Terminal, wireless communication method, and base station

Abstract

A terminal according to one embodiment of the present disclosure is provided with: a receiving unit that receives information for identifying a model associated with a function or a function associated with the model; and a control unit that determines a model or functionality of an application on the basis of the information, the control unit determining a simultaneous operation of an operation pertaining to the model and an operation pertaining to the functionality on the basis of a specific rule. According to one embodiment of the present disclosure, it is possible to achieve appropriate overhead reduction / channel estimation / resource utilization.

Description

Technical Field

[0001] This disclosure relates to terminals, wireless communication methods, and base stations in next-generation mobile communication systems. Background Technology

[0002] In Universal Mobile Telecommunications System (UMTS) networks, Long Term Evolution (LTE) was standardized for the purpose of further increasing data rates and reducing latency (Non-Patent Document 1). Furthermore, LTE-Advanced (3GPP Rel. 10-14) was standardized for the purpose of further increasing capacity and advancing LTE (Third Generation Partnership Project (3GPP) Release (Rel.) 8, 9).

[0003] The study also examines subsequent systems of LTE (e.g., also known as the 5th generation mobile communication system (5G), 5G+, the 6th generation mobile communication system (6G), New Radio (NR), 3GPP Rel. 15 and later, etc.).

[0004] Existing technical documents

[0005] Non-patent literature

[0006] Non-patent document 1: 3GPP TS 36.300 V8.12.0 “Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Overall description; Stage 2 (Release 8)”, April 2010 Summary of the Invention

[0007] The problem that the invention aims to solve

[0008] Regarding future wireless communication technologies, research is underway on the flexible application of artificial intelligence (AI) technologies such as machine learning (ML) in the control and management of networks / devices.

[0009] As a practical use case for AI models, research is underway on spatial domain downlink (DL) beam prediction, temporal DL beam prediction, and localization. Such beam prediction methods can also be referred to as AI-based beam prediction (beam reporting), AI-based localization, and AI-based beam management (BM). Temporal DL beam prediction can also be described as, for example, channel state information (CSI) prediction in the temporal domain.

[0010] Research is underway on introducing various types of Life Cycle Management (LCM) into the flexible application of AI, but there is insufficient research in this area. When research is insufficient, appropriate overhead reduction, channel estimation, and resource utilization cannot be achieved, raising concerns about inhibiting improvements in communication throughput and quality.

[0011] Therefore, one of the purposes of this disclosure is to provide a terminal, wireless communication method, and base station that can achieve appropriate overhead reduction, channel estimation, and resource utilization.

[0012] Methods for solving problems

[0013] One aspect of the present disclosure relates to a terminal comprising: a receiving unit for receiving information for identifying a model associated with a function, or a function associated with a model; and a control unit for determining, based on the information, an application model or function, wherein the control unit determines, based on specific rules, the simultaneous operation of an operation involved in the model and an operation involved in the function.

[0014] Invention Effects

[0015] According to one method of this disclosure, appropriate overhead reduction / channel estimation / resource utilization can be achieved. Attached Figure Description

[0016] Figure 1 This is a diagram illustrating an example of a framework for managing AI models.

[0017] Figure 2 This is a diagram representing a specific example of an AI model.

[0018] Figure 3 This is a diagram illustrating an example of the relationship between the model and functionality involved in the first embodiment.

[0019] Figure 4A as well as Figure 4B This is a diagram illustrating an example of the relationship between the model and functionality involved in the second embodiment.

[0020] Figure 5A as well as Figure 5B This is a diagram illustrating an example of the relationship between the model and functionality involved in the second embodiment.

[0021] Figures 6A to 6C This is a diagram showing the associated transformations of the LCMs supported by the UE (terminal).

[0022] Figure 7 This is a diagram illustrating an example of the association of LCM supported by the UE in the third embodiment.

[0023] Figure 8 This is a diagram illustrating an example of the association of LCM supported by the UE in the third embodiment.

[0024] Figure 9 This is a diagram illustrating an example of the association of LCM supported by the UE in the third embodiment.

[0025] Figure 10A as well as Figure 10B This is a diagram illustrating an example of the relationship between the model and functionality involved in the fourth embodiment.

[0026] Figure 11A as well as Figure 11B This is a diagram illustrating an example of the relationship between the model and functionality involved in the fourth embodiment.

[0027] Figure 12A as well as Figure 12B This is a diagram illustrating an example of the relationship between the model and functionality involved in the fourth embodiment.

[0028] Figure 13A as well as Figure 13B This is a diagram illustrating an example of the relationship between the model and functionality involved in the fourth embodiment.

[0029] Figure 14 This is a diagram illustrating an example of the schematic structure of a wireless communication system according to one embodiment.

[0030] Figure 15 This is a diagram illustrating an example of the structure of a base station according to one embodiment.

[0031] Figure 16 This is a diagram illustrating an example of the structure of a user terminal according to one embodiment.

[0032] Figure 17 This is a diagram illustrating an example of the hardware structure of a base station and a user terminal according to one embodiment.

[0033] Figure 18 This is a diagram illustrating an example of a vehicle according to one embodiment. Detailed Implementation

[0034] (Application of Artificial Intelligence (AI) technology to wireless communication)

[0035] Regarding future wireless communication technologies, research is underway on the flexible application of AI technologies such as machine learning (ML) for the control and management of networks / devices.

[0036] For example, research is underway on how AI technologies can be flexibly applied by terminals (user terminals, user equipment (UE)) / base stations to improve channel state information (CSI) feedback (e.g., reduce overhead, improve accuracy, prediction), beam management (e.g., improve accuracy, prediction in the time / spatial domain), and location measurement (e.g., improve location estimation / prediction).

[0037] AI models can also output at least one of the following information based on the input information: estimated value, predicted value, selected operation, classification, etc. UE / BS can also input channel state information, reference signal measurements, etc., into the AI ​​model and output high-precision channel state information / measurements / beam selection / location, future channel state information / wireless link quality, etc.

[0038] Additionally, in this disclosure, AI can also be rewritten as an object (also referred to as an object, subject, data, function, program, etc.) having at least one of the following characteristics:

[0039] • Estimation based on observed or collected information;

[0040] • Selection based on observed or collected information;

[0041] • Predictions based on observed or collected information.

[0042] In this disclosure, estimation, prediction, and inference can be rewritten interchangeably. Furthermore, in this disclosure, making an estimate, making a prediction, and inferring can also be rewritten interchangeably.

[0043] In this disclosure, the object may be, for example, a device or apparatus such as a UE or BS. Furthermore, in this disclosure, the object may also correspond to a program / model / entity operating within that device.

[0044] Furthermore, in this disclosure, the AI ​​model can also be rewritten as an object having at least one of the following characteristics:

[0045] • By providing (feeding) information, estimates are produced;

[0046] • By providing information, predict estimated values;

[0047] • Discover characteristics by providing information;

[0048] • By providing information, select an action.

[0049] Furthermore, in this disclosure, AI model can also refer to a data-driven algorithm that uses AI technology to generate a set of outputs based on a set of inputs.

[0050] Furthermore, in this disclosure, AI models, models, ML models, predictive analytics, predictive analytics models, tools, autoencoders, encoders, decoders, neural network models, AI algorithms, schemes, etc., can be rewritten interchangeably. Additionally, AI models can be derived using at least one of regression analysis (e.g., linear regression analysis, multiple regression analysis, logistic regression analysis), support vector machines, random forests, neural networks, deep learning, etc.

[0051] In this disclosure, the autoencoder can also be rewritten with any autoencoder such as a stacked autoencoder or a convolutional autoencoder. The encoder / decoder of this disclosure can also adopt models such as Residual Network (ResNet), DenseNet, and RefineNet.

[0052] Furthermore, in this disclosure, encoder, encoding, encoding / encoded, encoder-based modification / change / control, compression, compression / compressed, generating, and generating / generated can also be rewritten in different ways.

[0053] Furthermore, in this disclosure, the terms decoder, decoding, decoding / being decoded, modification / change / control based on decoder, decompressing, decompressing / being decompressed, reconstructing, and reconstructing / being reconstructed can also be rewritten in different ways.

[0054] In this disclosure, the layers (regarding the AI ​​model) can also be rewritten with the layers (input layer, intermediate layer, etc.) used in the AI ​​model. The layers in this disclosure can also correspond to at least one of the following: input layer, intermediate layer, output layer, batch normalization layer, convolutional layer, activation layer, dense layer, normalization layer, pooling layer, attention layer, dropout layer, fully connected layer, etc.

[0055] In this disclosure, the training methods for AI models can also include supervised learning, unsupervised learning, reinforcement learning, federated learning, etc. Supervised learning can also refer to training the model based on inputs and corresponding labels. Unsupervised learning can also refer to training the model using unlabeled data. Reinforcement learning can also refer to training the model in an interactive environment based on inputs (in other words, states) and feedback signals (in other words, rewards) generated from the model's outputs (in other words, actions).

[0056] In this disclosure, the terms "generation," "computation," and "derivation" can be rewritten interchangeably. In this disclosure, the terms "implementation," "running," "operation," and "execution" can also be rewritten interchangeably. In this disclosure, the terms "training," "learning," "updating," and "retraining" can also be rewritten interchangeably. In this disclosure, the terms "inference," "after-training," "formal utilization," and "actual utilization" can also be rewritten interchangeably. In this disclosure, "signal" can also be rewritten interchangeably with "signal / channel."

[0057] Figure 1 This is a diagram illustrating an example of a framework for managing an AI model. In this example, the stages associated with the AI ​​model are represented by blocks. This example also represents the lifecycle management (LCM) of an AI model.

[0058] The data collection phase corresponds to the phase of collecting data for the generation / updating of AI models. The data collection phase may also include data preparation (e.g., deciding which data to migrate for model training / inference), data migration (e.g., migrating data to entities performing model training / inference (e.g., UE, gNB), etc.).

[0059] Additionally, data collection can also refer to the processing of data collected by network nodes, management entities, or UEs for the purpose of AI model training / data analysis / inference. In this disclosure, processing and procedures can also be rewritten interchangeably. Furthermore, in this disclosure, collection can mean obtaining a dataset (e.g., usable as input / output) for training / inference of the AI ​​model based on measurements (channel measurements, beam measurements, wireless link quality measurements, location estimation, etc.).

[0060] In this disclosure, offline field data can be data collected from the field (real world) and used for offline training of AI models. Furthermore, in this disclosure, online field data is data collected from the field (real world) and used for online training of AI models.

[0061] In the model training phase, the model is trained based on the data transferred from the collection phase (training data). This phase may also include data preparation (e.g., implementation of data preprocessing, cleaning, formatting, transformation, etc.), model training / validation (validation), model testing (e.g., confirming whether the trained model meets performance thresholds), model exchange (e.g., migration of models for distributed learning), and model deployment / update (deploying / updating the model to entities performing model inference), etc.

[0062] In addition, AI model training can also refer to the processing of a trained AI model that is trained using data-driven methods to obtain a model for inference.

[0063] Furthermore, AI model validation can also refer to a training subprocess that evaluates the quality of an AI model using a dataset different from the one used during model training. This subprocess helps in selecting model parameters that generalize beyond the datasets used during model training.

[0064] Furthermore, AI model testing can also refer to a subprocess of training used to evaluate the performance of the final AI model using a different dataset than that used in model training / validation. Additionally, unlike validation, testing can be performed without relying on the subsequent model (tuning).

[0065] In the model inference phase, model inference is performed based on the data transferred from the collection phase (inference data). This phase may also include data preparation (e.g., implementation of data preprocessing, cleaning, formatting, transformation, etc.), model inference, model monitoring (e.g., monitoring the performance of model inference), model performance feedback (providing model performance feedback to the entities used for model training), and output (providing the model's output to the actors).

[0066] Additionally, AI model inference can also refer to the process of using a trained AI model to generate a set of outputs from a set of inputs.

[0067] Furthermore, a UE-side model can also refer to an AI model whose inference is entirely implemented within the UE. Similarly, a network-side model can refer to an AI model whose inference is entirely implemented within the network (e.g., gNB).

[0068] Furthermore, a one-sided model can also refer to a UE-side model or a network-side model. A two-sided model can also refer to a pair of AI models performing joint inference. Here, joint inference can include AI inference performed jointly across the UE and the network; for example, it could be that the first part of the inference is performed by the UE, and the remaining part is performed by the gNB (or vice versa).

[0069] In addition, AI model monitoring can also refer to the processing used to monitor the inference performance of AI models, and can be interchanged with model performance monitoring, performance monitoring, etc.

[0070] Additionally, model registration can also mean assigning a version identifier to a model and enabling it to execute (register) by compiling it into specific hardware used during the inference phase. Furthermore, model deployment can also mean distributing a fully developed and tested runtime image (or execution environment image) of a model to the target where inference is implemented (e.g., UE / gNB) (or enabling it on that target).

[0071] The Actor phase can also include action triggers (e.g., deciding whether to trigger an action on other entities), feedback (e.g., providing feedback on training data / inference data / performance feedback, etc.).

[0072] Furthermore, training models for mobility optimization, for example, can be performed within the network (NW) through operations, administration, and maintenance (OAM) / gNodeB (gNB). In the former case, interoperability, large-capacity storage, operator manageability, and model flexibility (feature engineering, etc.) are advantageous. In the latter case, the elimination of model update latency and data exchange for model decompression is advantageous. Inference for the aforementioned models can also be performed, for example, within the gNB.

[0073] The entities used for training / inference can also vary depending on the use case (in other words, the function of the AI ​​model). The functions of an AI model can also include beam management, beam prediction, autoencoder (or information compression), CSI feedback, location localization, etc.

[0074] For example, for AI-assisted beam management based on measurement reports, OAM / gNB can be used for model training, and gNB can be used for model inference.

[0075] The model is trained for AI-assisted UE positioning, or for the Location Management Function (LMF), and then the LMF is used for model inference.

[0076] For CSI feedback / channel estimation using autoencoders, model training can also be performed by OAM / gNB / UE, and model inference can be performed jointly by gNB / UE.

[0077] For AI-assisted beam management based on beam measurement or AI-assisted UE-based positioning, the OAM / gNB / UE can perform model training, and the UE can perform model inference.

[0078] Additionally, model activation can also refer to activating an AI model for a specific function. Model deactivation can also refer to deactivating an AI model for a specific function. Model switching can also refer to deactivating the currently activated AI model for a specific function and activating a different AI model.

[0079] Furthermore, model transfer can also refer to the distribution of an AI model over the air interface. This distribution may include distributing one or both of the following: parameters of a model structure known on the receiving side, or a new model with parameters. Additionally, the distribution may include a complete model or a portion of the model. Model download can also refer to model migration from the network to the UE. Model upload can also refer to model migration from the UE to the network.

[0080] Figure 2 This is a diagram representing a specific example of an AI model. In this example, the UE and the NW (e.g., the basestation (BS)) are able to identify models #1 and #2 (details about the models may not be fully understood). The UE, for example, reports the performance of model #1 and the performance of model #2 to the NW, and the NW instructs the UE on the AI ​​model used.

[0081] (Lifecycle Management (LCM))

[0082] In future wireless communication systems (e.g., after Rel. 18), the introduction of multiple LCMs is being studied.

[0083] These multiple LCMs can be either function-based LCMs or model ID-based LCMs. Function-based LCMs can be called function-based LCMs, and model ID-based LCMs can be called model-ID-based LCMs.

[0084] In a functionally based LCM, the NW (e.g., base station / NW node) can indicate the operations involved in the AI / ML functionality (e.g., activation, deactivation, rollback operation, handover at least one). Here, the rollback operation can be based on the information (input information) used when applying the corresponding AI function, or based on the information (input information) used when applying the corresponding AI function and operations without AI functions.

[0085] The UE can perform model-level LCM (e.g., model switching and model selection at least one) in the indicated functionality.

[0086] In terms of functionality, which model is activated / deactivated can also be transparent.

[0087] Notifications regarding supported functionalities can also be obtained through reports of UE Capability Information.

[0088] In model ID-based LCM, the NW (e.g., base station / NW node) can use the model ID to indicate the operation (e.g., activation, deactivation, rollback, handover) involved in a dedicated (individual) AI / ML model.

[0089] UE can also perform model-level LCM (e.g., at least one of model switching and model selection) based on NW instructions.

[0090] In NW, models can also be defined using a model identifier (ID).

[0091] (Scenarios involved in model delivery / transfer)

[0092] Regarding model distribution / transfer, three scenarios are defined in the existing context when the model is trained by NW.

[0093] [Scenario y]

[0094] First, NW trains the model and distributes it to UEs outside of 3GPP networks based on multiple vendors' offline engineering.

[0095] Next, the UE reports on the support for the distributed models (this step can also be called model identification).

[0096] [Scenario z2]

[0097] First, NW trains its models using offline projects from multiple vendors and saves them in proprietary formats. A proprietary format can also mean a format defined by each vendor.

[0098] Next, the UE reports the utilization of the stored model in the 3GPP network (this step can also be called model identification).

[0099] Next, model transfer will be performed.

[0100] Next, the UE reports on support for the migrated model (this step can also be called model identification).

[0101] [Scenario z4]

[0102] First, the UE reports the supported model structure (this step can also be called model identification).

[0103] Next, the model parameters of the model structures supported by NW migration.

[0104] Next, the UE reports on support for the migrated model (this step can also be called model identification).

[0105] (functionality identification)

[0106] As a functional identification process, consider, for example, the UE reporting specific conditions in its UE capabilities (capability information). In this case, the NW can set the corresponding functionality based on the reported conditions.

[0107] Here, functionality can refer to the features / feature groups (FG) that can be utilized in AI / ML activated by a certain setting, such as a set of RRC parameters / LPP parameters. This setting can be supported based on conditions indicated by UE capabilities. Furthermore, functionality can also refer to the units that the NW can control on the UE side during LCM operations (activation / deactivation / switching).

[0108] Functional LCM-based operations can also be controlled based on the settings of features / feature groups available in the aforementioned AI / ML. Here, we examine the signaling used to support this functional LCM-based operation (signaling for activation / deactivation / switching).

[0109] In addition, the UE can also report updates to applicable functionalities. For example, it is necessary to investigate the mechanism for updating the applicable models after the models have been identified.

[0110] (Model identification)

[0111] Models identified by their model IDs can be associated with settings / conditions / additional conditions (specific scenarios, stations, datasets, etc.). A model can also represent a unit that the NW can control on the UE side during LCM operations (activation / deactivation / switching).

[0112] LCM operations based on model ID can also be controlled based on the identified model. Here, the model can also be associated with specific settings / conditions related to the UE capabilities of features / feature groups that can be utilized in AI / ML, as well as additional conditions determined / identified between the UE side and the NW side.

[0113] Furthermore, we envision that the identification process and control unit differ in both function-based LCMs and model ID-based LCMs. Here, we investigate sharing the activation / deactivation / switching process in both function-based and model ID-based LCMs.

[0114] In addition, the following types are studied as part of the model recognition process.

[0115] Type A: Model information and model ID can be associated without signaling. The UE reports the supported model IDs to the NW. That is, the mapping between model ID and model information can be recognized by the NW and UE without signaling. The NW and UE identify the corresponding model information based on the received model ID.

[0116] • Type B1: Model information is reported from the UE to the NW via the air interface (signaling). Model identification begins at the UE, and the NW assists (undertakes) the remaining steps of model identification. During the model identification process, a model ID can be assigned to the model.

[0117] • Type B2: Model information is reported from the NW to the UE via the air interface (signaling). Model identification begins at the NW, and the UE assists (undertakes) the remaining steps of model identification. During the model identification process, a model ID can be assigned to the model.

[0118] (Meta-information)

[0119] The UE may also receive at least one of the following as metadata.

[0120] Furthermore, the term "meta-information" in this disclosure is merely one example; meta-information can also refer to at least one of specific setting information, scenario information, environmental information, auxiliary information, and model information. In this disclosure, meta-information, setting information, scenario information, environmental information, auxiliary information, and model information can also be interchanged.

[0121] The metadata used by the UE / NW may also include at least one of the following: information related to NW setup / deployment, information related to the environment, information related to the AL / ML model on the NW side, and information related to the model requested by the NW.

[0122] Information related to NW setup / deployment may also include information related to antenna settings.

[0123] Information related to antenna settings may also represent at least one of the following: horizontal / vertical antenna element / panel number, port number, antenna spacing, antenna position, panel position, and transceiver unit (TxRU) mapping.

[0124] Information related to NW setup / deployment may also include information related to beam settings, for example.

[0125] Information related to beam setting may include, for example, at least one of beamwidth, number of beams, and beam direction.

[0126] Information related to NW setup / deployment may also include information related to TRP.

[0127] Information related to beam setting may include, for example, the height of the TRP and at least one of the relative positions of multiple TRPs.

[0128] Information related to the environment may also include information related to the deployment scenario.

[0129] Information related to the deployment scenario may also represent at least one of Urban Macro (UMa), Urban Micro (Umi), and Indoor Hotspot (InH).

[0130] Information related to the environment may also include information related to indoors or outdoors.

[0131] Information relating to indoor or outdoor conditions can also represent, for example, indoor / outdoor probabilities.

[0132] Information related to the environment can also be information related to objects surrounding the UE / base station.

[0133] Information related to objects surrounding the UE / base station can also represent, for example, the deployment of objects surrounding the UE / base station.

[0134] Information related to the environment may also include, for example, the scenario setting format (meta-information) described below.

[0135] Use cases that utilize AI models can also be associated with scenario setting formats consisting of long-term features.

[0136] Furthermore, long-term characteristics can be interchanged with short-term / medium-term / long-term characteristics, or simply with other characteristics. Additionally, scenario setting formats can be interchanged with meta-information, meta-information formats, scenario and configuration formats, scenario structure formats, scenario formats, configuration formats, use case formats, environment formats, and meta-formats. Moreover, formats can be interchanged with types, patterns, data, and settings.

[0137] The above features may also include a combination of one or more of the following elements:

[0138] • Scenarios / models (Urban Macro (UMa)), Urban Micro (Umi)), Indoor, Outdoor, Indoor Hotspot (InH) etc.).

[0139] • Frequency / frequency range.

[0140] • Parameter set (numerology) (or subcarrier spacing).

[0141] • Distribution / set of typical channel parameters (e.g., inter-site distances (ISD)), gNB height, delay spread, angular spread, Doppler spread, etc.) in a single scenario / model.

[0142] UE distribution.

[0143] UE speed.

[0144] ·UE trajectory.

[0145] • Number of transmit beams / number of receive beams.

[0146] •UE rotation mode.

[0147] • gNB / UE antenna structure (e.g., transmit and receive antenna vectors).

[0148] • Number of cells / Number of sectors.

[0149] ·bandwidth.

[0150] •UE payload.

[0151] • Channel quality (e.g., RSRP, SINR).

[0152] • Beam configuration ID.

[0153] • Physical Cell ID (PCI)

[0154] • Global Cell ID (GCI)

[0155] • Absolute Radio Frequency Channel Number (ARFCN)

[0156] • Probability of line-of-sight (LOS) / non-line-of-sight (NLOS)

[0157] It is also possible to expect a model where the scenario setting format associated with the UE being set / registered is consistent with the UE's setting / status.

[0158] Furthermore, we can expect a model where the scenario setting format associated with UE activation is consistent with the UE's settings / status.

[0159] Regarding the correspondence between use cases and scenario setting formats, this can be specified in the specification or the UE can be notified of information related to this correspondence. Furthermore, regarding the features included in the scenario setting format corresponding to the use case, this can be specified in the specification or the UE can be notified of information related to these features.

[0160] Information related to the AL / ML model on the NW side may also include information related to the paired models that can be used on the NW side.

[0161] Information related to the paired models available on the NW side can also represent, for example, the paired decoder used for CSI compression.

[0162] Information related to the AL / ML model on the NW side may also include information related to preprocessing / postprocessing available on the NW side.

[0163] Information related to preprocessing / postprocessing available on the NW side may include, for example, at least one of quantization / dequantization, DFT transform, IDFT transform, FFT transform, and IFFT transform.

[0164] (UE assistance information)

[0165] UE can report auxiliary information / metadata (meta-information) of AI / ML models.

[0166] Auxiliary information may also include at least one of the following: user status.

[0167] • Overheating assistance information;

[0168] • DRX parameter preference;

[0169] • Priority order related to maximum aggregate bandwidth;

[0170] • Preference for the maximum number of MIMO layers.

[0171] The AI / ML model can also be an AI / ML model that is registered, configured, compiled, or activated in the UE.

[0172] Auxiliary information / metadata of the AI / ML model can be sent along with the beam information, or it can be sent in place of the beam information. This beam information can, for example, be information related to the UE's antenna / beam.

[0173] The auxiliary information / metadata of the AI / ML model may also be at least one of the following.

[0174] The auxiliary information / metadata of an AI / ML model can also be the ID of the AI / ML model.

[0175] The ID of an AI / ML model can also be a global / local AI / ML model ID.

[0176] The auxiliary information / metadata of AI / ML models can also be information related to the applicable bandwidth corresponding to the AI / ML model ID.

[0177] This bandwidth can also be expressed as the minimum / maximum bandwidth that can be applied.

[0178] The bandwidth-related information may include, for example, information representing a band field indicator (e.g., "freqBandIndicatorNR"). This information representing the band field indicator may also be represented by a specific number of bits (e.g., 10 bits).

[0179] The bandwidth-related information may also include, for example, information indicating the bandwidth of the RS associated with the corresponding AI / ML model (e.g., “supportedBandwidth”).

[0180] Information indicating the bandwidth of the RS associated with the corresponding AI / ML model can also represent the frequency of each frequency range (e.g., FR1 / FR2 (FR2-1 / FR2-2) / FR3 / FR4 / FR5).

[0181] The auxiliary information / metadata of AI / ML models can also be information related to the applicable region corresponding to the AI / ML model ID.

[0182] Information related to the applicable region corresponding to the AI / ML model may also include at least one of the following (a list of information):

[0183] • Region ID.

[0184] • The global ID of the (NR) cell.

[0185] • (NR) Physical cell ID (Identifier).

[0186] • ARFCN (Absolute Radio Frequency Channel Number).

[0187] • Global ID (ECGI) of Evolved Cell.

[0188] The region ID may also include the global ID of the NR cell, the physical cell ID of the NR, and at least one of the ARFCNs.

[0189] The auxiliary information / metadata of AI / ML models can also be antenna settings / beam information corresponding to the AI / ML model ID.

[0190] (KPI)

[0191] Regarding performance monitoring of AI models, research is underway on public key performance indicators (KPIs).

[0192] Below is an initial list of common KPIs used to evaluate the performance of AI / ML-based models:

[0193] • Performance

[0194] Intermediate KPIs

[0195] • Link-level and system-level performance

[0196] Generalization performance

[0197] Over-the-air expenses,

[0198] • Cost of auxiliary information

[0199] • The overhead of data collection

[0200] • The overhead of model delivery / transfer

[0201] • The overhead of signaling associated with other AI / ML models,

[0202] • Inference complexity.

[0203] • Computational complexity of model inference: floating point operations (FLOPs, where 's' is a lowercase letter) (this refers to the amount of floating-point operations).

[0204] • Computational complexity of pre- and post-processing.

[0205] • Model complexity (number of parameters / data size (e.g., Mbytes) etc.)

[0206] • Training complexity

[0207] • LCM-related complexity and storage overhead

[0208] • Delay (e.g., inference delay).

[0209] In addition, the above KPIs are just one example, and other KPIs can be added to the list (e.g., KPIs associated with model training, use case-specific KPIs considered for the provided use cases, etc.).

[0210] The performance-related KPIs mentioned above can also be called performance KPIs.

[0211] (Functional related information)

[0212] The UE can also report the supported (or supported) functionalities.

[0213] Functionality can also be associated with specific information. Furthermore, functionality can also contain specific information. Additionally, functionality can belong to specific information.

[0214] In this disclosure, the terms "be associated with", "include", "belong to", and "correspond to" can be used interchangeably.

[0215] This specific information may also be at least one of the following. Furthermore, specific information and functionally related information may be rewritten from one another.

[0216] This specific information can also be information related to the conditions / settings that can be applied.

[0217] This specific information could be, for example, an applicable NW setting. For instance, the applicable parameters involved in this NW setting could be at least one of system information parameters and auxiliary information parameters.

[0218] This specific information could also be, for example, an applicable UE setting. For instance, the applicable parameters involved in this UE setting could also be higher-layer (e.g., RRC) parameters.

[0219] This specific information may also be information related to the applicable scenario. This information may also be information about at least one of the following: NLOS / LOS, UE distribution, SINR, RSRP, bandwidth, frequency, indoor / outdoor, and deployment scenario (e.g., at least one of city macro (UMa), city micro (Umi), and indoor hotspot (InH).

[0220] This specific information could also be, for example, information related to the deployment that can be applied. This information could also be, for example, information indicating at least one of the following: applicable antenna settings (e.g., the number of antenna elements / panels in the horizontal / vertical direction, the number of ports, the antenna spacing, the antenna position, the panel position, the transceiver unit (TxRU) mapping), beam settings (e.g., at least one of the beamwidth, the number of beams, and the beam direction), and TRP information (e.g., the height of the TRP, at least one of the relative positions of multiple TRPs).

[0221] This specific information could be, for example, information related to the applicable site. This information could also be, for example, information representing at least one of the following: area ID, NR cell global ID, NR physical cell ID, a specific frequency (e.g., ARFCN), or ECGI.

[0222] This specific information could, for example, be information related to the paired models that can be applied. The paired models could also be, for example, two (or more) NW-side models (a combination of models). This information could also, for example, be information about the paired models used for CSI compression.

[0223] This specific information could be, for example, information related to the applicable time. This information could also be, for example, information representing the applicable period (cycle / interval / duration). This period could also be represented using a specific time unit (e.g., slot / symbol / subslot / millisecond / second).

[0224] Regarding the applicable conditions / settings, they can be predefined in the specification or determined / identified using a specific ID / token. For example, the applicable settings / situations / conditions can be specified as specific parameters (e.g., test parameters) or represented using a specific ID / token.

[0225] Functional performance (e.g., prediction accuracy, location error) can be conceived as being better than (e.g., higher than) a specific threshold under applicable conditions / settings. These specific conditions / settings could, for example, be conditions / settings in a specific test.

[0226] Regarding this threshold / performance requirement, it can be specified in advance in the specification, or it can be determined / identified using a specific ID / token.

[0227] When the threshold / performance requirement is based on a specific ID / token, each vendor / operator is able to define and utilize the desired threshold / performance.

[0228] (analyze)

[0229] <Question 1>

[0230] As mentioned above, a UE can have one or more AI / ML models (or simply models) for a single function. That is, it is possible to associate more than one model with a single function. In the case of multiple models associated with a function on the UE side, it is unclear how, in function-based LCM, the number of models that can be associated with a particular function is determined / judged.

[0231] <Question 2>

[0232] Furthermore, for an AI / ML-enabled feature, it's possible to define more than one functionality. The model can generate a series of data (a set of outputs). Therefore, it's possible to associate / include one or more functionalities with the model on the UE side. In this case, consider the following issues.

[0233] • Whether a model is explicitly associated with one or more functionalities.

[0234] • How to establish the criteria / rules for this clarification.

[0235] • Clarity of the number of functionalities associated with a model for different assumptions.

[0236] <Question 3>

[0237] To support both function-based LCM and model-based LCM, it is necessary to clarify whether a UE supports simultaneous operation of both function-based LCM and model-based LCM.

[0238] For example, when simultaneous operation is not supported, it is necessary to clarify how the UE decides / judges one of the operation between the functional LCM and the model-based LCM.

[0239] On the other hand, it is envisioned that, if this simultaneous operation is supported, the UE-side model may sometimes be identified as a model and sometimes as a functionality.

[0240] Therefore, it is necessary to clarify whether a model identified as a model on the UE side is simultaneously identified within a function, or whether it is associated with an identified function. In this case, the UE needs to specify which operation to apply, or the priority rules for applying the operation.

[0241] Thus, if the UE operation based on the model and functional correlation is unclear, appropriate overhead reduction / channel estimation / resource utilization cannot be achieved, and there are concerns about inhibiting communication throughput / communication quality improvement.

[0242] Therefore, the inventors of this invention conceived of methods for solving these problems.

[0243] (Various rewrites, etc.)

[0244] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. The wireless communication methods involved in each embodiment can be applied individually or in combination.

[0245] In this disclosure, "A / B" and "at least one of A and B" may be rewritten as each other. In addition, in this disclosure, "A / B / C" may also mean "at least one of A, B and C".

[0246] In this disclosure, terms such as notification, activation, deactivation, indication (or indication), selection, configuration, update, and determination can be overridden. Similarly, terms such as support, control, ability to control, operation, and ability to operate can also be overridden.

[0247] In this disclosure, Radio Resource Control (RRC), RRC parameters, RRC messages, higher-level parameters, fields, Information Elements (IE), settings, etc., can also be modified interchangeably. In this disclosure, Medium Access Control (MAC) elements (MAC ControlElement (CE)), update commands, activation / deactivation commands, etc., can also be modified interchangeably.

[0248] In this disclosure, higher-layer signaling may be, for example, any one of Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information, other messages (e.g., messages from the core network such as positioning protocol messages such as NR Positioning Protocol A (NRPPa) / LTE Positioning Protocol (LPP) messages), or combinations thereof.

[0249] In this disclosure, MAC signaling may also use, for example, a MAC Control Element (MACCE) or a MAC Protocol Data Unit (PDU). Broadcast information may also be, for example, a Master Information Block (MIB), a System Information Block (SIB), a Minimum System Information (Remaining Minimum System Information (RMSI)), or Other System Information (OSI).

[0250] In this disclosure, physical layer signaling may also be, for example, downlink control information (DCI), uplink control information (UCI), etc.

[0251] In this disclosure, indexes, identifiers (IDs), indicators, resource IDs, etc., can be interchanged. In this disclosure, sequences, lists, sets, groups, clusters, subsets, etc., can also be interchanged.

[0252] In this disclosure, the following terms are used: panel, UE panel, panel group, beam, beam group, precoder, uplink (UL) transmitting entity, transmission / reception point (TRP), base station, spatial relation information (SRI), spatial relation, SRS resource indicator (SRI), control resource set (CORESET), physical downlink shared channel (PDSCH), codeword (CW), transport block (TB), reference signal (RS), antenna port (e.g., demodulation reference signal (DMRS)) port, antenna port group (e.g., DMRS port group), group (e.g., spatial relation group, code division multiplexing (CDM) group, reference signal group, CORESET group, physical uplink control channel). Channel (PUCCH) groups, PUCCH resource groups, resources (e.g., reference signal resources, SRS resources), resource sets (e.g., reference signal resource sets), CORESET pools, downlink transmission configuration indication state (TCI state) (DL TCI state), uplink TCI state (UL TCI state), unified TCI state, common TCI state, quasi-co-location (QCL) and QCL concept can also be rewritten.

[0253] In this disclosure, CSI-RS, Non-Zero Power (NZP) CSI-RS, Zero Power (ZP) CSI-RS, and CSI Interference Measurement (CSI-IM) can be rewritten interchangeably. Furthermore, CSI-RS can also include other reference signals.

[0254] In this disclosure, the RS being measured / reported may also mean the RS being measured / reported for the purpose of CSI reporting.

[0255] In this disclosure, timing, moment, time, time slot, sub-time slot, code element, subframe, etc., can also be rewritten to each other.

[0256] In this disclosure, direction, axis, dimension, domain, polarization, polarization component, etc., can also be rewritten in relation to each other.

[0257] In this disclosure, estimation, prediction, and inference can be rewritten interchangeably. Furthermore, in this disclosure, making an estimate, making a prediction, and inferring can also be rewritten interchangeably.

[0258] In this disclosure, the autoencoder, encoder, decoder, etc., can also be rewritten using at least one of the following: model, ML model, neural network model, AI model, AI algorithm, etc. Furthermore, the autoencoder can be rewritten with any autoencoder, such as a stacked autoencoder or a convolutional autoencoder. The encoder / decoder of this disclosure can also employ models such as Residual Network (ResNet), DenseNet, RefineNet, etc.

[0259] In this disclosure, terms such as bit, bit string, bit sequence, sequence, value, information, value obtained from a bit, and information obtained from a bit can be interchanged.

[0260] In this disclosure, the layers (for the encoder) can also be rewritten with layers (input layers, intermediate layers, etc.) used in the AI ​​model. The layers in this disclosure can also be equivalent to at least one of the following: input layer, intermediate layer, output layer, batch normalization layer, convolutional layer, activation layer, dense layer, normalization layer, pooling layer, attention layer, random deactivation layer, fully connected layer, etc.

[0261] In this disclosure, RSRP can also be rewritten with any parameters related to receive power / receive quality (e.g., RSRQ, SINR, CSI).

[0262] In this disclosure, RS can also be, for example, CSI-RS, SS / PBCH block (SS block (SSB)), etc. Furthermore, RS index can also be CSI-RS resource indicator (CSI-RS Resource Indicator (CRI)), SS / PBCH block indicator (SS / PBCH Block Indicator (SSBRI)), etc.

[0263] In this disclosure, channel measurement / estimation may be performed using at least one of the following: Channel State Information Reference Signal (CSI-RS), Synchronization Signal (SS), Synchronization Signal / Physical Broadcast Channel (SS / PBCH) block, DeModulation Reference Signal (DMRS), and Sounding Reference Signal (SRS).

[0264] In this disclosure, the receiving beam designation, the number of receiving beams, the index of the receiving beams, the selection of the receiving beams, the setting of the receiving beams, and the indication of the receiving beams can all be modified interchangeably. In this disclosure, the receiving beam, the transmitting beam, the DL receiving beam, the DL transmitting beam, the transmitting beam, and the pair of receiving beams can also be modified interchangeably. In this disclosure, the transmitting / receiving beams can also be modified interchangeably with the transmitting / receiving beams used for beam prediction and the transmitting / receiving beams used for CSI measurement / reporting for beam prediction.

[0265] In this disclosure, "functionality" can refer to the purpose of a model or the physical meaning of its inputs / outputs. Multiple models may also have the same functionality. Monitoring (performance verification) / activation / deactivation / toggle / rollback / update can also be instructed (controlled) on a function-by-function basis (e.g., per function).

[0266] Furthermore, a model ID can also refer to an identifier for a model (or a collection of models). Multiple models can also be assigned the same model ID in an actual deployment. In this case, these models are actually different models (e.g., different numbers of layers, etc.), but can still be treated as the same model.

[0267] In this disclosure, the use case may also include AI / ML for at least one of enhancement for CSI feedback / beam management / localization. Furthermore, the use case may also include other novel use cases for AI / ML.

[0268] In this disclosure, the cooperation level may also include levels x / y / z. Here, cooperation level refers to the cooperation level between NW-UE, and each level may also mean the following.

[0269] Level x: No collaboration.

[0270] • Level y: Signaling-based collaboration without model transfer.

[0271] • Level z: Signaling-based collaboration with model transfer.

[0272] Here, other aspects (whether there are model updates, whether model training / inference is supported) can also be applied to define the level of collaboration.

[0273] Furthermore, in this disclosure, the model ID can be interchanged with the ID of the metadata (or a set of metadata). The metadata (or metadata ID) can also be associated with information related to model / functionality applicability, environment, UE / gNB settings, etc.

[0274] In this disclosure, functionality may also be referred to simply as "function".

[0275] In this disclosure, functionality, function, functional ID, model, and model ID can also be overridden.

[0276] In this disclosure, updates, reports, and sending can also be overridden.

[0277] In this disclosure, metadata, assistance information, sensing information, KPIs, performance KPIs, UE status, and status can be interchanged.

[0278] In this disclosure, monitoring and evaluation can be interchanged. Furthermore, monitoring and evaluating validity / applicability can also be interchanged.

[0279] In this disclosure, decisions, judgments, the application of specific operations, and decision-making can be rewritten in various ways.

[0280] In this disclosure, entities, specific entities, UE, NW, gNB, and LMF can be rewritten in different ways.

[0281] In this disclosure, NW, LMF, gNB, and BS can also be rewritten.

[0282] In this disclosure, the UE-side model and the UE can also be rewritten to each other.

[0283] In this disclosure, the model, the UE-side model, the logical model, and the physical model can also be rewritten to each other.

[0284] In this disclosure, model / functionality can refer to a data-driven algorithm that applies AI / ML techniques and generates a series of outputs based on a series of inputs.

[0285] In this disclosure, a logical model may also refer to a model that has been identified and assigned a model ID. Furthermore, a physical model may refer to the actual implementation of the logical model.

[0286] In this disclosure, functionality (AI / ML functionality) may also refer to a specific AI / ML-enabled feature (AI / ML-enabled feature) / FG (feature group) that is activated by specific settings.

[0287] In this disclosure, performance metrics and monitoring metrics can also be rewritten.

[0288] In this disclosure, “model / functionality / LCM non-repetition” may also mean at least one of the following.

[0289] • For a given model, only one functionality is associated, and no other functionalities are associated.

[0290] • For a given functionality, associate it with only one model and not with other models;

[0291] • The model and functionality are not directly related.

[0292] In this disclosure, "model / functionality / LCM repetition" may also mean at least one of the following:

[0293] • For a certain model / functionality, in addition to being associated with more than one functionality / model, it is also associated with more than one other model / functionality;

[0294] • For one model / functionality, associate it with two or more different functionalities / models;

[0295] • The model and functionality are directly linked.

[0296] In this disclosure, the operations involved in functionality and the LCM based on functionality can be rewritten interchangeably. Furthermore, the operations involved in the model and the LCM based on the model can also be rewritten interchangeably.

[0297] In this disclosure, association, correspondence, and mapping can also be rewritten.

[0298] In this disclosure, a model / functionality contains a functionality / model, and a model / functionality is associated with a functionality / model; these can also be rewritten in relation to each other.

[0299] In this disclosure, terms such as discard, abort, cancel, truncate, rate match, postpone, and do not send can be rewritten.

[0300] In this disclosure, each implementation method / option can be applied individually or in combination.

[0301] (Wireless communication method)

[0302] The embodiments disclosed herein can be broadly categorized as follows.

[0303] • First implementation method: The relationship between model and functionality (based on functionality).

[0304] • Second implementation method: The relationship between model and functionality (model-based).

[0305] • Third implementation method: Model-based / Function-based LCM (operational separately).

[0306] • Fourth implementation method: Model-based / Function-based LCM (simultaneous operation).

[0307] The following describes various implementation methods based on these descriptions.

[0308] <First Implementation Method>

[0309] The first implementation corresponds to problem 1 / problem 2 above and involves the relationship between model and functionality (based on functionality).

[0310] For a given functionality, one or more (i.e., more than 1) models can be associated. Figure 3 This is a diagram illustrating an example of the relationship between the model and functionality involved in the first embodiment. For example, such as... Figure 3 As shown, multiple models (models #1 to #n) can be associated with a single functionality #A. n can be any integer, representing, for example, the maximum number of models that can be associated with a single functionality. Additionally, #x can represent the ID used to determine / identify the functionality, and #n can represent the ID used to determine / identify the model.

[0311] Furthermore, numbers / letters can be used as IDs for identifying models / functions, but this is not a limitation, and can be appropriately modified. For example, letters can be used as IDs for identifying models, and numbers can be used as IDs for identifying functions. The following implementation methods are similar.

[0312] When a UE has multiple models for a single function, the following methods 1-1 to 1-2 can also be considered / applied.

[0313] Method 1-1

[0314] The number of models that can be associated with a function (maximum number) can be set / indicated by NW or defined in advance by the specification. The number of models that can be associated with a function (maximum number) can also be determined based on at least one of the following options.

[0315] <Option 1>

[0316] The maximum number can be the same (common) for the functionality of all UE sides.

[0317] <Option 2>

[0318] The maximum number can be the same (common) for all use cases or for all AI / ML-enabled features.

[0319] <Option 3>

[0320] The maximum number can vary depending on the functionality.

[0321] Among the above options, the maximum number can also be determined based on at least one of the following: the capabilities reported by the UE (UE capabilities), the functional association information reported by the UE, the complexity of the UE-side model, and the functional requirements.

[0322] With this maximum number set / indicated, the range of the number of models that can be associated with a function (how many models are associated in the maximum number) can also depend on the actual implementation of the UE.

[0323] Methods 1-2

[0324] The number of models that can be associated with a function can be set / indicated by NW, or it can be defined in advance by the specification. The number of models that can be associated with a function can also be determined based on at least one of the following options.

[0325] <Option 1>

[0326] • NW-based instructions.

[0327] Before and after function identification, the NW can use higher-layer signaling (RRC / MAC CE) / physical layer signaling (DCI) to set / indicate the number of models associated with a certain function for the UE. This setting / indication can be sent from the NW on a per-UE / use case / AI / ML supported function / function.

[0328] <Option 2>

[0329] • Based on predefined criteria.

[0330] For example, the number of models associated with a certain function can be predefined in the specification according to the features / functionalities supported by each use case / AI / ML.

[0331] <Option 3>

[0332] • Based on actual UE implementation.

[0333] In the above methods 1-1 to 1-2, the UE can expect to receive a setting / instruction for one or both of the maximum number / quantity of models associated with a certain function.

[0334] According to the first implementation described above, the UE can appropriately identify the association between a certain functionality and the model.

[0335] <Second Implementation Method>

[0336] The second implementation corresponds to problem 1 / problem 2 above and involves the relationship between model and functionality (based on model).

[0337] You can also associate one or more (i.e., more than 1) functionalities with a model. The type of this association is used as an example. Examples 1 and 2 are provided below. Figure 4A as well as Figure 4B This diagram illustrates an example of the relationship between the model and functionality involved in the second embodiment. More specifically, Figure 4A Corresponding to case 1, Figure 4B This corresponds to scenario 2.

[0338] <Scenario 1>

[0339] Scenario 1 illustrates a case where the UE-side model encompasses multiple functionalities. For example... Figure 4A As shown, functionality #A is associated with (contains) multiple models (models #1 to #3). Furthermore, functionality #B is associated with (contains) multiple models (models #3 to #5). Here, if we focus on model #3, it can be said that model #3 is associated with multiple functionalities (functionalities #A to #B).

[0340] <Scenario 2>

[0341] Scenario 2 illustrates a situation where the UE-side model contains multiple functionalities, or where the UE-side model is identified as having multiple functionalities. For example... Figure 4B As shown, model #1 contains (associates) multiple functionalities (functionalities #A~#B).

[0342] The second implementation method can be divided into methods 2-1 to 2-2.

[0343] Method 2-1

[0344] Method 2-1 describes a specific instance where a model is associated with one or more (i.e., more than 1) functionalities.

[0345] In the case where a model is associated with more than one functionality, at least one of the following Atl1~Alt2 can be considered / applied. Figure 5A as well as Figure 5B This is a diagram illustrating an example of the relationship between the model and functionality involved in the second embodiment.

[0346] <alt1>

[0347] A model can be associated with only one functionality (it can not be associated with multiple (more than 2) functionalities).

[0348] Figure 5A This is a diagram illustrating an example of Alt1 considering case 1 above. For example... Figure 5A As shown, functionality #A is associated with (contains) multiple models (models #1 to #3). Similarly, functionality #B is associated with (contains) multiple models (models #4 to #5). Here, if we focus on model #3, then model #3 is associated with functionality #A; however, it is not associated with functionality #B. That is, each model (models #1 to #5) is associated with only one functionality (functionality #A or #B).

[0349] Figure 5B This is a diagram illustrating an example of Alt1 considering scenario 2 described above. For example... Figure 5B As shown, model #1 contains only one functionality (functionality #A) (associated with it).

[0350] <alt2>

[0351] A model can also be associated with multiple functionalities.

[0352] Figure 4A This is a diagram representing an example of Alt2 considering case 1 above. For example... Figure 4A As shown, functionality #A is associated with (contains) multiple models (models #1 to #3). Furthermore, functionality #B is associated with (contains) multiple models (models #3 to #5). Here, if we focus on model #3, we can say that model #3 is associated with multiple functionalities (functionalities #A to #B).

[0353] Figure 4B This is a diagram illustrating an example of Alt2 considering scenario 2 above. For example... Figure 4B As shown, model #1 contains (associates) multiple functionalities (functionalities #A~#B).

[0354] Method 2-2

[0355] Method 2-2 illustrates how a UE-side model is associated with UE operations under multiple functional scenarios.

[0356] In functional identification and functional-based LCM, when a UE-side model is associated with multiple functionalities, whether simultaneous operation of different functionalities is supported can be determined based on at least one of the following options.

[0357] <Option 1>

[0358] Simultaneous operation of multiple functionalities associated with the same model is not supported.

[0359] (Option 1-1)

[0360] UEs do not need to expect the UE-side models to be activated simultaneously based on different functions.

[0361] (Options 1-2)

[0362] When the UE receives activation commands / requests from multiple functionalities simultaneously in the UE-side model, the priority of these functionalities can be determined based on at least one of the following: Here, the activation command / request can be included within a functionality and is transparent to the NW.

[0363] [Options 1-2-1]

[0364] Priority can also be set / indicated by NW via higher-layer signaling (RRC / MAC CE / LPP) / physical layer signaling (DCI).

[0365] [Options 1-2-2]

[0366] Priority can follow rules defined in advance in the specification. Priority rules can also be determined based on, for example, the chronological order of activation commands / requests (receive order), use cases / AI / ML support functions / functionality / LCM process / number of configured functional IDs.

[0367] [Options 1-2-3]

[0368] Priority can also be determined / judged by the UE (autonomously). In this case, the UE can also notify the NW of the determined / judged priority via higher-layer signaling (RRC / MAC CE) / physical layer signaling (UCI).

[0369] <Option 2>

[0370] At the same time, multiple functionalities associated with the model can be activated simultaneously.

[0371] <Option 3>

[0372] Whether different simultaneous operations are supported can also depend on the UE's capabilities (reporting).

[0373] In method 2-2, the functional priority / priority can also be rewritten with the model priority / priority.

[0374] "change"

[0375] Multiple functionalities associated with the same processing component (e.g., AI / ML model) may not be desired to be activated simultaneously. Therefore, the UE may also report that it does not expect a specific functionality to be activated at the same time. The specific functionality can be determined based on the UE's capabilities, defined in advance in the specification, or set / indicated from the NW via higher-layer signaling / physical layer signaling.

[0376] According to the second embodiment described above, the UE can appropriately identify the association between a certain function and a model. Furthermore, when a model is associated with multiple functions, the UE can appropriately determine the support for simultaneous operation of multiple functions.

[0377] <Third Implementation Method>

[0378] The third implementation corresponds to problem 3 above and involves model-based / function-based LCM (separate operation).

[0379] For all UEs, it is envisioned that the UE supports model-based LCM / functionality-based LCM.

[0380] In the third embodiment, UE operation is described when the UE does not support simultaneous operation of model-based LCM and function-based LCM. Since the simultaneous operation is not supported, scheduling can be performed more easily because it is based on either model-based or function-based LCM.

[0381] refer to Figures 6A to 6C This indicates the types of LCMs supported. Figures 6A to 6C This is a diagram showing the changes in the associated LCMs supported by the UE (terminal).

[0382] A UE can also have only one type of LCM: model-based or function-based. For example, such as Figure 6A As shown, a UE can also have (associated with) multiple models (models #1~#3) as a model-based LCM. Or, as... Figure 6B As shown, the UE can also have (can be associated with) multiple functionalities (functionality #1~#2) as a functionality-based LCM.

[0383] Furthermore, the UE can also have (and can be associated with) both model-based and function-based LCMs. For example, such as Figure 6C As shown, a UE can also have (can be associated with) multiple models (models #1~#2) as a model-based LCM, and have (can be associated with) a functionality #1 (can also be multiple) as a functionality-based LCM.

[0384] exist Figures 6A to 6C The number of models / functionalities associated with the UE is merely an example and may be modified as appropriate.

[0385] The third implementation method can be divided into methods 3-1 to 3-3. Figures 7 to 9 This is a diagram illustrating an example of the association of LCM supported by the UE in the third embodiment.

[0386] Method 3-1

[0387] This method describes situations where the model and functionality do not overlap, and situations where the model and functionality are not directly related. For example... Figure 7 As shown, imagine a scenario in use case #1 (e.g., beam prediction) where the UE, as a function-based LCM, has (is associated with) multiple functions (functions #1~#2). Furthermore, the UE can also act as a model-based LCM with multiple models, which is independent of the function-based LCM.

[0388] In this case, the UE can determine which LCM to use (selecting a specific function / model) based on specific rules described later. The UE can switch between function-based and model-based LCMs according to specific rules.

[0389] Method 3-2

[0390] This method illustrates situations where the model and functionality overlap, and situations where the model and functionality are directly related. For example... Figure 8 As shown, in a certain use case #1 (e.g., beam prediction), we envision a situation where the UE is a function-based LCM with (associated) multiple functionalities (functionalities #1~#2).

[0391] Here, each of the functionalities #1 to #2 has (and is associated with) multiple models (models #1 to #2). That is, multiple models (models #1 to #2) are repeatedly associated with each of the multiple functionalities (functionalities #1 to #2). In this case, the UE can determine which LCM to utilize (selecting specific models and functionalities) based on specific rules.

[0392] In addition, as other examples, such as Figure 9 As shown, imagine a scenario in use case #1 (e.g., beam prediction) where the UE, as an LCM (model-based or function-based), has multiple (associated) models (models #1~#2) and multiple functions (functions #1~#2). In this scenario, multiple models and multiple functions are associated within a single LCM (a common / repeating LCM). In this case, the UE can also determine which LCM to utilize (selecting a specific model or function) based on specific rules.

[0393] Thus, for a given use case, when the UE has repeatedly identified models and functionalities, the UE can identify (recognize) both the model and the functionality for each use case. In this case, the UE can determine which model / functionality to activate based on the set / indicated model / functionality ID.

[0394] Method 3-3

[0395] This method describes the operation when the UE does not support simultaneous operation of model-based LCM and function-based LCM.

[0396] The UE can also determine whether to apply model-based LCM or function-based LCM based on at least one of the specific rules (options 1-3) shown below. Options 1-3 below can also be applied before identifying the model / function.

[0397] <Option 1>

[0398] • NW-based instructions.

[0399] The NW can send instructions to the UE to notify it of priorities related to the management of model / functionality (model-level scale / functionality-level scale). These instructions can be UE-specific or cell-specific. Furthermore, these instructions can be sent using higher-layer signaling (RRC / MAC CE / SIB) / physical layer signaling (DCI).

[0400] <Option 2>

[0401] • Based on predefined criteria.

[0402] Option 2 can be further divided into Alt1~Alt5.

[0403] (Alt1)

[0404] The LCM applied can be determined based on the collaboration level. For example, a function-based LCM can be selected when a collaboration level (level x / y) is set / indicated, while a model-based LCM can be selected when a collaboration level (level z) is set / indicated.

[0405] (Alt2)

[0406] The LCM applied can also be determined based on the use case / sub-use case / feature. For example, a function-based LCM can be selected when the use case is localization, and a model-based LCM can be selected when the use case is CSI feedback.

[0407] In Alt2, when multiple use cases / features are supported, a specific LCM can be selected / applied preferentially for a particular use case. For example, if multiple use cases include CSI feedback, that CSI feedback can be preferentially (independent of other use cases) selected using the model-based LCM.

[0408] (Alt3)

[0409] The LCM applied can also be determined based on whether the UE supports model migration. For example, if the UE does not support model migration, a function-based LCM can be selected; if the UE supports model migration, a model-based LCM can be selected.

[0410] (Alt4)

[0411] The applied LCM can also be determined based on whether the UE is requested model information from the NW. For example, a function-based LCM can be selected if the UE is not requested from the NW, and a model-based LCM can be selected if the UE is requested from the NW.

[0412] (Alt5)

[0413] The LCM applied can also be determined based on any combination of Alt1 to Alt4.

[0414] <Option 3>

[0415] • UE-based decisions.

[0416] The LCM applied can also be determined / judged by the UE (autonomously). In this case, the UE can also notify the NW of the determined / judged LCM (information related to the determined / judged LCM / information used to identify the determined / judged LCM) via higher-layer signaling (RRC / MAC CE) / physical layer signaling (UCI).

[0417] According to the third implementation described above, the UE can appropriately control model-based / function-based LCM (individual operation).

[0418] <Fourth Implementation Method>

[0419] The fourth implementation corresponds to problem 3 above and involves model-based / function-based LCM (simultaneous operation).

[0420] As mentioned above, all UEs are designed to support model-based LCM / functionality-based LCM.

[0421] In the fourth embodiment, UE operation is described when the UE supports simultaneous operation of model-based LCM and function-based LCM. Depending on whether this simultaneous operation is supported, good flexibility between model and function can be achieved.

[0422] When the UE supports simultaneous operation of model-based LCM and function-based LCM, the model on the UE side can be identified as a model, or it can be identified within a function.

[0423] The fourth implementation method can be divided into methods 4-1 to 4-2. Figures 10A-10B to Figures 13A-13B This is a diagram illustrating an example of the relationship between the model and functionality involved in the fourth embodiment.

[0424] Method 4-1

[0425] Whether a model is identified as a model / assigned a model ID, and whether it is identified within a function / associated with a function that is also identified, is determined / judged based on at least one of the following options.

[0426] <Option 1>

[0427] UEs may also not expect model / functionality identification and model / functionality-based LCM duplication in the UE-side model.

[0428] (Option 1-1)

[0429] UE does not expect that the model associated with the identified functionality will be assigned a model ID and be identified by the model.

[0430] For example, such as Figure 10A As shown, there are multiple models (models #1 to #3) associated with the identified functional #A. In this case, the UE does not expect model #3 to be assigned a model ID for model identification.

[0431] In addition, such as Figure 10B As shown, model #1 contains (and is associated with) identified functionality #A and functionality #B. In this case, the UE does not expect model #1 to be assigned a model ID for model identification.

[0432] (Options 1-2)

[0433] UEs do not need to expect the identified model to be associated with the identified functionality.

[0434] For example, such as Figure 11A As shown, there are multiple models (models #1 to #2) associated with the identified function #A. In this case, the UE does not expect the model identified by a specific ID to be associated with the identified function #A.

[0435] In addition, such as Figure 11B As shown, the identified model #1 contains (is associated with) multiple functionalities (functionalities #A~#B). In this case, the UE does not expect functionality ID to be assigned to functionality #A for functionality identification.

[0436] Option 1 above is not complex and can be easily implemented based on model / functionality.

[0437] <Option 2>

[0438] The UE-side model can be recognized as a model while simultaneously being identified within a specific function. The UE can expect the UE-side model to be recognized as a model while also being identified within a specific function.

[0439] (Option 2-1)

[0440] All UE-side models (physical / logical models) associated with the identified functionality can also be further identified at the model level (unit).

[0441] For example, such as Figure 12A As shown, for the identified functionality #A, multiple models (models #1 to #3) are associated. In this case, a model ID can also be assigned to model #3 for model identification. The UE can also expect a model ID to be assigned to model #3 for model identification.

[0442] In addition, such as Figure 12B As shown, model #1 contains (is associated with) identified functionality #A and functionality #B. In this case, a model ID can be assigned to model #1 for model identification. The UE can also expect a model ID to be assigned to model #1 for model identification.

[0443] In option 2-1, since the model is identified after the functionality is identified, there is a temporal sequence between the two (functionality and model).

[0444] (Option 2-2)

[0445] All models identified on the UE side can also be further associated with the identified functionalities.

[0446] For example, such as Figure 13A As shown, multiple models (models #1 to #2) are associated with the identified functionality #A. In this case, a model identified by a specific ID can also be associated with the identified functionality #A. The UE can also expect a model identified by a specific ID to be associated with the identified functionality #A.

[0447] In addition, such as Figure 13B As shown, the identified model #1 contains (is associated with) multiple functionalities (functionalities #A~#B). In this case, a functional ID can be assigned to functionality #A for functionality identification. The UE can also expect a functional ID to be assigned to functionality #A for functionality identification.

[0448] In option 2-2, functional identification and model identification are performed simultaneously. The result of simultaneous identification of both (functionality and model) is that the relationship between the two is mapped (associated).

[0449] Option 2 above ensures flexibility in switching between model and functionality.

[0450] Method 4-2

[0451] As explained in Option 2 of Method 4-1, the UE-side model is identified as a model and can also be identified within a function. Therefore, when scheduling the UE-side model, we envision the case where the model-based LCM and the function-based LCM overlap.

[0452] Method 4-2-1

[0453] In such a scenario, if the UE-side model is scheduled via either model-based LCM or function-based LCM, the UE can determine which level of LCM to apply based on at least one of the following options: Specifically, the UE can determine / judge the LCM to apply based on at least one of the following options 1-1 to 1-3.

[0454] <Option 1-1>

[0455] • Based on predefined criteria.

[0456] For example, if a model is identified by model level and functionality level, model-based LCM can always be prioritized. Furthermore, priority can also be determined based on collaboration level, use case, feature, or whether model transfer is supported.

[0457] <Options 1-2>

[0458] • NW-based instructions.

[0459] The UE can also receive instructions from the NW indicating the intention to apply either a model-based LCM or a function-based LCM. In this case, the UE can also additionally report at least one of the following Alt1 to Alt3 information to the NW.

[0460] (Alt1)

[0461] • The model ID associated with the identified functional model.

[0462] • Model information (e.g., model parameters / model structure) associated with the identified functional model.

[0463] Alt1 information can also be reported for functional identification.

[0464] (Alt2)

[0465] • Functional ID associated with the functionality of the identified model.

[0466] • Functional association information that is associated with the functionality of the identified model.

[0467] Alt2 information can also be reported for model recognition.

[0468] (Alt3)

[0469] • Information used to indicate which of the following is preferred / requested by the UE: model-based LCM or function-based LCM.

[0470] NW indication (information related to an indication from the NW) can be the same as or different from UE report (request information reported by the UE).

[0471] <Options 1-3>

[0472] • Based on the user's autonomous decision-making / judgment.

[0473] The UE may also report at least one of the following information to the NW by making its own decision / judgment.

[0474] • Information related to the LCM used (information used to determine either model-based or functionality-based LCM).

[0475] • The model ID / functionality ID of the LCM used (either model-based or functionality-based).

[0476] For example, if a model is identified through model #A and that model #A is associated with an identified function #X, the UE can report model #A and notify that model #A is scheduled at the model level.

[0477] <Variation Example>

[0478] To indicate the period during which a model is scheduled via model-based / functionality-based LCM, a certain validity duration can also be set.

[0479] Method 4-2-2

[0480] In addition, when the model on the UE side is simultaneously scheduled through model-based LCM and function-based LCM, the UE can also apply the following options 1 to 2.

[0481] <Option 1>

[0482] The UE may also add at least one of the following Alt1 to Alt2 auxiliary information to the NW report.

[0483] (Alt1)

[0484] • The model ID associated with the identified functional model.

[0485] • Model information (e.g., model parameters / model structure) associated with the identified functional model.

[0486] Alt1 information can also be reported for functional identification.

[0487] In Alt1, in the UE-side model, if there is a duplication between the function-based LCM and the model-based LCM, the model ID / model information associated with the function can also be reported during the identification of the function.

[0488] (Alt2)

[0489] • Functional ID associated with the functionality of the identified model.

[0490] • Functional association information that is associated with the functionality of the identified model.

[0491] Alt2 information can also be reported for model recognition.

[0492] In Alt2, in the UE-side model, if there is a duplication between the function-based LCM and the model-based LCM, the functional ID / functional association information associated with the model can also be reported during model identification.

[0493] <Option 2>

[0494] When a UE is scheduled using a model and there is overlap between the functional LCM and the model-based LCM in the model on the UE side, it can also perform at least one of the following Alt1 to Alt3 operations.

[0495] (Alt1)

[0496] The UE-side model (which can also be rewritten as UE) can perform both model-level and function-level operations simultaneously.

[0497] (Alt2)

[0498] The UE-side model can also discard one of the model-level operations and the function-level operations according to specific rules. Specific rules can be, for example, the predefined rules described in option 1-1 of method 4-2-1.

[0499] (Alt3)

[0500] The UE-side model can also delay one of the model-level operations and the function-level operations according to specific rules. These specific rules could be, for example, the predefined rules described in option 1-1 of method 4-2-1.

[0501] According to the fourth implementation described above, the UE can appropriately control model-based / function-based LCM (simultaneous operation).

[0502] <Supplement>

[0503] [Supplement 1: AI Model Information]

[0504] In this disclosure, AI model information may also refer to information that includes at least one of the following:

[0505] • Information on the input / output of the AI ​​model.

[0506] • Information used for preprocessing / postprocessing of inputs / outputs in AI models.

[0507] Information about the parameters of the AI ​​model.

[0508] • Training information used for AI models (training information).

[0509] • Inference information used in AI models.

[0510] • Performance information related to the AI ​​model.

[0511] Here, the input / output information of the above AI model may also include information related to at least one of the following:

[0512] • Contents related to input / output data (e.g., RSRP, SINR, amplitude / phase information in the channel matrix (or precoding matrix), information related to Angle of Arrival (AOA), information related to Angle of Departure (AoD), and location information).

[0513] • Auxiliary information of the data (also known as meta-information).

[0514] • The type of input / output data (e.g., immutable value, floating-point number).

[0515] • Bit width of input / output data (e.g., 64 bits for each input value).

[0516] • Quantization interval (quantization step size) of input / output data (e.g., 1dBm for L1-RSRP);

[0517] • The range of input / output data that can be taken (e.g., [0, 1]).

[0518] Furthermore, in this disclosure, information relating to AOA may include information relating to at least one of the azimuth angle of arrival and the zenith angle of arrival (ZOA). Similarly, information relating to AoD may include, for example, information relating to at least one of the azimuth angle of departure and the zenith angle of depature (ZoD).

[0519] In this disclosure, location information can also be location information related to the UE / NW. Location information may also include at least one of the following: information obtained using a positioning system (e.g., Global Navigation Satellite System (GNSS), Global Positioning System (GSP), etc.) (e.g., latitude, longitude, altitude), information of the BS adjacent to (or serving) the UE (e.g., BS / cell identifier (ID)), distance between BS and UE, direction / angle of the BS (UE) as seen from the UE (BS), coordinates of the BS (UE) as seen from the UE (BS) (e.g., X / Y / Z axis coordinates), etc.), and the UE's specific address (e.g., Internet Protocol (IP) address), etc. The UE's location information may not be limited to information based on the location of the BS, but may also be information based on a specific point.

[0520] Location information can also include information related to its actual implementation (e.g., antenna location / position / orientation, antenna panel location / orientation, number of antennas, number of antenna panels, etc.).

[0521] Location information may also include mobility information. Mobility information may also include information representing at least one of the following: information representing mobility type, UE movement speed, UE acceleration, UE movement direction, etc.

[0522] Here, the mobility type can also be equivalent to at least one of the following: fixed location UE, movable / moving UE, no mobility UE, low mobility UE, middle mobility UE, high mobility UE, cell-edge UE, not-cell-edge UE, etc.

[0523] In this disclosure, the environmental information (used for the data) can be information related to the environment in which the data is acquired / utilized, such as information equivalent to frequency information (band ID, etc.), environmental type information (information indicating at least one of indoor, outdoor, urban macro (UMa) and urban micro (Umi)), information indicating line of sight (LOS) / non-line of sight (NLOS)), etc.

[0524] Here, LOS can mean that the UE and BS are in an environment where they are visible to each other (or there are no obstructions), while NLOS can mean that the UE and BS are not in an environment where they are visible to each other (or there are obstructions). Information representing LOS / NLOS can be soft values ​​(e.g., the probability of LOS / NLOS) or hard values ​​(e.g., either LOS / NLOS).

[0525] In this disclosure, metadata can mean, for example, information related to input / output information suitable for an AI model, information related to data that has been acquired / can be acquired, etc. Specifically, metadata can also include information related to the beams of RS (e.g., CSI-RS / SRS / SSB, etc.) (e.g., the pointing angle of each beam, 3dB beamwidth, shape of the pointing beam, number of beams), antenna layout information of the gNB / UE, frequency information, environmental information, metadata ID, etc. Furthermore, metadata can also be used as input / output for an AI model.

[0526] The preprocessing / postprocessing information used for the input / output of the above AI model may also include at least one of the following related information:

[0527] • Whether to apply normalization (e.g., Z-score normalization, min-max normalization).

[0528] • Parameters used for normalization (e.g., mean / variance for Z-score normalization, minimum / maximum for minimum-maximum normalization).

[0529] • Whether a specific numerical transformation method is applied (e.g., one-hot encoding, label encoding, etc.).

[0530] • Selection rules for whether to use it as training data.

[0531] For example, the input information can be normalized using the Z-score (x new = (x - μ) / σ. Here, μ is the mean of x, and σ is the standard deviation) as the preprocessed normalized input information x. new Input to an AI model can also be used to process the output y from the AI ​​model. out Post-processing is applied to obtain the final output y.

[0532] The parameters of the aforementioned AI model may also include at least one of the following related information:

[0533] • Weight information in AI models (e.g., the coefficients (coupling coefficients) of neurons).

[0534] • The structure of the AI ​​model.

[0535] • Types of AI models that serve as model components (e.g., Residual Network (ResNet)), DenseNet, RefineNet, Transformer models, CRBlock, Recurrent Neural Network (RNN)), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)).

[0536] • The functionality of the AI ​​model as a model component (e.g., decoder, encoder).

[0537] In addition, the weight information in the aforementioned AI model may also include at least one of the following related information:

[0538] • Bit width (size) of weight information.

[0539] • Quantization interval of weight information.

[0540] • Granularity of weight information.

[0541] • The range of acceptable weight information.

[0542] • The parameters of the weights in the AI ​​model.

[0543] • Information about the difference between the AI ​​model before and after the update (in the case of an update).

[0544] • Weight initialization methods (e.g., zero initialization, random initialization (based on normal / uniform / truncated normal distribution), Xavier initialization (for sigmoid function), He initialization (for Rectified Linear Units (ReLU))).

[0545] In addition, the structure of the AI ​​model described above may also include at least one of the following related information.

[0546] • Number of floors.

[0547] • Layer type (e.g., convolutional layer, activation layer, dense layer, normalization layer, pooling layer, attention layer).

[0548] • Layer information.

[0549] • Timing-specific parameters (e.g., bidirectionality, time step).

[0550] • Parameters used for training (e.g., the type of feature (L2 normalization, random deactivation feature, etc.), and where to place the feature (e.g., after which layer)).

[0551] The aforementioned layer information may also include at least one of the following related information:

[0552] • The number of neurons in each layer.

[0553] • Kernel size.

[0554] • The stride used for pooling / convolutional layers.

[0555] • Pooling methods (MaxPooling, AveragePooling, etc.).

[0556] • Information about the residual block.

[0557] • Number of heads.

[0558] • Normalization methods (batch normalization, instance normalization, layer normalization, etc.).

[0559] • Activation functions (Sigmoid, tanh function, ReLU, Leaky ReLU information, Maxout, Softmax).

[0560] An AI model can be included as a component of other AI models. For example, an AI model can also be processed in the following order: ResNet as model component #1, a transformer model as model component #2, a dense layer, and a normalization layer.

[0561] The training data used for the above AI model may also contain at least one of the following related information:

[0562] • Information used to optimize the algorithm (e.g., the type of optimization (Stochastic Gradient Descent (SGD)), AdaGrad, Adam, etc.), and optimization parameters (learning rate, momentum information, etc.).

[0563] • Information about the loss function (e.g., information related to the metrics of the loss function (Mean Absolute Error (MAE)), Mean Square Error (MSE)), Cross-entropy loss, NLLLoss, Kullback-Leibler (KL) divergence, etc.)).

[0564] • Parameters that should be frozen for training purposes (e.g., layers, weights).

[0565] • Parameters that should be updated (e.g., layer, weight).

[0566] • Parameters (e.g., layers, weights) that should be used as initial parameters for training (and should be used as initial parameters).

[0567] • Training / updating methods for AI models (e.g., (recommended) number of training epochs, batch size, amount of data used in training).

[0568] The inference information used for the aforementioned AI model may also include information related to decision tree branch pruning, parameter quantization, and the functionality of the AI ​​model. Here, the functionality of the AI ​​model may be equivalent to at least one of the following: temporal beam prediction, spatial beam prediction, autoencoder for CSI feedback, autoencoder for beam management, etc.

[0569] An autoencoder oriented towards CSI feedback can also be used as follows:

[0570] The UE sends the encoded bits output as CSI feedback (CSI report) to the AI ​​model of the encoder, which is input into the CSI / channel matrix / precoder matrix.

[0571] The BS will reconstruct the CSI / channel matrix / precoder output from the received encoded bits as input to the decoder's AI model.

[0572] In spatial domain beam prediction, the UE / BS can take measurements (beam quality, e.g., RSRP) based on sparse (or coarse) beams as input to the AI ​​model and output dense (or fine) beam quality.

[0573] In time-domain beam prediction, the UE / BS can take time-series (past, present, etc.) measurement results (beam quality, e.g., RSRP) as input to the AI ​​model and output the future beam quality.

[0574] The performance information related to the AI ​​model mentioned above may also include information related to the expected value of the loss function defined for the AI ​​model.

[0575] The AI ​​model information in this disclosure may also include information related to the application scope (applicable scope) of the AI ​​model. This application scope may also be represented by physical cell ID, serving cell index, etc. Information related to the application scope may also be included in the environmental information mentioned above.

[0576] AI model information associated with a specific AI model can be predefined in the standard or notified to the UE from the network (NW). The AI ​​model specified in the standard can also be referred to as a reference AI model. AI model information related to the reference AI model can also be referred to as reference AI model information.

[0577] Additionally, the AI ​​model information in this disclosure may also include an index for identifying the AI ​​model (e.g., it may also be referred to as the AI ​​model index, AI model ID, model ID, etc.). The AI ​​model information in this disclosure includes the AI ​​model index in addition to the aforementioned AI model input / output information, or may replace the aforementioned AI model input / output information with the AI ​​model index. The association between the AI ​​model index and the AI ​​model information (e.g., the AI ​​model input / output information) can be specified in advance in the standard or notified to the UE from the NW.

[0578] The AI ​​model information in this disclosure can be associated with the AI ​​model and can also be referred to as AI model relevant information, or simply as relevant information. The information used to identify the AI ​​model in the AI ​​model relevant information may not be explicitly included. AI model relevant information can also be, for example, information containing only metadata.

[0579] In this disclosure, the model ID can also be interchanged with the ID corresponding to the set of AI models (model set ID). Furthermore, in this disclosure, the model ID can also be interchanged with the metadata ID. Metadata (or metadata ID) can also be associated with beam-related information (beam setting) as described above. For example, metadata (or metadata ID) can be used by the UE to select an AI model based on which beam the BS should use, or it can be used by the UE to notify the BS which beam should be used in order to apply a deployed AI model. Additionally, in this disclosure, the metadata ID can also be interchanged with the ID corresponding to the set of metadata (metadata set ID).

[0580] [Supplement 2: Information notification to UE]

[0581] The notification of any information (from NW) to the UE in the above embodiments (in other words, the reception of any information from BS in the UE) can also be performed using physical layer signaling (e.g., DCI), higher layer signaling (e.g., RRC signaling, MAC CE), specific signals / channels (e.g., PDCCH, PDSCH, reference signals) or combinations thereof.

[0582] In the case where the above notification is made via MAC CE, the MAC CE can also be identified by including a new Logical Channel ID (LCID) in the MAC subheader, which is not specified in the existing standard.

[0583] When the above notification is made through a DCI, the notification can also be made through specific fields of the DCI, the Radio Network Temporary Identifier (RNTI) used in the scrambling of the Cyclic Redundancy Check (CRC) bits assigned to the DCI, the format of the DCI, etc.

[0584] Furthermore, any information notification to the UE in the above embodiments can be performed periodically, semi-persistently, or non-periodically.

[0585] [Supplement 3: Notifications from UE]

[0586] The notification of any information from the UE (to the NW) in the above embodiments (in other words, the transmission / reporting of any information in the UE to the BS) can also be performed using physical layer signaling (e.g., UCI), higher layer signaling (e.g., RRC signaling, MACCE), specific signals / channels (e.g., PUCCH, PUSCH, reference signals) or combinations thereof.

[0587] In the case where the above notification is made via MAC CE, the MAC CE can also be identified by including a new LCID, which is not specified in the existing standard, in the MAC subheader.

[0588] In cases where the above notification is sent via UCI, the above notification may also be sent using PUCCH or PUSCH.

[0589] Furthermore, the notification of any information from the UE in the above embodiments can also be carried out periodically, semi-persistently, or non-periodically.

[0590] [Regarding the application of each implementation method]

[0591] At least one of the above-described implementation methods can also be applied to situations where specific conditions are met. These specific conditions can be specified in the standard or notified to the UE / BS using higher-layer signaling / physical layer signaling.

[0592] At least one of the above-described embodiments may also be applied only to, for example, UEs that have reported the specific UE capabilities described below or support those specific UE capabilities (the following is just one example):

[0593] • Supports functionality-based LCM.

[0594] • Supports model-based LCM.

[0595] • Supports individual or simultaneous operation of model-based / function-based LCM.

[0596] This specific UE capability can also represent support for specific processing / operation / control / information for at least one of the above-described implementations / options / selections.

[0597] Furthermore, the aforementioned specific UE capabilities can be applied across the entire frequency range (commonly independent of frequency), or per frequency (e.g., one or a combination of cells, bands, band combinations, BWPs, component carriers, etc.), or per frequency range (e.g., Frequency Range 1 (FR1), FR2, FR3, FR4, FR5, FR2-1, FR2-2), or per subcarrier spacing (SCS), or per feature set (FS) or per feature set per component carrier (FSPC).

[0598] Furthermore, the aforementioned specific UE capabilities can be either the ability to apply across all duplex modes (commonly independent of duplex mode) or the capability for each duplex mode (e.g., Time Division Duplex (TDD) and Frequency Division Duplex (FDD)).

[0599] Furthermore, at least one of the above embodiments can also be applied to situations where the UE is set / activated / triggered by higher-layer signaling / physical layer signaling to specific information associated with the above embodiments (or to perform the operations of the above embodiments). For example, the specific information may be information indicating activation of LCM based on model / functionality ID, arbitrary RRC parameters for a specific version (e.g., Rel.18 / 19), etc.

[0600] Even if at least one of the above-mentioned specific UE capabilities is not supported or the above-mentioned specific information is not set, the UE may, for example, apply the operation of Rel. 15 / 16 / 17.

[0601] (Postscript)

[0602] Regarding one embodiment (first / second embodiment) of this disclosure, the invention is described below.

[0603] [Postscript 1]

[0604] A terminal having:

[0605] The receiving unit receives information relating to the number of models associated with functionality, or the number of functionalities associated with models; and

[0606] The control unit, based on the information, determines the model or functionality of the application.

[0607] [Postscript 2]

[0608] The terminal as described in Appendix 1, wherein,

[0609] The number of models represents the maximum number that can be associated with the functionality, and may be the same or different for each functionality, or common to each applied use case or supporting function.

[0610] [Postscript 3]

[0611] The terminal as described in Appendix 1 or Appendix 2, wherein,

[0612] The control unit determines and operates simultaneously the operations involved in the model and the operations involved in the functionality.

[0613] [Postscript 4]

[0614] The terminal as described in any of Notes 1 to 3, wherein,

[0615] The receiving unit receives information related to the functionality or the priority of the model.

[0616] The control unit controls the operations involved in the application's model or functionality based on the priority.

[0617] (Postscript)

[0618] Regarding one embodiment (third / fourth embodiment) of this disclosure, the following invention is noted.

[0619] [Postscript 1]

[0620] A terminal having:

[0621] The receiving unit receives information for identifying models associated with functionality, or functionality associated with models; and

[0622] The control unit, based on the information, determines the application's model or functionality.

[0623] The control unit determines, based on specific rules, the simultaneous operation of the operations involved in the model and the operations involved in the functionality.

[0624] [Postscript 2]

[0625] The terminal as described in Appendix 1, wherein,

[0626] If simultaneous operation of the operations involved in the model and the operations involved in the functionality is not supported, the control unit determines the selection of either the operations involved in the model or the operations involved in the functionality based on the functionality and the cooperation level of the model.

[0627] [Postscript 3]

[0628] The terminal as described in Appendix 1 or Appendix 2, wherein,

[0629] If simultaneous operation of the operations involved in the model and the operations involved in the functionality is not supported, the control unit determines the selection of either the operations involved in the model or the operations involved in the functionality based on the supported use cases, features, or requested model information.

[0630] [Postscript 4]

[0631] The terminal as described in any of Notes 1 to 3, wherein,

[0632] When supporting both the operations involved in the model and the operations involved in the functionality, the control unit controls the sending of reports for the identification of the model or the functionality.

[0633] (Wireless communication system)

[0634] The structure of a wireless communication system according to one embodiment of this disclosure will now be described. In this wireless communication system, communication is performed using any one or a combination of the wireless communication methods according to the above embodiments of this disclosure.

[0635] Figure 14 This is a diagram illustrating an example of the schematic structure of a wireless communication system according to one embodiment. The wireless communication system 1 (which may also be simply referred to as System 1) may also be a system that implements communication using Long Term Evolution (LTE) or 5th generation mobile communication system New Radio (5GNR) as standardized by the Third Generation Partnership Project (3GPP).

[0636] Furthermore, the wireless communication system 1 can also support dual connectivity between multiple radio access technologies (RATs) (Multi-RAT Dual Connectivity (MR-DC)). MR-DC can also include dual connectivity between LTE (Evolved Universal Terrestrial Radio Access (E-UTRA)) and NR (E-UTRA-NR Dual Connectivity (EN-DC)), dual connectivity between NR and LTE (NR-E-UTRA Dual Connectivity (NE-DC)), etc.

[0637] In EN-DC, the LTE (E-UTRA) base station (eNB) is the Master Node (MN), and the NR base station (gNB) is the Secondary Node (SN). In NE-DC, the NR base station (gNB) is the MN, and the LTE (E-UTRA) base station (eNB) is the SN.

[0638] Wireless communication system 1 can also support dual connectivity between multiple base stations within the same RAT (e.g., MN and SN are dual connectivity between NR base stations (gNB) (NR-NR Dual Connectivity (NN-DC))).

[0639] The wireless communication system 1 may also include a base station 11 forming a macro cell C1 with a relatively wide coverage area, and a base station 12 (12a-12c) configured within the macro cell C1 and forming a small cell C2 narrower than the macro cell C1. The user terminal 20 may also be located within at least one cell. The configuration and number of each cell and the user terminal 20 are not limited to the arrangement shown in the figure. Hereinafter, without distinguishing between base stations 11 and 12, they will be collectively referred to as base station 10.

[0640] User terminal 20 may also connect to at least one of multiple base stations 10. User terminal 20 may also utilize at least one of carrier aggregation (CA) using multiple component carriers (CC) and dual connectivity (DC).

[0641] Each CC can also be included in at least one of the first frequency band (Frequency Range 1 (FR1)) and the second frequency band (Frequency Range 2 (FR2)). Macro cell C1 can also be included in FR1, and small cell C2 can also be included in FR2. For example, FR1 can also be a frequency band below 6 GHz (sub-6 GHz), and FR2 can also be a frequency band above 24 GHz (above-24 GHz). In addition, the frequency bands, definitions, etc. of FR1 and FR2 are not limited to these; for example, FR1 can also be equivalent to a frequency band higher than FR2.

[0642] In addition, user terminal 20 can also use at least one of Time Division Duplex (TDD) and Frequency Division Duplex (FDD) to communicate in each CC.

[0643] Multiple base stations 10 can also be connected via wired (e.g., fiber optic, X2 interface, etc. based on Common Public Radio Interface (CPRI)) or wireless (e.g., NR communication). For example, when NR communication between base stations 11 and 12 is used as a backhaul, base station 11, which is equivalent to a host station, can also be referred to as an Integrated Access Backhaul (IAB) donor, and base station 12, which is equivalent to a relay station, can also be referred to as an IAB node.

[0644] Base station 10 may also be connected to core network 30 via other base stations 10 or directly. Core network 30 may include, for example, at least one of Evolved Packet Core (EPC), 5G Core Network (5GCN), Next Generation Core (NGC), etc.

[0645] The core network 30 may also include, for example, user plane functions (UPF), access and mobility management functions (AMF), session management functions (SMF), unified data management (UDM), application functions (AF), data network (DN), location management functions (LMF), and network functions (NF) such as operation, administration, and maintenance (OAM). Alternatively, multiple functions can be provided through a single network node. Furthermore, communication with external networks (e.g., the Internet) can also be achieved via the DN.

[0646] User terminal 20 can also be a terminal that supports at least one of the following communication methods: LTE, LTE-A, 5G, etc.

[0647] In wireless communication system 1, wireless access methods based on Orthogonal Frequency Division Multiplexing (OFDM) can also be used. For example, in at least one of the downlink (DL) and uplink (UL) links, Cyclic Prefix OFDM (CP-OFDM), Discrete Fourier Transform Spread OFDM (DFT-s-OFDM), Orthogonal Frequency Division Multiple Access (OFDMA), and Single Carrier Frequency Division Multiple Access (SC-FDMA) can also be used.

[0648] The wireless access method can also be referred to as a waveform. In addition, in the wireless communication system 1, other wireless access methods (e.g., other single-carrier transmission methods, other multi-carrier transmission methods) can also be used in the wireless access methods of UL and DL.

[0649] As a downlink channel, the wireless communication system 1 can also use downlink shared channels (Physical Downlink Shared Channel (PDSCH)), broadcast channels (Physical Broadcast Channel (PBCH)), downlink control channels (Physical Downlink Control Channel (PDCCH)) and so on, which are shared among the user terminals 20.

[0650] In addition, as uplink channels, the wireless communication system 1 may also use uplink shared channels (Physical Uplink Shared Channel (PUSCH)), uplink control channels (Physical Uplink Control Channel (PUCCH)), random access channels (Physical Random Access Channel (PRACH)) and so on, which are shared by each user terminal 20.

[0651] User data, high-level control information, and System Information Blocks (SIBs) are transmitted via the PDSCH. User data and high-level control information can also be transmitted via the PUSCH. In addition, Master Information Blocks (MIBs) can also be transmitted via the PBCH.

[0652] Lower-layer control information can also be transmitted via PDCCH. Lower-layer control information may include, for example, downlink control information (DCI), which includes scheduling information for at least one of PDSCH and PUSCH.

[0653] Additionally, the DCI that schedules PDSCH can also be called DL allocation, DL DCI, etc., and the DCI that schedules PUSCH can also be called UL authorization, UL DCI, etc. Furthermore, PDSCH can be rewritten as DL data, and PUSCH can be rewritten as UL data.

[0654] In PDCCH detection, a Control Resource Set (CORESET) and a search space can also be utilized. A CORESET corresponds to the resources used to search for DCIs. The search space corresponds to the search area and search method for PDCCH candidates. A CORESET can also be associated with one or more search spaces. The UE can also monitor CORESETs associated with a specific search space based on search space settings.

[0655] A search space can also correspond to a PDCCH candidate corresponding to one or more aggregation levels. One or more search spaces can also be referred to as a search space set. In addition, the terms "search space", "search space set", "search space setting", "search space set setting", "CORESET", "CORESET setting" etc. disclosed herein can be rewritten interchangeably.

[0656] The PUCCH can also transmit uplink control information (uplink control information (UCI)) including at least one of the following: Channel State Information (CSI), delivery confirmation information (such as Hybrid Automatic Repeat Request ACK / Acknowledgement (HARQ-ACK), ACK / NACK, etc.), and Scheduling Request (SR). The PRACH can also transmit random access preambles used for establishing connections with the cell.

[0657] In addition, in this disclosure, downlink, uplink, etc., may be described without the word "link". Furthermore, various channels may be described without the word "physical".

[0658] In wireless communication system 1, synchronization signals (SS) and downlink reference signals (DL-RS) can also be transmitted. As DL-RS, wireless communication system 1 can also transmit cell-specific reference signals (CRS), channel state information reference signals (CSI-RS), demodulation reference signals (DMRS), positioning reference signals (PRS), phase tracking reference signals (PTRS), etc.

[0659] Synchronization signals can be, for example, at least one of a primary synchronization signal (PSS) and a secondary synchronization signal (SSS). A signal block including SS (PSS, SSS) and PBCH (and DMRS used for PBCH) can also be called an SS / PBCH block, SS block (SSB), etc. Additionally, SS, SSB, etc., can also be called reference signals.

[0660] Furthermore, in wireless communication system 1, the uplink reference signal (UL-RS) can also transmit measurement reference signals (sounding reference signals (SRS)) and demodulation reference signals (DMRS). Additionally, DMRS can also be referred to as user terminal-specific reference signals (UE-specific reference signals).

[0661] (Base station)

[0662] Figure 15 This diagram illustrates an example of the structure of a base station according to one embodiment. The base station 10 includes a control unit 110, a transmit / receive unit 120, a transmit / receive antenna 130, and a transmission path interface (transmission line interface) 140. Alternatively, the control unit 110, the transmit / receive unit 120, the transmit / receive antenna 130, and the transmission path interface 140 may each be provided in more than one manner.

[0663] Furthermore, while this example primarily illustrates the functional blocks of the characteristic portions of this embodiment, it is also conceivable that the base station 10 may also have other functional blocks required for wireless communication. Some of the processing of each unit described below may also be omitted.

[0664] The control unit 110 performs overall control of the base station 10. The control unit 110 can be composed of a controller, control circuit, etc., which are described based on common knowledge in the art to which this disclosure pertains.

[0665] The control unit 110 can also control signal generation and scheduling (e.g., resource allocation, mapping). The control unit 110 can also control transmission, reception, and measurement using the transmit / receive unit 120, transmit / receive antenna 130, and transmission path interface 140. The control unit 110 can also generate data, control information, sequences, etc., to be transmitted as signals and forward them to the transmit / receive unit 120. The control unit 110 can also perform call processing (setting, releasing, etc.) of the communication channel, status management of the base station 10, and management of wireless resources.

[0666] The transmitting / receiving unit 120 may also include a baseband unit 121, a radio frequency (RF) unit 122, and a measurement unit 123. The baseband unit 121 may also include a transmitting processing unit 1211 and a receiving processing unit 1212. The transmitting / receiving unit 120 may be composed of a transmitter / receiver, RF circuitry, baseband circuitry, filters, phase shifters, measurement circuitry, transmitting / receiving circuitry, etc., as described based on common knowledge in the art to which this disclosure pertains.

[0667] The transmitting and receiving unit 120 can be configured as a single integrated transmitting and receiving unit, or it can be composed of a transmitting unit and a receiving unit. The transmitting unit can also be composed of a transmitting processing unit 1211 and an RF unit 122. The receiving unit can also be composed of a receiving processing unit 1212, an RF unit 122, and a measurement unit 123.

[0668] The transmitting and receiving antenna 130 can be constructed from an antenna, such as an array antenna, as described based on common knowledge in the art to which this disclosure pertains.

[0669] The transmitting / receiving unit 120 can also transmit the aforementioned downlink channel, synchronization signal, downlink reference signal, etc. The transmitting / receiving unit 120 can also receive the aforementioned uplink channel, uplink reference signal, etc.

[0670] The transmitting and receiving unit 120 may also use digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), etc., to form at least one of the transmitting beam and the receiving beam.

[0671] The transmitting and receiving unit 120 (transmitting processing unit 1211) can also perform processing at the Packet Data Convergence Protocol (PDCP) layer, Radio Link Control (RLC) layer (e.g., RLC retransmission control), and Medium Access Control (MAC) layer (e.g., HARQ retransmission control) on data and control information obtained from the control unit 110, and generate a bit string to be transmitted.

[0672] The transmitting and receiving unit 120 (transmitting processing unit 1211) can also perform transmission processing such as channel coding (including error correction coding), modulation, mapping, filter processing (filtering processing), Discrete Fourier Transform (DFT) processing (as needed), Inverse Fast Fourier Transform (IFFT) processing, precoding, and digital-to-analog conversion on the bit string to be transmitted, and output the baseband signal.

[0673] The transmitting and receiving unit 120 (RF unit 122) can also perform modulation, filtering, amplification, etc. on the baseband signal to the wireless frequency band, and transmit the wireless frequency band signal through the transmitting and receiving antenna 130.

[0674] On the other hand, the transmitting and receiving unit 120 (RF unit 122) can also amplify, filter, and demodulate the signals of the wireless frequency band received through the transmitting and receiving antenna 130 into the baseband signal.

[0675] The transmitting and receiving unit 120 (receiving and processing unit 1212) can also perform receiving and processing on the acquired baseband signal, including analog-to-digital conversion, Fast Fourier Transform (FFT) processing, Inverse Discrete Fourier Transform (IDFT) processing (as needed), filter processing, demapping, demodulation, decoding (which may also include error correction decoding), MAC layer processing, RLC layer processing, and PDCP layer processing, to acquire user data, etc.

[0676] The transmitting / receiving unit 120 (measurement unit 123) can also perform measurements related to the received signal. For example, the measurement unit 123 can also perform radio resource management (RRM) measurements, channel state information (CSI) measurements, etc., based on the received signal. The measurement unit 123 can also measure received power (e.g., Reference Signal Received Power (RSRP)), received quality (e.g., Reference Signal Received Quality (RSRQ), Signal to Interference plus Noise Ratio (SINR), Signal to Noise Ratio (SNR)), signal strength (e.g., Received Signal Strength Indicator (RSSI)), propagation path information (e.g., CSI), etc. The measurement results can also be output to the control unit 110.

[0677] The transmission path interface 140 can also transmit and receive signals (backhaul signaling) between the device included in the core network 30 (e.g., a network node providing NF), other base stations 10, etc., and can also acquire and transmit user data (user plane data), control plane data, etc. for user terminal 20.

[0678] In addition, the transmitting unit and receiving unit of the base station 10 in this disclosure may also be composed of at least one of a transmitting / receiving unit 120, a transmitting / receiving antenna 130, and a transmission path interface 140.

[0679] The transmitting and receiving unit 120 can transmit information related to the number of models associated with functionality, or the number of functionalities associated with models. The transmitting and receiving unit 120 can also receive signals transmitted based on the models or functionalities of the application objects determined according to said information.

[0680] The transmitting and receiving unit 120 can also transmit information for identifying a model associated with a function, or a function associated with a model. The transmitting and receiving unit 120 can also receive signals transmitted based on the model or function of an application object determined according to the information. The transmitting and receiving unit 120 can also receive signals transmitted based on the simultaneous operation of operations involved in the model and operations involved in the function, determined according to specific rules.

[0681] (User terminal)

[0682] Figure 16 This diagram illustrates an example of the structure of a user terminal according to one embodiment. The user terminal 20 includes a control unit 210, a transmitting / receiving unit 220, and a transmitting / receiving antenna 230. Alternatively, the control unit 210, the transmitting / receiving unit 220, and the transmitting / receiving antenna 230 may each be provided as one or more.

[0683] Furthermore, while this example primarily illustrates the functional blocks of the characteristic portions of this embodiment, it is also conceivable that the user terminal 20 may also have other functional blocks required for wireless communication. Some of the processing of each unit described below may also be omitted.

[0684] The control unit 210 performs overall control of the user terminal 20. The control unit 210 can be composed of a controller, control circuit, etc., which are described based on common knowledge in the technical field to which this disclosure pertains.

[0685] The control unit 210 can also control signal generation, mapping, etc. The control unit 210 can also control transmission, reception, measurement, etc., using the transmission / reception unit 220 and the transmission / reception antenna 230. The control unit 210 can also generate data, control information, sequences, etc., to be transmitted as signals and forward them to the transmission / reception unit 220.

[0686] The transmitting / receiving unit 220 may also include a baseband unit 221, an RF unit 222, and a measurement unit 223. The baseband unit 221 may also include a transmitting processing unit 2211 and a receiving processing unit 2212. The transmitting / receiving unit 220 may be composed of a transmitter / receiver, RF circuit, baseband circuit, filter, phase shifter, measurement circuit, transmitting / receiving circuit, etc., as described based on common knowledge in the art to which this disclosure pertains.

[0687] The transmitting and receiving unit 220 can be configured as a single integrated transmitting and receiving unit, or it can be composed of a transmitting unit and a receiving unit. The transmitting unit can also be composed of a transmitting processing unit 2211 and an RF unit 222. The receiving unit can also be composed of a receiving processing unit 2212, an RF unit 222, and a measurement unit 223.

[0688] The transmitting and receiving antenna 230 can be constructed from an antenna, such as an array antenna, as described based on common knowledge in the art to which this disclosure pertains.

[0689] The transmitting / receiving unit 220 can also receive the downlink channel, synchronization signal, downlink reference signal, etc., mentioned above. The transmitting / receiving unit 220 can also transmit the uplink channel, uplink reference signal, etc., mentioned above.

[0690] The transmitting and receiving unit 220 may also use digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), etc., to form at least one of the transmitting beam and the receiving beam.

[0691] The transmitting and receiving unit 220 (transmitting processing unit 2211) may, for example, perform PDCP layer processing, RLC layer processing (e.g., RLC retransmission control), MAC layer processing (e.g., HARQ retransmission control) on the data and control information obtained from the control unit 210, and generate the bit string to be transmitted.

[0692] The transmitting and receiving unit 220 (transmitting processing unit 2211) can also perform channel coding (which may include error correction coding), modulation, mapping, filter processing, DFT processing (as needed), IFFT processing, precoding, digital-to-analog conversion and other transmission processing on the bit string to be transmitted, and output the baseband signal.

[0693] Furthermore, whether or not to apply DFT processing can be based on the settings of transform precoding. For a certain channel (e.g., PUSCH), if transform precoding is enabled, the transmit / receive unit 220 (transmit processing unit 2211) can perform DFT processing as described above in order to transmit the channel using the DFT-s-OFDM waveform. If not, the transmit / receive unit 220 (transmit processing unit 2211) can perform the above transmission processing without performing DFT processing.

[0694] The transmitting and receiving unit 220 (RF unit 222) can also perform modulation, filtering, amplification, etc. on the baseband signal to the wireless frequency band, and transmit the wireless frequency band signal through the transmitting and receiving antenna 230.

[0695] On the other hand, the transmitting and receiving unit 220 (RF unit 222) can also amplify, filter, demodulate, etc., the signals of the wireless frequency band received by the transmitting and receiving antenna 230.

[0696] The transmitting and receiving unit 220 (receiving and processing unit 2212) can also perform receiving and processing on the acquired baseband signal, such as analog-to-digital conversion, FFT processing, IDFT processing (as needed), filter processing, demapping, demodulation, decoding (which may also include error correction decoding), MAC layer processing, RLC layer processing, and PDCP layer processing, to obtain user data.

[0697] The transmitting / receiving unit 220 (measurement unit 223) can also perform measurements related to the received signal. For example, the measurement unit 223 can also perform RRM measurements, CSI measurements, etc., based on the received signal. The measurement unit 223 can also measure received power (e.g., RSRP), received quality (e.g., RSRQ, SINR, SNR), signal strength (e.g., RSSI), propagation path information (e.g., CSI), etc. The measurement results can also be output to the control unit 210.

[0698] Additionally, the measurement unit 223 can also derive channel measurements for CSI calculation based on channel measurement resources. Channel measurement resources can be, for example, non-zero power (NZP) CSI-RS resources. Furthermore, the measurement unit 223 can also derive interference measurements for CSI calculation based on interference measurement resources. Interference measurement resources can be at least one of NZP CSI-RS resources for interference measurement, CSI-Interference Measurement (IM) resources, etc. Additionally, CSI-IM can be referred to as CSI-Interference Management (IM), and can be interchanged with zero power (ZP) CSI-RS. Furthermore, in this disclosure, CSI-RS, NZP CSI-RS, ZPCSI-RS, CSI-IM, CSI-SSB, etc., can also be interchanged.

[0699] Alternatively, the transmitting and receiving units of the user terminal 20 in this disclosure may also be composed of at least one transmitting / receiving unit 220 and transmitting / receiving antenna 230.

[0700] The sending / receiving unit 220 can receive information related to the number of models associated with a functionality, or the number of functionalities associated with models. The control unit 210 can also determine the applied models or functionalities based on this information. Alternatively, the number of models may represent the maximum number that can be associated with the functionality, and may be the same or different for each functionality, or common to each applied use case or supporting function. The control unit 210 can also determine the simultaneous operation of operations involved in the model and operations involved in the functionality. The sending / receiving unit 220 can also receive information related to the priority of the functionality or the model. The control unit 210 can also control the operations involved in the applied models or functionalities based on the priority.

[0701] The sending and receiving unit 220 can also receive information for identifying a model associated with a function, or a function associated with a model. The control unit 210 can determine the applied model or function based on this information. The control unit 210 can determine the simultaneous operation of the operation involved in the model and the operation involved in the function based on specific rules. Alternatively, if simultaneous operation of the operation involved in the model and the operation involved in the function is not supported, the control unit 210 can determine the selection of either the operation involved in the model or the operation involved in the function based on the cooperation level of the function and the model. Alternatively, if simultaneous operation of the operation involved in the model and the operation involved in the function is not supported, the control unit 210 can determine the selection of either the operation involved in the model or the operation involved in the function based on supported use cases, features, or requested model information. Alternatively, if simultaneous operation of the operation involved in the model and the operation involved in the function is supported, the control unit 210 can control the transmission of reports for identifying the model or the function.

[0702] (Hardware structure)

[0703] Furthermore, the block diagrams used in the description of the above embodiments illustrate functional units. These functional blocks (structural units) are implemented through any combination of at least one of hardware and software. Moreover, the implementation method of each functional block is not particularly limited. That is, each functional block can be implemented using a single device that is physically or logically combined, or it can be implemented by directly or indirectly (e.g., using wired, wireless, etc.) connecting two or more physically or logically separate devices. A functional block can also be implemented by combining the aforementioned single device or multiple devices with software.

[0704] Here, the functions include judgment, decision, determination, calculation, calculation, processing, export, investigation, search, confirmation, receiving, sending, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, regard as, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assigning, but are not limited to these. For example, a functional block (structural unit) that implements the sending function can also be called a transmitting unit, transmitter, etc. Each of these, as described above, is not particularly limited in its implementation method.

[0705] For example, in one embodiment of this disclosure, the base station, user terminal, etc., can also function as a computer for processing the wireless communication method of this disclosure. Figure 17 This diagram illustrates an example of the hardware structure of a base station and a user terminal according to one embodiment. The base station 10 and the user terminal 20 described above can also be physically configured as a computer device including a processor 1001, a memory 1002, a storage device 1003, a communication device 1004, an input device 1005, an output device 1006, and a bus 1007.

[0706] Furthermore, in this disclosure, terms such as apparatus, circuit, device, section, and unit can be interchanged. The hardware structure of base station 10 and user terminal 20 can be configured to include one or more of the apparatuses shown in the figures, or it can be configured not to include any of the apparatuses.

[0707] For example, only one processor 1001 is shown, but there can be multiple processors. Furthermore, processing can be performed by one processor, or simultaneously, sequentially, or by two or more processors using other methods. Additionally, processor 1001 can be implemented using more than one chip.

[0708] Regarding the functions in base station 10 and user terminal 20, for example, by reading specific software (programs) into hardware such as processor 1001 and memory 1002, so that processor 1001 performs calculations and controls communication via communication device 1004, or by controlling at least one of reading and writing data in memory 1002 and storage device 1003.

[0709] The processor 1001 enables the operating system to operate and control the computer as a whole. The processor 1001 may also be a central processing unit (CPU) that includes interfaces with peripheral devices, control devices, arithmetic devices, registers, etc. For example, at least a portion of the control unit 110 (210), the transmit / receive unit 120 (220), etc., described above may also be implemented by the processor 1001.

[0710] Furthermore, the processor 1001 reads programs (program code), software modules, data, etc., from at least one of the storage 1003 and the communication device 1004 into the memory 1002, and performs various processes accordingly. As a program, a program that causes the computer to perform at least a portion of the operations described in the above embodiments can be used. For example, the control unit 110 (210) can also be implemented by a control program stored in the memory 1002 and operated in the processor 1001; similar implementations can be made for other functional blocks.

[0711] The memory 1002 may also be a computer-readable recording medium, such as being composed of at least one of a read-only memory (ROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a random access memory (RAM), or other suitable storage media. The memory 1002 may also be referred to as a register, cache, main memory (main storage device), etc. The memory 1002 is capable of storing executable programs (program code), software modules, etc., for implementing the wireless communication method according to an embodiment of this disclosure.

[0712] Storage device 1003 may also be a computer-readable recording medium, such as a flexible disc, floppy disk, optical disk (e.g., a compact disc ROM), digital multifunction disk, Blu-ray disc, removable disk, hard disk drive, smart card, flash memory device (e.g., a card, stick, key drive), magnetic stripe, database, server, or at least one other suitable storage medium. Storage device 1003 may also be referred to as an auxiliary storage device.

[0713] The communication device 1004 is hardware (transmitting and receiving device) used for communication between computers via at least one of a wired network and a wireless network. It is also referred to as a network device, network controller, network interface card (NIC), communication module, etc. To implement at least one of, for example, Frequency Division Duplex (FDD) and Time Division Duplex (TDD), the communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc. For example, the aforementioned transmit / receive unit 120 (220) and transmit / receive antenna 130 (230) may also be implemented by the communication device 1004. The transmit / receive unit 120 (220) may also be implemented by physically or logically separating the transmit unit 120a (220a) and the receive unit 120b (220b).

[0714] Input device 1005 is an input device that receives input from external sources (e.g., keyboard, mouse, microphone, switch, button, sensor, etc.). Output device 1006 is an output device that performs output to external sources (e.g., display, speaker, light-emitting diode (LED) lamp, etc.). Alternatively, input device 1005 and output device 1006 can also be an integrated structure (e.g., a touch panel).

[0715] Furthermore, the processor 1001, memory 1002, and other devices are connected via a bus 1007 for communicating information. The bus 1007 can be configured as a single bus or as different buses between the devices.

[0716] Furthermore, the base station 10 and the user terminal 20 can also 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 can also use this hardware to implement part or all of the functional blocks. For example, the processor 1001 can also be implemented using at least one of these hardware components.

[0717] (Variation example)

[0718] Furthermore, the terms described in this disclosure, as well as those necessary for understanding this disclosure, may be replaced with terms that have the same or similar meanings. For example, channel, symbol, and signal (signal or signaling) may be interchanged. Additionally, a signal may also be a message. A reference signal can also be abbreviated as RS, and may be referred to as pilot, pilot signal, etc., depending on the applied standard. Furthermore, a component carrier (CC) may also be referred to as cell, frequency carrier, carrier frequency, etc.

[0719] A radio frame can also be composed of one or more periods (frames) in the time domain. Each of these periods (frames) that constitute a radio frame can also be called a subframe. Furthermore, a subframe can also be composed of one or more time slots in the time domain. A subframe can also be a fixed time length (e.g., 1 ms) independent of the parameter set (numerology).

[0720] Here, the parameter set can also be communication parameters applied in at least one of the transmission and reception of a signal or channel. For example, the parameter set can also represent 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, specific filtering processing performed by the transmitter and receiver in the frequency domain, and specific windowing processing performed by the transmitter and receiver in the time domain.

[0721] In the time domain, a time slot can also be composed of one or more symbols (Orthogonal Frequency Division Multiplexing (OFDM) symbols, Single Carrier Frequency Division Multiple Access (SC-FDMA) symbols, etc.). In addition, a time slot can also be a time unit based on a set of parameters.

[0722] A time slot can also contain multiple mini-time slots. Each mini-time slot can also consist of one or more symbols in the time domain. Furthermore, a mini-time slot can also be called a sub-time slot. A mini-time slot can also consist of fewer symbols than a time slot. A PDSCH (or PUSCH) transmitted in a time unit larger than a mini-time slot can also be called PDSCH (PUSCH) mapping type A. A PDSCH (or PUSCH) transmitted using mini-time slots can also be called PDSCH (PUSCH) mapping type B.

[0723] Radio frames, subframes, time slots, mini-time slots, and symbols all represent time units for transmitting signals. Radio frames, subframes, time slots, mini-time slots, and symbols can also use their respective other names. Furthermore, the time units such as frames, subframes, time slots, mini-time slots, and symbols in this disclosure can be interchanged.

[0724] For example, a subframe can also be called a TTI, multiple consecutive subframes can also be called a TTI, and a time slot or a mini-time slot can also be called a TTI. That is, at least one of a subframe and a TTI can be a subframe in existing LTE (1ms), a period shorter than 1ms (e.g., 1-13 symbols), or a period longer than 1ms. In addition, the unit representing TTI may not be called a subframe, but rather a time slot, mini-time slot, etc.

[0725] Here, TTI refers, for example, to the smallest unit of time for scheduling in wireless communication. For instance, in an LTE system, the base station schedules radio resources (frequency bandwidth, transmit power, etc., available to each user terminal) in TTI units. However, the definition of TTI is not limited to this.

[0726] TTI can also be a unit of time for transmitting channel-coded data packets (transmission blocks), code blocks, codewords, etc., and can also be a unit of processing such as scheduling and link adaptation. In addition, when a TTI is given, the actual time interval (e.g., the number of symbols) mapped to transmission blocks, code blocks, codewords, etc. can be shorter than the TTI.

[0727] Additionally, where a time slot or a mini-time slot is referred to as a TTI, more than one TTI (i.e., more than one time slot or more than one mini-time slot) can also serve as the minimum time unit for scheduling. Furthermore, the number of time slots (mini-time slots) constituting the minimum time unit of the schedule can also be controlled.

[0728] A TTI with a duration of 1ms can also be referred to as a normal TTI (TTI in 3GPPRel.8-12), a standard TTI, a long TTI, a normal subframe, a standard subframe, a long subframe, a time slot, etc. A TTI shorter than a normal TTI can also be referred to as a shortened TTI, a short TTI, a partial TTI (partial or fractional TTI), a shortened subframe, a short subframe, a mini time slot, a sub-time slot, a time slot, etc.

[0729] In addition, a long TTI (e.g., a normal TTI, a subframe, etc.) can also be rewritten as a TTI with a duration of more than 1 ms, and a short TTI (e.g., a shortened TTI, etc.) can also be rewritten as a TTI with a duration of less than a long TTI but more than 1 ms.

[0730] A resource block (RB) is a unit of resource allocation in both the time and frequency domains. In the frequency domain, it can also contain one or more consecutive subcarriers. The number of subcarriers in an RB can be the same regardless of the parameter set, for example, it can be 12. The number of subcarriers in an RB can also be determined based on the parameter set.

[0731] Furthermore, an RB can contain one or more symbols in the time domain, and can also be a time slot, a mini-time slot, a subframe, or the length of a TTI. A TTI, a subframe, etc., can also be composed of one or more resource blocks.

[0732] In addition, one or more RBs can also be referred to as Physical Resource Blocks (PRBs), Sub-Carrier Groups (SCGs), Resource Element Groups (REGs), PRB pairs, RB pairs, etc.

[0733] Furthermore, a resource block can also consist of one or more resource elements (REs). For example, an RE can also be a radio resource area consisting of a subcarrier and a symbol.

[0734] The Bandwidth Part (BWP) (also referred to as partial bandwidth, etc.) can also represent a subset of consecutive common resource blocks (RBs) used for a certain parameter set in a certain carrier. Here, common RBs can also be determined by the index of RBs based on the common reference point of the carrier. PRBs can also be defined in a BWP and appended with numbers within that BWP.

[0735] A BWP can also include a UL BWP (the BWP used by UL) and a DL BWP (the BWP used by DL). For a UE, one or more BWPs can also be set within a single carrier.

[0736] At least one of the configured BWPs can be active, and the UE may not intend to transmit or receive specific signals / channels outside of the active BWPs. In addition, "cell", "carrier", etc. in this disclosure may be rewritten as "BWP".

[0737] Furthermore, the structures described above, such as radio frames, subframes, time slots, mini-time slots, and symbols, are merely illustrative. For example, the number of subframes contained in a radio frame, the number of time slots in each subframe or radio frame, the number of mini-time slots contained within a time slot, the number of symbols and RBs contained in a time slot or mini-time slot, the number of subcarriers contained in an RB, and the number of symbols in a TTI, symbol length, and cyclic prefix (CP) length can be varied in many ways.

[0738] Furthermore, the information, parameters, etc., described in this disclosure can be represented by absolute values, relative values ​​with respect to a specific value, or other corresponding information. For example, wireless resources can also be indicated by a specific index.

[0739] In this disclosure, the names used for parameters, etc., are not limiting names in any respect. Furthermore, the mathematical expressions, etc., using these parameters may differ from those explicitly disclosed in this disclosure. Various channels (PUCCH, PDCCH, etc.) and information elements can be identified by any suitable name; therefore, the various names assigned to these various channels and information elements are not limiting names in any respect.

[0740] The information, signals, etc., described in this disclosure can also be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc., which may be mentioned throughout the above description, can also be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, light fields or photons, or any combination thereof.

[0741] Furthermore, information, signals, etc., can be output in at least one of the following directions: from higher layers to lower layers, and from lower layers to higher layers. Information, signals, etc., can also be input and output via multiple network nodes.

[0742] Input and output information, signals, etc., can be stored in a specific location (e.g., memory) or managed using a management table. Input and output information, signals, etc., can be overwritten, updated, or appended. Output information, signals, etc., can also be deleted. Input information, signals, etc., can also be sent to other devices.

[0743] The notification of information is not limited to the methods / implementations described in this disclosure, and may also be carried out by other methods. For example, the notification of information in this disclosure may also be implemented by physical layer signaling (e.g., downlink control information (DCI), uplink control information (UCI), etc.), higher layer signaling (e.g., radio resource control (RRC) signaling, broadcast information (Master Information Block (MIB), System Information Block (SIB) etc.), medium access control (MAC) signaling), other signals, or combinations thereof.

[0744] In addition, physical layer signaling can also be referred to as Layer 1 / Layer 2 (L1 / L2) control information (L1 / L2 control signals), L1 control information (L1 control signals), etc. Furthermore, RRC signaling can also be referred to as RRC messages, such as RRC connection setup messages, RRC connection reconfiguration messages, etc. Additionally, MAC signaling can also be notified using, for example, the MAC control element (CE).

[0745] Furthermore, notification of specific information (e.g., a "is X" notification) is not limited to explicit notification, but can also be implicit (e.g., by not providing that specific information, or by providing other information).

[0746] The determination can be made by a value represented by a single bit (0 or 1), by a true or false value (boolean), or by a numerical comparison (e.g., a comparison with a specific value).

[0747] Whether software is called software, firmware, middleware, microcode, hardware description language, or any other name, it should be broadly interpreted to refer to instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, functions, etc.

[0748] Furthermore, software, instructions, and information can also be sent and received via a transmission medium. For example, when software is sent from a website, server, or other remote source using at least one of wired technologies (coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL) etc.) and wireless technologies (infrared, microwave, etc.), at least one of these wired and wireless technologies is included within the definition of a transmission medium.

[0749] The terms "system" and "network" as used in this disclosure are interchangeable. "Network" may also refer to devices included in a network (e.g., base stations).

[0750] In this disclosure, the terms "precoding", "precoder", "weight (precoding weight)", "quasi-co-location (QCL)", "transmission configuration indication state (TCI state)", "spatial relation", "spatial domain filter", "transmit power", "phase rotation", "antenna port", "layer", "number of layers", "rank", "resource", "resource set", "beam", "beamwidth", "beam angle", "antenna", "antenna element", "panel", "UE panel", "transmitting entity", and "receiving entity" are used interchangeably.

[0751] Furthermore, in this disclosure, the antenna port can also be rewritten with an antenna port used for any signal / channel (e.g., a DeModulation Reference Signal (DMRS) port). In this disclosure, the resources can also be rewritten with resources used for any signal / channel (e.g., reference signal resources, SRS resources, etc.). Additionally, the resources may also include time / frequency / code / space / power resources. Moreover, the spatial domain transmission filter may include at least one of a spatial domain transmission filter and a spatial domain reception filter.

[0752] The aforementioned groups may include, for example, at least one of the following: spatial relation group, code division multiplexing (CDM) group, reference signal (RS) group, control resource set (CORESET) group, PUCCH group, antenna port group (e.g., DMRS port group), layer group, resource group, beam group, antenna group, panel group, etc.

[0753] Furthermore, in this disclosure, beam, SRS Resource Indicator (SRI), CORESET, CORESET pool, PDSCH, PUSCH, Codeword (CW), Transport Block (TB), RS, etc., can also be rewritten to each other.

[0754] Furthermore, in this disclosure, the TCI state, downlink TCI state (DL TCI state), uplink TCI state (UL TCI state), unified TCI state, common TCI state, and joint TCI state can also be rewritten.

[0755] Furthermore, in this disclosure, "QCL", "QCL concept", "QCL relationship", "QCL type information", "QCL property (QCLproperty / properties)", "specific QCL type (e.g., type A, type D) property", "specific QCL type (e.g., type A, type D)" can also be rewritten in different ways.

[0756] In this disclosure, indexes, identifiers (IDs), indicators, indications, resource IDs, etc., can be interchanged. Sequences, lists, sets, groups, clusters, subsets, etc., can also be interchanged.

[0757] Furthermore, the spatial relationship information identifier (ID) (TCI state ID) and the spatial relationship information (TCI state) can be interchanged. "Spatial relationship information (TCI state)" can also be interchanged with "a set of spatial relationship information (TCI states)," "one or more spatial relationship information," etc. TCI state and TCI can also be interchanged. Spatial relationship information and spatial relationship can also be interchanged.

[0758] In this disclosure, the terms "Base Station (BS)", "Wireless Base Station", "Fixed Station", "NodeB", "eNB (eNodeB)", "gNB (gNodeB)", "Access Point", "Transmission Point (TP)", "Reception Point (RP)", "Transmission / Reception Point (TRP)", "Panel", "Cell", "Sector", "Cell Group", "Carrier", and "Component Carrier" are used interchangeably. There are also instances where the terms macrocell, small cell, femtocell, and picocell are used to refer to a base station.

[0759] A base station can accommodate one or more (e.g., three) cells. When a base station accommodates multiple cells, its overall coverage area can be divided into several smaller areas, each of which can also provide communication services through a base station subsystem (e.g., a small indoor base station (Remote Radio Head (RRH))). Terms such as "cell" or "sector" refer to a portion or all of the coverage area of ​​at least one of the base station and base station subsystems providing communication services within that coverage area.

[0760] In this disclosure, the act of a base station sending information to a terminal can also be rewritten in relation to the act of the base station instructing the terminal to perform control / operation based on that information.

[0761] In this disclosure, the terms "Mobile Station (MS)", "user terminal", "user equipment (UE)", and "terminal" are used interchangeably.

[0762] There are also instances where mobile stations are referred to as subscriber stations, mobile units, subscriber units, wireless units, remote units, mobile devices, wireless devices, wireless communication devices, remote devices, mobile subscriber stations, access terminals, mobile terminals, wireless terminals, remote terminals, handsets, user agents, mobile clients, clients, or several other appropriate terms.

[0763] At least one of the base station and the mobile station can also be referred to as a transmitting device, a receiving device, a wireless communication device, etc. Additionally, at least one of the base station and the mobile station can also be a device mounted on a moving object, the moving object itself, etc.

[0764] The term "mobile body" refers to a movable object whose speed is arbitrary, including situations where the body is stationary. Examples of such mobile bodies include vehicles, transport vehicles, automobiles, motorcycles, bicycles, connected cars, excavators, bulldozers, wheel loaders, dump trucks, forklifts, trains, buses, handcarts, rickshaws, ships (ships and other watercraft), airplanes, rockets, artificial satellites, drones, multicopters, quadcopters, hot air balloons, and objects carried on them, but are not limited to these. Furthermore, the mobile body can also be a mobile body that moves autonomously based on operational commands.

[0765] The mobile entity can be a means of transportation (e.g., a vehicle, an airplane, etc.), a mobile entity moving in an unmanned manner (e.g., a drone, an autonomous vehicle, etc.), or a robot (humanized or unmanned). Additionally, at least one of the base station and the mobile station also includes a device that does not necessarily move during communication operations. For example, at least one of the base station and the mobile station can also be an Internet of Things (IoT) device such as a sensor.

[0766] Figure 18 This is a diagram illustrating an example of a vehicle according to one embodiment. The vehicle 40 includes a drive unit 41, a steering unit 42, an accelerator pedal 43, a brake pedal 44, a shift lever 45, left and right front wheels 46, left and right rear wheels 47, an axle 48, an electronic control unit 49, various sensors (including a current sensor 50, a speed sensor 51, a pressure sensor 52, a vehicle speed sensor 53, an acceleration sensor 54, an accelerator pedal sensor 55, a brake pedal sensor 56, a shift lever sensor 57, and an object detection sensor 58), an information service unit 59, and a communication module 60.

[0767] The drive unit 41 is comprised of at least one of an engine, a motor, or a combination of an engine and a motor. The steering unit 42 is configured to include at least a steering wheel (also called a steering handle) that steers at least one of the front wheels 46 and the rear wheels 47 based on operation of the steering wheel by the user.

[0768] The electronic control unit 49 consists of a microprocessor 61, a memory (ROM, RAM) 62, and a communication port (e.g., an input / output (IO) port) 63). Signals from various sensors 50-58 present in the vehicle are input to the electronic control unit 49. The electronic control unit 49 can also be referred to as an electronic control unit (ECU).

[0769] The signals from various sensors 50-58 include the following: current signal from current sensor 50 sensing the current of the motor; rotational speed signal of front wheel 46 / rear wheel 47 obtained by speed sensor 51; air pressure signal of front wheel 46 / rear wheel 47 obtained by air pressure sensor 52; vehicle speed signal obtained by vehicle speed sensor 53; acceleration signal obtained by acceleration sensor 54; accelerator pedal 43 depress amount signal obtained by accelerator pedal sensor 55; brake pedal 44 depress amount signal obtained by brake pedal sensor 56; shift lever 45 operation signal obtained by shift lever sensor 57; and detection signal obtained by object detection sensor 58 for detecting obstacles, vehicles, pedestrians, etc.

[0770] The information service unit 59 consists of various devices such as a navigation system, audio system, speakers, display, television, and radio, used to provide (output) various information such as driving information, traffic information, and entertainment information, and one or more ECUs that control these devices. The information service unit 59 uses information obtained from external devices via the communication module 60, etc., to provide various information / services (e.g., multimedia information / multimedia services) to the occupants of the vehicle 40.

[0771] The information service unit 59 may include input devices (e.g., keyboard, mouse, microphone, switch, button, sensor, touch panel, etc.) that accept input from the outside, and output devices (e.g., display, speaker, LED light, touch panel, etc.) that implement output to the outside.

[0772] The driver assistance system unit 64 comprises various devices used to provide functions for preventing accidents or reducing the driver's workload, such as millimeter-wave radar, light detection and ranging (LiDAR), cameras, positioning detectors (e.g., Global Navigation Satellite System (GNSS), map information (e.g., High Definition (HD) maps, Autonomous Vehicle (AV) maps), gyroscope systems (e.g., Inertial Measurement Unit (IMU), Inertial Navigation System (INS), etc.), artificial intelligence (AI) chips, and AI processors, and one or more ECUs that control these devices. Furthermore, the driver assistance system unit 64 sends and receives various information via communication module 60 to realize driver assistance functions or autonomous driving functions.

[0773] The communication module 60 can communicate with the microprocessor 61 and the structural elements of the vehicle 40 via the communication port 63. For example, the communication module 60 sends and receives data (information) with the drive unit 41, steering unit 42, accelerator pedal 43, brake pedal 44, gear shift lever 45, left and right front wheels 46, left and right rear wheels 47, axles 48, microprocessor 61 and memory (ROM, RAM) 62 in the electronic control unit 49 of the vehicle 40, and various sensors 50-58 via the communication port 63.

[0774] The communication module 60 can be controlled by the microprocessor 61 of the electronic control unit 49 and is a communication device capable of communicating with external devices. For example, it can transmit and receive various types of information with external devices via wireless communication. The communication module 60 can be located both inside and outside the electronic control unit 49. The external device can be, for example, the aforementioned base station 10, user terminal 20, etc. Furthermore, the communication module 60 can be, for example, at least one of the aforementioned base station 10 and user terminal 20 (or it can function as at least one of the base station 10 and user terminal 20).

[0775] The communication module 60 can also wirelessly transmit at least one of the signals input to the electronic control unit 49 from the various sensors 50-58 described above, the information obtained based on these signals, and the information based on input from an external (user) source obtained via the information service unit 59 to an external device. The electronic control unit 49, the various sensors 50-58, the information service unit 59, etc., can also be referred to as input units that receive input. For example, the PUSCH transmitted via the communication module 60 can also contain information based on the aforementioned inputs.

[0776] The communication module 60 receives various information (traffic information, signal information, workshop information, etc.) sent from external devices and displays it on the vehicle's information service unit 59. The information service unit 59 can also be referred to as an output unit that outputs information (for example, outputs information to devices such as displays and speakers based on the PDSCH received by the communication module 60 (or the data / information decoded from the PDSCH).

[0777] Furthermore, the communication module 60 stores various types of information received from external devices into a memory 62 that can be utilized by the microprocessor 61. Based on the information stored in the memory 62, the microprocessor 61 can also control the drive unit 41, steering unit 42, accelerator pedal 43, brake pedal 44, gear shift lever 45, left and right front wheels 46, left and right rear wheels 47, axles 48, and various sensors 50-58, etc., of the vehicle 40.

[0778] Furthermore, the base station in this disclosure can also be rewritten as a user terminal. For example, various methods / implementations of this disclosure can be applied to structures that replace communication between the base station and the user terminal with communication between multiple user terminals (e.g., also referred to as device-to-device (D2D) or vehicle-to-everything (V2X)). In this case, it can also be configured such that the user terminal 20 has the functions of the base station 10 described above. In addition, terms such as "uplink" and "downlink" can also be rewritten as terms corresponding to inter-terminal communication (e.g., "sidelink"). For example, uplink channel, downlink channel, etc., can also be rewritten as sidelink channel.

[0779] Similarly, the user terminal in this disclosure can also be rewritten as a base station. In this case, it can also be configured such that the base station 10 has the functions of the user terminal 20 described above.

[0780] In this disclosure, actions are assumed to be performed by the base station, and sometimes, depending on the circumstances, by its upper node. In a network containing one or more network nodes having a base station, the various operations performed for communication with a terminal can obviously be performed by the base station, one or more network nodes other than the base station (e.g., considering a Mobility Management Entity (MME), a Serving-Gateway (S-GW), etc., but not limited to these), or combinations thereof.

[0781] The various methods / implementations described in this disclosure can be used individually or in combination, and can be switched as needed during execution. Furthermore, the processing procedures, timing sequences, flowcharts, etc., of the various methods / implementations described in this disclosure can be rearranged as long as they do not contradict each other. For example, for the method described in this disclosure, the illustrated order is used to indicate various steps, but the order in which they are indicated is not limited.

[0782] The various methods / implementations described in this disclosure can also be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 6th generation mobile communication system (6G), xth generation mobile communication system (xG, where x is, for example, an integer or a decimal)), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New Radio Access (NX), Futuregeneration Radio Access (FX), Global System for Mobile Communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE This includes 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-Wideband (UWB)), Bluetooth (registered trademark), systems utilizing other suitable wireless communication methods, and next-generation systems derived from, modified, generated, or specified based on these methods. Furthermore, multiple systems can be combined (e.g., LTE or LTE-A, combinations with 5G, etc.) for application.

[0783] As used in this disclosure, the term "based on" does not mean "based on only" unless otherwise specified. In other words, the term "based on" means both "based on only" and "based on at least".

[0784] Any reference to an element using the designations "first," "second," etc., as used in this disclosure does not comprehensively limit the quantity or order of these elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Therefore, reference to the first and second elements does not imply that only two elements may be used, or that the first element must take precedence over the second element in some form.

[0785] The term "determining" as used in this disclosure can encompass a wide variety of operations. For example, "determining" can also refer to judging, calculating, computing, processing, deriving, investigating, searching (e.g., searching in a table, database, or other data structure), and ascertaining.

[0786] In addition, "judgment (decision)" can also refer to receiving (e.g., receiving information), transmitting (e.g., sending information), inputting, outputting, accessing (e.g., accessing data in memory), etc., as situations where "judgment (decision)" is performed.

[0787] Furthermore, "judgment (decision)" can also refer to situations where resolving, selecting, choosing, establishing, or comparing are considered as making a "judgment (decision)". That is, "judgment (decision)" can also refer to certain operations as situations where a "judgment (decision)" is made. In this disclosure, "judgment (decision)" can also be rewritten in relation to the operations described above.

[0788] Furthermore, in this disclosure, "determine / determining" can also be interchanged with "assume / assuming," "expect / expecting," and "consider / considering." Additionally, in this disclosure, "not assuming..." can also be interchanged with "assuming not...".

[0789] In this disclosure, "expect" can be interchanged with "be expected." For example, "expect(s)..." (where "..." can also be expressed as a that clause, an infinitive to, etc.) and "be expected..." can also be interchanged. "does not expect..." and "be not expected..." can also be interchanged. Furthermore, "An apparatus A is not expected..." and "Apparatus B other than apparatus A does not expect..." can also be interchanged (for example, if apparatus A is a UE, apparatus B can also be a base station).

[0790] The term "maximum transmit power" as used in this disclosure may refer to the maximum value of the transmit power, the nominal maximum transmit power (the nominal UE maximum transmit power), or the rated maximum transmit power (the rated UE maximum transmit power).

[0791] As used in this disclosure, the terms "connected," "coupled," or any variations thereof, refer to all direct or indirect connections or combinations between two or more elements, and can include cases where there is one or more intermediate elements between two mutually "connected" or "coupled" elements. The connections or combinations between elements can be physical, logical, or a combination thereof. For example, "connected" can also be rewritten as "access."

[0792] In this disclosure, when two elements are connected, it is possible to consider using more than one wire, cable, printed electrical connection, etc. to be "connected" or "combined" with each other, and as several non-limiting and non-exclusive examples, to use electromagnetic energy with wavelengths having wireless frequency domain, microwave region, light (both visible and invisible) region to be "connected" or "combined" with each other.

[0793] In this disclosure, the term "A is different from B" can also mean "A and B are different from each other". Additionally, the term can also mean "A and B are different from C respectively". Terms such as "separate" and "combined" can also be interpreted in the same way as "different".

[0794] When the terms "include," "including," and variations thereof are used in this disclosure, these terms, like the term "comprising," mean inclusive. Furthermore, the term "or" as used in this disclosure does not mean XOR.

[0795] In this disclosure, for example, in cases where articles are added through translation, such as a, an, and the in English, the disclosure may also include cases where the noun following these articles is in a plural form.

[0796] In this disclosure, terms such as "below," "less than," "above," "more than," and "equal to" can be interchanged. Furthermore, in this disclosure, terms meaning "good," "bad," "large," "small," "high," "low," "early," "late," "wide," and "narrow" are not limited to the positive, comparative, and superlative degrees and can be interchanged. Additionally, in this disclosure, expressions prefixed with "i" (where i is any integer) to terms meaning "good," "bad," "large," "small," "high," "low," "early," "late," "wide," and "narrow" are not limited to the positive, comparative, and superlative degrees and can be interchanged (for example, "highest" and "i-th highest" can also be interchanged).

[0797] In this disclosure, "of", "for", "regarding", "related to", "associated with", etc., can also be rewritten interchangeably.

[0798] In this disclosure, phrases such as "when A, B", "if A, then B", "B upon A", "B in response to A", "B based on A", "B during / while A", "B before A", "B at the same time as / on A", "B after A", "B since A", and "B until A" can be rewritten interchangeably. Furthermore, A and B can be rewritten as nouns, verbs, or other appropriate expressions depending on the context. Additionally, the time difference between A and B can be almost zero (either immediately after or immediately before A). Moreover, a time offset can be applied to the time at which A occurs. For example, "A" can also be interchanged with "before / after the time offset generated by A". This time offset (e.g., more than one symbol / slot) can be predetermined or determined by the UE based on the notification information.

[0799] In this disclosure, timing, moment, time, time instance, arbitrary time unit (e.g., time slot, sub-time slot, symbol, subframe), period, opportunity, resource, etc., can also be overridden.

[0800] The inventions disclosed herein have been described in detail above. However, it will be apparent to those skilled in the art that the inventions disclosed herein are not limited to the embodiments described herein. The description herein is for illustrative purposes only and is not intended to limit the inventions disclosed herein in any way.

Claims

1. A terminal, comprising: The receiving unit receives information for identifying models associated with functionality, or functionality associated with models; and The control unit, based on the information, determines the application's model or functionality. The control unit determines, based on specific rules, the simultaneous operation of the operations involved in the model and the operations involved in the functionality.

2. The terminal as described in claim 1, wherein, If simultaneous operation of the operations involved in the model and the operations involved in the functionality is not supported, the control unit determines the selection of either the operations involved in the model or the operations involved in the functionality based on the functionality and the cooperation level of the model.

3. The terminal as described in claim 1, wherein, If simultaneous operation of the operations involved in the model and the operations involved in the functionality is not supported, the control unit determines the selection of either the operations involved in the model or the operations involved in the functionality based on the supported use cases, features, or requested model information.

4. The terminal as described in claim 1, wherein, When supporting both the operations involved in the model and the operations involved in the functionality, the control unit controls the sending of reports for the identification of the model or the functionality.

5. A wireless communication method for a terminal, comprising: The step of receiving information for identifying a model associated with functionality, or functionality associated with a model; and Based on the information, determine the application's model or functional steps. The terminal determines the simultaneous operation of the operations involved in the model and the operations involved in the functionality based on specific rules.

6. A base station, comprising: The sending unit sends information for identifying the model associated with a function, or the function associated with a model; and The receiving unit receives signals sent based on the model or functionality of the application object determined according to the information. The receiving unit receives signals transmitted based on the simultaneous operation of the model's operations and the functionality's operations, which are determined according to specific rules.

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