile communication system (5G, also known as New Radio (NR) or Next Generation (NG)) and is also developing specifications for the next generation of mobile communication systems, known as Beyond 5G, 5G Evolution or 6G.

Release 18 discusses Artificial Intelligence (AI)/Machine Learning (ML). The introduction of AI/ML models (hereinafter also referred to as learning models) is expected to improve Channel State Information (CSI) feedback, beam management (BM), and positioning. How to configure or instruct the processing capabilities of such learning models to terminals (User Equipment, UE) or base stations (gNodeB, gNB) is being considered (Non-Patent Document 1).

“Study on Artificial Intelligence (AI)/Machine Learning (ML) for NR Air Interface”, RP-221348, 3GPP TSG RAN Meeting #96, 3GPP, June 6-9, 2022

The learning models set by the network including the gNB vary widely in terms of size, complexity, number, etc. available to the UE. In such a case, if the UE notifies the network including the gNB in detail of parameters such as size, complexity, number, etc. of the learning models available to the UE, there is a risk that limited communication resources (not limited to frequency-direction resources, but also including time-direction resources, for example) will be strained.

The present disclosure has been made in light of these circumstances, and aims to provide a terminal that can notify a learning model that the terminal can use, using limited communication resources, and a base station that can request such notification.

One aspect of the disclosure is a terminal that includes a control unit (control unit 230) that uses learning models that are classified into multiple categories according to parameters related to the learning models, and a transmission unit (transmission/reception unit 210) that notifies a base station of the categories as information about learning models that the control unit can use.

One aspect of the disclosure is a base station that includes a control unit (control unit 130) that classifies learning models into a plurality of categories according to parameters related to the learning models, and a transmission unit (transmission/reception unit 110) that transmits a message to a terminal requesting categories of learning models that the terminal can use.

FIG. 1 is a diagram showing the overall configuration of a wireless communication system. FIG. 2 shows a diagram illustrating frequency ranges used in a wireless communication system. FIG. 3 is a diagram showing an example of the configuration of a radio frame, a subframe, a slot, and a symbol used in a radio communication system. FIG. 4 is a functional block diagram of the base station. FIG. 5 is a functional block diagram of the terminal. FIG. 6 is a sequence diagram relating to notification of the size of a learning model. FIG. 7 is a sequence diagram relating to notification of the size of a learning model. FIG. 8 is a sequence diagram relating to notification of the complexity of a learning model. FIG. 9 is a sequence diagram relating to notification of the complexity of a learning model. FIG. 10 is a sequence diagram relating to notification of the number of learning models. FIG. 11 is a sequence diagram relating to notification of the number of learning models. FIG. 12 is a diagram illustrating an example of a hardware configuration of a base station and a terminal. FIG. 13 is a diagram illustrating an example of the configuration of a vehicle.

The following describes the embodiments with reference to the drawings. Note that identical or similar symbols are used for identical functions and configurations, and descriptions thereof will be omitted as appropriate.

(1) Overall Schematic Configuration of Wireless Communication System The wireless communication system 10 shown in Fig. 1 is a wireless communication system conforming to a method called 5G. On the other hand, the wireless communication system 10 may be a wireless communication system conforming to a method called Beyond 5G, 5G Evolution, or 6G.

The wireless communication system 10 can support Massive Multiple-Input Multiple-Output (Massive MIMO), which generates more directional beams by controlling the wireless signals transmitted from multiple antenna elements, Carrier Aggregation (CA), which bundles together multiple component carriers (CC), and Dual Connectivity (DC), which communicates with two base stations simultaneously.

As shown in FIG. 1, the wireless communication system 10 includes a base station (gNodeB, gNB) 100 connected to a Next Generation-Radio Access Network (NG-RAN) 20, and a terminal (User Equipment, UE) 200 that performs wireless communication with the gNB 100. The NG-RAN 20 is connected to a core network (CN) not shown. The CN is composed of a network function (NF) such as an Access and Mobility Management Function (AMF). Note that the specific configuration of the wireless communication system 10, for example, the number of gNBs 100 and UEs 200, is not limited to the example shown in FIG. 1. Furthermore, the NG-RAN 20 and the CN may simply be expressed as a "network" and may or may not be considered to be included in the wireless communication system 10.

The gNB100 may be a base station in a Centralized-Radio Access Network (C-RAN) configuration having a distributed unit (DU) that has the function of connecting to the UE200, and a central unit (CU) that has the function of connecting to the network. In this case, the gNB100 may be read as a DU or a CU. If the gNB100 is a DU, it may be called a gNB-DU, an Integrated Access and Backhaul (IAB) node, a wireless communication node, etc. Similarly, if the gNB100 is a CU, it may be called a gNB-CU, an IAB donor, etc.

The wireless communication system 10 may also support a plurality of frequency ranges (FRs). That is, as shown in FIG. 2, the wireless communication system 10 may support the following FRs:
・FR1: 410MHz to 7.125GHz
・FR2-1: 24.25GHz to 52.6GHz
・FR2-2: Over 52.6GHz to 71GHz

In FR1, a subcarrier spacing (SCS) of 15, 30 or 60 kHz and a bandwidth (BW) of 5 to 100 MHz may be used. In FR2-1, an SCS of 60 or 120 kHz (which may include 240 kHz) and a BW of 50 to 400 MHz may be used.

In FR2-2, Cyclic Prefix-Orthogonal Frequency Division Multiplexing (CP-OFDM) or Discrete Fourier Transform-Spread-Orthogonal Frequency Division Multiplexing (DFT-S-OFDM) with a larger SCS may be applied to avoid increased phase noise.

Also, as shown in FIG. 3, one slot in the wireless communication system 10 is composed of 14 symbols. If this configuration is maintained, the larger (wider) the SCS is, the shorter the symbol period (and slot period) will be. Note that the SCS is not limited to the frequencies shown in FIG. 3, and may be, for example, frequencies such as 480 kHz and 960 kHz.

In addition, the number of symbols constituting one slot does not necessarily have to be 14 symbols, and may be, for example, 28 or 56 symbols. Furthermore, the number of slots per subframe may differ depending on the SCS.

(2) Functional block configuration of wireless communication system (2.1) Functional block configuration of base station As shown in FIG. 4, the gNB 100 includes a transceiver unit 110, a generation unit 120, and a control unit 130.

The transceiver 110 transmits and receives radio signals to and from the UE 200. The transceiver 110 may include a transmitter that transmits radio signals to the UE 200 or other gNBs 100, and a receiver that receives radio signals from the UE 200 or other gNBs 100. The radio signals include a message generated by the generator 120 and a learning model set by the controller 130.

The generation unit 120 generates a message to be sent to the UE 200. The message may be a UE Capability Enquiry that requests the UE 200's capability information (UE Capability Information) from the UE 200, or it may be another message to be sent to the network side.

In the UE Capability Enquiry, the generation unit 120 may request information on a learning model available to the UE 200 as capability information of the UE 200. The information on the learning model available to the UE 200 may be a parameter (or a maximum value of the parameter) related to the learning model available to the UE 200, or a category classified according to a parameter related to the learning model available to the UE 200. In other words, the parameter (or the maximum value of the parameter) and the category are information indicating a learning model available to the UE 200.

The control unit 130 controls the gNB 100. For example, the control unit 130 controls the transmission and reception of radio signals by the transceiver unit 110 and the generation of messages by the generation unit 120.

The control unit 130 classifies the learning models available to the UE 200 into a number of categories according to the parameters associated with the learning models. The categories may also be called classes.

The parameters may be the size (capacity), complexity, number, etc. of learning models available to UE200. The complexity may be the number of layers, the number of neurons, the size of the training data set, the amount of information required to predict the learning model, etc. The categories are classifications of the above-mentioned parameters according to their numerical values. Details will be described later, but the learning models available to UE200 are classified into three categories (classes), A to C, for example, according to their size.

The above-mentioned parameters and categories are merely examples. The parameters may be the version of the learning model available to UE 200, or the length of time until the validity period set for the learning model expires. The validity period may be understood to be a predetermined period of time after the learning model is trained using teacher data. When the validity period expires, the control unit 130 may retrain the learning model and reset the validity period. There may be two categories, A and B, or four categories, A to D.

The control unit 130 sets the above-mentioned learning model for the UE 200. That is, the transceiver unit 110 transmits the learning model that the control unit 130 sets for the UE 200.

Specifically, the control unit 130 sets the learning model based on the category notified by the UE 200. As will be described in the section on operation examples, if the category notified by the UE 200 is, for example, the above-mentioned category B, the control unit 130 may set the learning model for the UE 200 to category A, which is lower than category B. This is because, even if the category is the same B, the above-mentioned parameters (for example, the size of the learning model) have a range (for example, 1 Mbytes or more and less than 100 Mbytes), so there is a risk that the UE 200 cannot use the size of the learning model set by the control unit 130 (for example, 50 Mbytes) (for example, the maximum learning model that the UE 200 can use is 30 Mbytes). If this problem is not taken into consideration (or if it is not necessary to consider it), the control unit 130 may set the learning model for the UE 200 to category B.

(2.2) Functional Block Configuration of Terminal As shown in FIG. 5 , the UE 200 includes a transceiver unit 210, a generator unit 220, and a controller 230.

The transceiver 210 transmits and receives wireless signals to and from the gNB100. The transceiver 210 may include a transmitter that transmits wireless signals to the gNB100, and a receiver that receives wireless signals from the gNB100. The wireless signals include a message generated by the generator 220.

The generation unit 220 generates a message to be transmitted to the gNB 100. The message may be UE Capability Information of the UE 200, or may be another message to be transmitted to the gNB 100 or the network side.

The transceiver 210 may notify information about a learning model available to the UE 200 by transmitting capability information about the UE 200 generated by the generator 220. The information about the learning model available to the UE 200 may be parameters (or maximum values of parameters) related to the learning model available to the UE 200, or may be a category classified according to parameters related to the learning model available to the UE 200. In other words, the parameters (or maximum values of parameters) and categories are information indicating the learning model available to the UE 200. For the parameters and categories, please refer to the explanation of the gNB 100 and the explanation of the operation example described below.

The control unit 230 controls the UE 200. For example, the control unit 230 controls the transmission and reception of radio signals by the transceiver unit 210 and the generation of messages by the generation unit 220.

The control unit 230 uses a learning model. The control unit 230 installs a learning model that is set (transmitted) from the gNB 100. By using the learning model, the UE 200 can optimize processing such as Channel State Information (CSI) feedback, beam management (BM), and positioning.

UE200 can reduce overhead related to CSI feedback, for example, by using a learning model. Furthermore, UE200 can improve the accuracy of CSI feedback by using a learning model.

UE200 can use a learning model to predict, for example, a BM, a beam of good quality for itself in the future.

For example, in terms of positioning, UE200 can predict its own future location using a learning model instead of actually performing positioning. Furthermore, UE200 can improve the accuracy of positioning using the learning model.

(3) Operation of wireless communication system (3.1) Issues (3.1.1) Issue 1
Parameters related to the learning model, for example, the size (capacity) of the learning model, range widely from several megabytes to several gigabytes. The UE 200 needs to notify the gNB 100 of the size of the available learning model, but when communication resources (not limited to frequency direction resources, including time direction resources, for example) are limited, there is a problem that it is difficult to notify the gNB 100 of the size of the available learning model in detail.

(3.1.2) Issue 2
Parameters related to the learning model, for example, the complexity of the learning model, vary widely, including the number of layers, the number of neurons, the size of the training data set, the amount of information required for predicting the learning model, etc. The UE 200 needs to notify the gNB 100 of the complexity of the available learning model, but when communication resources (not limited to frequency direction resources, but including time direction resources, for example) are limited, there is a problem that it is difficult to notify the gNB 100 of the complexity of the available learning model in detail.

(3.1.3) Issue 3
The parameters related to the learning model, for example, the number of learning models (available to the UE 200) are diverse. The UE 200 needs to notify the gNB 100 of the number of available learning models, but when communication resources (not limited to frequency-direction resources, including time-direction resources, for example) are limited, there is a problem that it is difficult to notify the gNB 100 of the number of available learning models in detail.

(3.2) Operational Examples (3.2.1) Operational Example 1
As shown in Fig. 6, the gNB 100 can request a UE Capability Enquiry from the UE 200. That is, the gNB 100 can request the capability information of the UE 200. Specifically, the gNB 100 can request the size of a learning model that the UE 200 can use.

The sizes of the learning models available to UE 200 are classified into, for example, the following categories (classes).
・Class A: 1Mbytes or less ・Class B: 1Mbytes or more but less than 100Mbytes ・Class C: 100Mbytes or more

As shown in FIG. 6, UE200 may notify the size of the learning model available to UE200 in response to a request from gNB100. In this case, in order to notify the size of the learning model available to UE200, the above-mentioned category may be notified. For example, if the size of the learning model available to UE200 is (maximum) 30 Mbytes, the Class B category may be notified.

The gNB100 is notified that the size of the learning model available to the UE200 is in the Class B category, and is therefore able to set a learning model of a size corresponding to Class A for the UE200. As mentioned above, the gNB100 may also set a learning model corresponding to Class B for the UE200.

As shown in FIG. 7, in response to a request from gNB100, UE200 may notify the maximum size in order to notify the size of the learning model available to UE200. For example, if the size of the learning model available to UE200 is (maximum) 30 Mbytes, the size may be notified as 30 Mbytes. Since gNB100 is notified that the size of the learning model available to UE200 is maximum 30 Mbytes, it can set a learning model of maximum 30 Mbytes for UE200.

As described above, UE200 can notify gNB100 of the parameters (sizes) related to the learning model that UE200 can use as simple categories. This allows UE200 to notify of simple categories instead of complex parameters, reducing the risk of straining communication resources.

(3.2.2) Operation example 2
As shown in Fig. 8, the gNB 100 can request a UE Capability Enquiry from the UE 200. That is, the gNB 100 can request the capability information of the UE 200. Specifically, the gNB 100 can request the complexity of a learning model available to the UE 200.

The complexity of the learning model available to UE 200 is classified into, for example, the following categories (classes).
・Class A: Simple ・Class B: Complex ・Class C: Very complex

The complexity of the learning model that UE200 can use depends, for example, on the number of layers of the learning model, the number of neurons, the size of the training dataset, and the amount of information required to predict the learning model. That is, if these parameters are less than a specified numerical range, the complexity is classified as Class A; if they are within a specified numerical range, the complexity is classified as Class B; and if they are greater than the specified numerical range, the complexity is classified as Class C.

Complexity may be represented by any one of the number of layers of a learning model, the number of neurons, the size of a training dataset, and the amount of information required to predict a learning model. In other words, complexity may be classified by any one of these parameters (number of layers, number of neurons, size of a training dataset, and the amount of information required to predict a learning model). On the other hand, complexity may be classified by taking these parameters into consideration comprehensively.

Note that the layers of the learning model may be, for example, each layer in a neural network. Each layer is composed of an input layer, an intermediate layer, and an output layer, but the number of intermediate layers varies widely, which is related to the complexity. The neurons may be neurons in a neural network. The size of the training dataset may be the size of the teacher dataset for training the neural network.

Information required for predicting the learning model may be, for example, when the learning model is a model related to the positioning of UE200, the Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal-to-Interference-plus-Noise Ratio (SINR) of reference signals (or synchronization signals) such as Positioning Reference Signal (PRS), Sounding Reference Signal (SRS), Channel State Information Reference Signal (CSI-RS), and SS/PBCH Block (SSB), or may be location information of the transmitting/receiving point (TRP), UE antenna boresight, Angle of Arrival (AoA), Angle of Departure (AoD), the position, moving speed, moving direction of UE200, and information related to the environment surrounding UE200.

As shown in FIG. 8, UE200 may notify the complexity of the learning model available to UE200 in response to a request from gNB100. In this case, the above-mentioned categories may be notified in order to notify the complexity of the learning model available to UE200. For example, if the complexity of the learning model available to UE200 is "complex", the category of Class B may be notified.

The gNB100 is notified that the complexity of the learning model available to the UE200 is in the Class B category, and is therefore able to set a learning model of complexity corresponding to Class A for the UE200. As mentioned above, the gNB100 may also set a learning model corresponding to Class B for the UE200.

As shown in FIG. 9, in response to a request from gNB100, UE200 may notify the maximum value of each of the above-mentioned parameters indicating complexity (number of layers, number of neurons, size of training data set, amount of information required for predicting the learning model) in order to notify the complexity of the learning model available to UE200. For example, if the number of layers of the learning model available to UE200 is (maximum) 100, the number of layers may be notified as 100. Since gNB100 is notified that the complexity of the learning model available to UE200 (here, the number of layers represents the complexity) is up to 100, it is possible to set a learning model with up to 100 layers for UE200.

If the complexity is represented by one parameter, only the maximum value of that parameter may be notified. On the other hand, if the complexity is evaluated by comprehensively taking into account multiple parameters, the maximum values of each of the multiple parameters may be notified.

As described above, UE200 can notify gNB100 of the parameters (complexity) related to the learning model that UE200 can use as a simple category. As a result, UE200 notifies of the simple category instead of the complex parameters, which can reduce the risk of straining communication resources.

In particular, since the complexity of the learning model depends on many of the parameters mentioned above (number of layers, number of neurons, size of the training dataset, amount of information required for predicting the learning model), there is a high risk of straining communication resources if detailed notification is attempted. Therefore, it can be said that the effectiveness of notifying simple categories instead of complex parameters is higher than that of Operation Example 1 and Operation Example 3 described below.

(3.2.3) Operation example 3
As shown in Fig. 10, the gNB 100 can request a UE Capability Enquiry from the UE 200. That is, the gNB 100 can request the capability information of the UE 200. Specifically, the gNB 100 can request the number of learning models available to the UE 200.

The number of learning models available to UE 200 is classified into, for example, the following categories (classes).
・Class A: 1 only ・Class B: 2 or more and less than 5 ・Class C: 5 or more

As shown in FIG. 10, UE200 may notify the number of learning models available to UE200 in response to a request from gNB100. In this case, in order to notify the number of learning models available to UE200, the above-mentioned categories may be notified. For example, if the size of the learning models available to UE200 is (maximum) 3, the Class B category may be notified.

The gNB100 is notified that the size of the learning models available to the UE200 is in the Class B category, and can therefore set a number of learning models corresponding to Class A for the UE200. As mentioned above, the gNB100 may also set a learning model corresponding to Class B for the UE200.

As shown in FIG. 11, in response to a request from gNB100, UE200 may notify the maximum number of learning models available to UE200 in order to notify the number of learning models available to UE200. For example, if the number of learning models available to UE200 is (maximum) 3, the number 3 may be notified. Since gNB100 is notified that the number of learning models available to UE200 is up to 3, it can set up to 3 learning models for UE200.

As described above, UE200 can notify gNB100 of the parameters (number) related to the learning model that UE200 can use as simple categories. This allows UE200 to notify of simple categories instead of complex parameters, reducing the risk of straining communication resources.

(4) Other Embodiments The contents of the present invention have been described above in accordance with the embodiments. 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.

In the above disclosure, the learning model optimizes processes such as Channel State Information (CSI) feedback, beam management (BM), and positioning, but is not limited to this. For example, the learning model may minimize call losses, radio link failures (RLF), and unnecessary handovers (HO) of UE200, in other words, optimize the mobility of UE200.

In the above disclosure, the request and notification of the capability information of UE200 may be performed based on the validity period set in the above learning model. This allows for more efficient use of communication resources.

In the above disclosure, learning model may be replaced with another term meaning a similar model, in addition to the AI/ML model.

In the above disclosure, the learning model is set or transmitted by the gNB100, but this is not limited to this. It may be set or transmitted by other configurations on the network side.

In the above disclosure, predictions made by a learning model may be referred to as AI predictions.

In the above disclosure, "use" may be read as "download," "install," "process," "predict," or "control," and "available" may be read as "support." Additionally, "send" may be read as "request," "configure," "instruct," or "notify."

The above operational examples may be combined and applied in a composite manner, provided no contradictions arise.

In the above disclosure, configure, activate, update, indicate, enable, specify, and select may be read as interchangeable. Similarly, link, associate, correspond, and map may be read as interchangeable, and allocate, assign, monitor, and map may also be read as interchangeable.

Furthermore, specific, dedicated, UE-specific, and UE-individual may be read as interchangeable. Similarly, common, shared, group-common, UE-common, and UE-shared may be read as interchangeable.

The block diagrams (FIGS. 4 and 5) 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 connected directly or indirectly (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 any of these.

Furthermore, the above-mentioned gNB100 and UE200 (the device) may function as a computer that performs processing of the wireless communication method of the present disclosure. FIG. 12 is a diagram showing an example of the hardware configuration of the device. As shown in FIG. 12, the device 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 explanation, the term "apparatus" can be interpreted as a circuit, device, unit, etc. The hardware configuration of the apparatus may be configured to include one or more of the devices shown in the figure, or may be configured to exclude some of the devices.

Each functional block of the device (Figures 4 and 5) is realized by any hardware element of the computer device, or a combination of the hardware elements.

Furthermore, each function of the device is realized by loading a specific software (program) onto hardware such as the processor 1001 and memory 1002, causing the processor 1001 to perform calculations, control communications by the communications device 1004, and control at least one of reading and writing data in the 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) that includes an interface with peripheral devices, a control unit, an arithmetic unit, registers, etc.

The processor 1001 also 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. The programs used are those that cause a computer to execute at least some of the operations described in the above-mentioned embodiments. Furthermore, the various processes described above 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.

Memory 1002 is a computer-readable recording medium and may be composed of, for example, at least one of Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), Random Access Memory (RAM), etc. Memory 1002 may also be called a register, cache, main memory, etc. 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, etc. Storage 1003 may also 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 called, 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 structure (e.g., a touch panel).

Furthermore, 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.

Furthermore, 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. Furthermore, 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 this disclosure may be applied to at least one of systems utilizing 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), or other suitable systems and next generation systems enhanced therefrom. Multiple systems may also be applied in combination (e.g., a combination of at least one of LTE and LTE-A with 5G).

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 this disclosure, certain operations that are described as being performed by a base station may in some cases be performed by its upper node. In a network consisting of one or more network nodes having base stations, it is clear that various operations performed for communication with terminals may be performed by at least one of the base station and other network nodes other than the base station (such as, but not limited to, an MME or S-GW). Although the above example shows 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 (such as an MME and an S-GW).

Information, signals (information, etc.) can 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 sent to another device.

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

In addition, software, instructions, information, etc. may be transmitted and received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using at least one of 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 at least one of these wired and wireless technologies is 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 explained 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, the 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 represented using absolute values, may be represented using relative values from a predetermined value, or may be represented using other corresponding information. For example, a radio resource may be indicated by an index.

The names used for the above-mentioned parameters are not limiting in any respect. Furthermore, 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. Base stations may also be referred to by terms such as macrocell, small cell, femtocell, and picocell.

A base station can accommodate one or more (e.g., three) cells (also called sectors). If 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 provide 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)," and "terminal" 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 object, or the moving object itself, etc. The moving object may be a vehicle (e.g., a car, an airplane, etc.), an unmanned moving object (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 include a device that does not necessarily move during communication operations. 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.

Furthermore, the base station in the present disclosure may be interpreted 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 of a base station. Furthermore, terms such as "uplink" and "downlink" may be interpreted as terms corresponding to communication between terminals (for example, "side"). For example, the uplink channel, downlink channel, etc. may be interpreted as a side channel.

Similarly, the mobile station in this disclosure may be interpreted as a base station. In this case, the base station may be configured to have the functions of the mobile station.

A radio frame may be composed of one or more frames in the time domain. Each of the one or more frames in the time domain may be called 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, for example, at least one of the following: 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 (e.g., 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 that corresponds to the radio frame, subframe, slot, minislot, and symbol.

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-encoded 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.

In addition, 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) that constitute the minimum time unit of scheduling may be controlled.

A TTI having a time length of 1 ms may be referred to as 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 referred to as 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 of more than 1 ms, and a short TTI (e.g., a shortened TTI, etc.) may be interpreted as a TTI having a TTI length of 1 ms or more but less than the TTI length of a long TTI.

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, and may be, for example, 12. The number of subcarriers included in an RB may be determined based on the numerology.

Furthermore, 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 also 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.

Furthermore, a resource block may be composed of one or more resource elements (RE). 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 an index of the RB relative to a common reference point of the carrier. PRBs may be defined in a BWP and numbered within that BWP.

The BWP may include a BWP for UL (UL BWP) and a BWP for 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 "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, the cyclic prefix (CP) length, and other configurations can be changed in various ways.

The terms "connected," "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 optical (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 referred to as 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 an element using a designation 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 of distinguishing 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 the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to 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 through translation, such as a, an, and the in English, this disclosure may include that the noun following these articles is 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, search, inquiry (e.g., searching in a table, database, or other data structure), ascertaining something that is deemed to be a "judging" or "determining," 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. Additionally, "judgment" and "decision" can include considering resolving, selecting, choosing, establishing, comparing, etc., to have been "judged" or "decided." In other words, "judgment" and "decision" can include considering some action to have been "judged" or "decided." Additionally, "judgment" 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." The term may also 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."

FIG. 13 shows an example of the configuration of a vehicle 2001. As shown in FIG. 13, the vehicle 2001 includes a drive unit 2002, a steering unit 2003, an accelerator pedal 2004, a brake pedal 2005, a shift lever 2006, left and right front wheels 2007, left and right rear wheels 2008, an axle 2009, an electronic control unit 2010, various sensors 2021-2029, an information service unit 2012, and a communication module 2013.

The drive unit 2002 is composed of, for example, an engine, a motor, or a hybrid of an engine and a motor.

The steering unit 2003 includes at least a steering wheel (also called a handle) and is configured to steer at least one of the front wheels and the rear wheels based on the operation of the steering wheel operated by the user.

The electronic control unit 2010 is composed of a microprocessor 2031, a memory (ROM, RAM) 2032, and a communication port (IO port) 2033. Signals are input to the electronic control unit 2010 from various sensors 2021 to 2027 provided in the vehicle. The electronic control unit 2010 may also be called an ECU (Electronic Control Unit).

Signals from the various sensors 2021 to 2028 include a current signal from a current sensor 2021 that senses the current of the motor, a rotation speed signal of the front and rear wheels acquired by a rotation speed sensor 2022, an air pressure signal of the front and rear wheels acquired by an air pressure sensor 2023, a vehicle speed signal acquired by a vehicle speed sensor 2024, an acceleration signal acquired by an acceleration sensor 2025, an accelerator pedal depression amount signal acquired by an accelerator pedal sensor 2029, a brake pedal depression amount signal acquired by a brake pedal sensor 2026, a shift lever operation signal acquired by a shift lever sensor 2027, and a detection signal for detecting obstacles, vehicles, pedestrians, etc. acquired by an object detection sensor 2028.

The information service unit 2012 is composed of various devices, such as a car navigation system, an audio system, speakers, a television, and a radio, for providing various types of information such as driving information, traffic information, and entertainment information, and one or more ECUs for controlling these devices. The information service unit 2012 uses information acquired from external devices via the communication module 2013, etc., to provide various types of multimedia information and multimedia services to the occupants of the vehicle 1.

The driving assistance system unit 2030 is composed of various devices that provide functions for preventing accidents and reducing the driving burden on the driver, such as a millimeter wave radar, LiDAR (Light Detection and Ranging), a camera, a positioning locator (e.g., GNSS, etc.), map information (e.g., high definition (HD) map, autonomous vehicle (AV) map, etc.), a gyro system (e.g., IMU (Inertial Measurement Unit), INS (Inertial Navigation System), etc.), AI (Artificial Intelligence) chip, and an AI processor, as well as one or more ECUs that control these devices. The driving assistance system unit 2030 also transmits and receives various information via the communication module 2013 to realize driving assistance functions or autonomous driving functions.

The communication module 2013 can communicate with the microprocessor 2031 and components of the vehicle 1 via the communication port. For example, the communication module 2013 transmits and receives data via the communication port 2033 between the drive unit 2002, steering unit 2003, accelerator pedal 2004, brake pedal 2005, shift lever 2006, left and right front wheels 2007, left and right rear wheels 2008, axle 2009, microprocessor 2031 and memory (ROM, RAM) 2032 in electronic control unit 2010, and sensors 2021 to 2028, which are provided on the vehicle 2001.

The communication module 2013 is a communication device that can be controlled by the microprocessor 2031 of the electronic control unit 2010 and can communicate with an external device. For example, it transmits and receives various information to and from the external device via wireless communication. The communication module 2013 may be located either inside or outside the electronic control unit 2010. The external device may be, for example, a base station, a mobile station, etc.

The communication module 2013 transmits the current signal from the current sensor input to the electronic control unit 2010 to an external device via wireless communication. The communication module 2013 also transmits to an external device via wireless communication the following signals input to the electronic control unit 2010: front and rear wheel rotation speed signals acquired by the rotation speed sensor 2022, front and rear wheel air pressure signals acquired by the air pressure sensor 2023, vehicle speed signals acquired by the vehicle speed sensor 2024, acceleration signals acquired by the acceleration sensor 2025, accelerator pedal depression amount signals acquired by the accelerator pedal sensor 2029, brake pedal depression amount signals acquired by the brake pedal sensor 2026, shift lever operation signals acquired by the shift lever sensor 2027, and detection signals for detecting obstacles, vehicles, pedestrians, etc. acquired by the object detection sensor 2028.

The communication module 2013 receives various information (traffic information, signal information, vehicle distance information, etc.) transmitted from an external device, and displays it on an information service unit 2012 provided in the vehicle. The communication module 2013 also stores the various information received from the external device in a memory 2032 that can be used by the microprocessor 2031. Based on the information stored in the memory 2032, the microprocessor 2031 may control the drive unit 2002, steering unit 2003, accelerator pedal 2004, brake pedal 2005, shift lever 2006, left and right front wheels 2007, left and right rear wheels 2008, axles 2009, sensors 2021-2028, and the like provided in the vehicle 2001.

　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.

(Additional Note)
The above disclosure may be expressed as follows:

The first feature is a terminal equipped with a control unit that uses learning models classified into multiple categories according to parameters related to the learning models, and a transmission unit that notifies a base station of the categories as information on learning models that the control unit can use.

The second feature is that in the first feature, the parameter is at least one of the size, complexity, and number of the learning models.

The third feature is that in the second feature, the terminal is characterized in that at least one of the number of layers of the learning model, the number of neurons, the size of the training dataset, and the amount of information required for prediction of the learning model.

The fourth feature is the third feature, in which the transmission unit is a terminal that notifies the control unit of the maximum value available for at least one of the number of layers of the learning model, the number of neurons, the size of the training data set, and the amount of information required for prediction of the learning model.

The fifth feature is that in any one of the first to fourth features, the transmitting unit is a terminal that notifies the category in response to a request from the base station.

The sixth feature is a base station that includes a control unit that classifies learning models into a plurality of categories according to parameters related to the learning models, and a transmission unit that transmits a message to a terminal requesting categories of learning models that the terminal can use.

10 Wireless Communication Systems 20 NG-RAN
100 gNB
110 Transmitter/receiver 120 Generator 130 Controller 200 UE
210 Transmitting/receiving unit 220 Generating unit 230 Control unit 1001 Processor 1002 Memory 1003 Storage 1004 Communication device 1005 Input device 1006 Output device 1007 Bus 2001 Vehicle 2002 Driving unit 2003 Steering unit 2004 Accelerator pedal 2005 Brake pedal 2006 Shift lever 2007 Left and right front wheels 2008 Left and right rear wheels 2009 Axle 2010 Electronic control unit 2012 Information service unit 2013 Communication module 2021 Current sensor 2022 Rotational speed sensor 2023 Air pressure sensor 2024 Vehicle speed sensor 2025 Acceleration sensor 2026 Brake pedal sensor 2027 Shift lever sensor 2028 Object detection sensor 2029 Accelerator pedal sensor 2030 Driving assistance system section 2031 Microprocessor 2032 Memory (ROM, RAM)
2033 communication port

Claims (6)

A control unit that uses a learning model classified into a plurality of categories according to parameters related to the learning model;
a transmission unit that notifies a base station of the category as information of a learning model that the control unit can use;
A terminal comprising: The parameter is at least one of the size, complexity, and number of the learning models.
The terminal according to claim 1. The complexity is at least one of the number of layers of the learning model, the number of neurons, the size of a training data set, and the amount of information required for prediction of the learning model.
The terminal according to claim 2. The transmission unit notifies the control unit of a maximum value available for at least one of the number of layers of the learning model, the number of neurons, the size of a training dataset, and the amount of information required for prediction of the learning model.
The terminal according to claim 3. The transmission unit notifies the category in response to a request from the base station.
The terminal according to claim 1. A control unit that classifies the learning model into a plurality of categories according to parameters related to the learning model;
A transmitter that transmits a message to a terminal requesting a category of a learning model available to the terminal;
A base station comprising:

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