Terminal, wireless communication method, and base station

Abstract

A terminal according to one aspect of the present disclosure comprises: a reception unit that receives data collection setting information for artificial intelligence (AI)-based positioning; and a control unit that controls execution of inference, training, and / or practice of a model, and / or issuing a report related to the aforementioned inference, training, and / or practice, said control being carried out on the basis of a parameter indicating an application included in the setting information or a state of a model procedure. One aspect of the present disclosure makes it possible to achieve favorable overhead reduction, channel estimation, and resource utilization.

Description

Terminal, wireless communication method and base station

[0001] The present disclosure relates to a terminal, a wireless communication method, and a base station in a next-generation mobile communication system.

[0002] Long Term Evolution (LTE) has been specified for the Universal Mobile Telecommunications System (UMTS) network with the aim of achieving higher data rates and lower latency (Non-Patent Document 1). Also, LTE-Advanced (3GPP Rel. 10-14) has been specified with the aim of achieving higher capacity and more advanced features than LTE (Third Generation Partnership Project (3GPP (registered trademark)) Release (Rel.) 8, 9).

[0003] Successor systems to LTE (e.g., 5th generation mobile communication system (5G), 5G+ (plus), 6th generation mobile communication system (6G), New Radio (NR), 3GPP Rel. 15 or later, etc.) are also being considered.

[0004] 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

[0005] Regarding future wireless communication technologies, the use of artificial intelligence (AI) technologies such as machine learning (ML) for network / device control and management is being considered.

[0006] Use cases for utilizing AI models include spatial domain downlink (DL) beam prediction, temporal DL beam prediction, positioning, etc. Such beam prediction methods may be referred to as AI-based beam prediction (beam reporting), AI-based positioning, AI-based beam management (BM), etc. Temporal DL beam prediction may be referred to as time-domain Channel State Information (CSI) prediction, for example.

[0007] Insufficient consideration may be given to the use of AI in this way, which may result in insufficient overhead reduction, channel estimation, and resource utilization, resulting in limited improvements in communication throughput and communication quality.

[0008] Therefore, one of the objects of the present disclosure is to provide a terminal, a wireless communication method, and a base station that can achieve suitable overhead reduction / channel estimation / resource utilization.

[0009] A terminal according to one aspect of the present disclosure includes a receiving unit that receives configuration information for data collection in artificial intelligence (AI)-based positioning, and a control unit that controls at least one of execution of inference, training, and exercises of a model, and reporting on at least one of the inference, training, and exercises, based on parameters indicating an application included in the configuration information or the state of a model procedure.

[0010] According to one aspect of the present disclosure, it is possible to achieve favorable overhead reduction / channel estimation / resource utilization.

[0011] FIG. 1 is a diagram illustrating an example of a framework for managing AI models. FIG. 2 is a diagram illustrating an example of specifying an AI model. FIG. 3 is a diagram illustrating an example of a UE positioning method. FIG. 4 is a diagram illustrating an example of a UE positioning method. FIG. 5 is a diagram illustrating an example of a UE positioning method. FIG. 6 is a diagram illustrating an example of a UE positioning method. FIG. 7 is a diagram illustrating an example of forwarding information from a UE to an LMF in GNSS / A-GNSS positioning. FIG. 8 is a diagram illustrating an example of forwarding information from a UE to an LMF in barometric pressure sensor positioning. FIG. 9 is a diagram illustrating an example of forwarding information from a UE to an LMF in TBS positioning. FIG. 10 is a diagram illustrating an example of forwarding information from a UE to an LMF in motion sensor positioning. FIG. 11 is a diagram illustrating an example of forwarding information from a UE to an LMF in WLAN positioning. FIG. 12 is a diagram illustrating an example of forwarding information from a UE to an LMF in Bluetooth (registered trademark) positioning. FIG. 13 is a diagram illustrating an example of a request for location information in DL TDOA positioning. FIG. 14 is a diagram illustrating another example of a request for location information in DL TDOA positioning. FIG. 15 is a diagram illustrating an example of a response for location information in DL TDOA positioning. FIG. 16 is a diagram illustrating an example of accuracy requirements for RSTD measurement. FIG. 17 is a diagram illustrating an example of settings for data collection according to the first embodiment. FIG. 18 is a diagram illustrating an example of a schematic configuration of a wireless communication system according to an embodiment. FIG. 19 is a diagram illustrating an example of a configuration of a base station according to an embodiment. FIG. 20 is a diagram illustrating an example of a configuration of a user terminal according to an embodiment. FIG. 21 is a diagram illustrating an example of hardware configurations of a base station and a user terminal according to an embodiment. FIG. 22 is a diagram illustrating an example of a vehicle according to an embodiment.

[0012] (Application of Artificial Intelligence (AI) Technology to Wireless Communications) With regard to future wireless communications technologies, the use of AI technology such as machine learning (ML) for network / device control and management is being considered.

[0013] For example, it is being considered that terminals (user terminals, user equipment (UE)) / base stations (BSs) will utilize AI technology to improve Channel State Information (CSI) feedback (e.g., reduced overhead, improved accuracy, prediction), improve beam management (e.g., improved accuracy, prediction in the time / space domain), and improve position measurement (e.g., improved position estimation / prediction).

[0014] Based on the input information, the AI ​​model may output at least one information such as an estimate, a prediction, a selected action, a classification, etc. The UE / BS may input channel state information, reference signal measurements, etc. to the AI ​​model and output highly accurate channel state information / measurements / beam selection / location, future channel state information / radio link quality, etc.

[0015] In the present disclosure, AI may be interpreted as an object (also called a subject, object, data, function, program, etc.) that has (performs) at least one of the following characteristics: - Estimation based on observed or collected information; - Selection based on observed or collected information; - Prediction based on observed or collected information.

[0016] In the present disclosure, estimation, prediction, and inference may be used interchangeably. Also, in the present disclosure, estimate, predict, and infer may be used interchangeably.

[0017] In the present disclosure, an object may be, for example, an apparatus, device, etc., such as a UE or a BS. Also, in the present disclosure, an object may correspond to a program / model / entity that operates in the apparatus.

[0018] Also, in the present disclosure, an AI model may be interpreted as an object that has (performs) at least one of the following characteristics: - Generates an estimate by feeding information; - Predicts an estimate by feeding information; - Discovers features by feeding information; - Selects an action by feeding information.

[0019] Additionally, in this disclosure, an AI model may refer to a data-driven algorithm that applies AI techniques to generate a set of outputs based on a set of inputs.

[0020] In addition, in the present disclosure, the terms AI model, model, ML model, predictive analytics, predictive analysis model, tool, autoencoder, encoder, decoder, neural network model, AI algorithm, scheme, etc. may be interchangeable. The AI ​​model may be derived using at least one of regression analysis (e.g., linear regression analysis, multiple regression analysis, logistic regression analysis), support vector machine, random forest, neural network, deep learning, etc.

[0021] In this disclosure, the term "autoencoder" may be interchangeably referred to as any autoencoder, such as a stacked autoencoder, a convolutional autoencoder, etc. The encoder / decoder of this disclosure may employ a model such as a Residual Network (ResNet), a DenseNet, or a RefineNet.

[0022] Furthermore, in the present disclosure, the terms encoder, encoding, encode / encoded, modification / alteration / control by an encoder, compressing, compress / compressed, generating, generate / generated, etc. may be read interchangeably.

[0023] In addition, in the present disclosure, decoder, decoding, decode / decoded, modification / alteration / control by decoder, decompressing, decompress / decompressed, reconstructing, reconstruct / reconstructed, etc. may be read interchangeably.

[0024] In the present disclosure, a layer (of an AI model) may be interchangeably read as a layer (such as an input layer or an intermediate layer) used in the AI ​​model. The layer in the present disclosure may correspond to at least one of an input layer, an intermediate layer, an output layer, a batch normalization layer, a convolutional layer, an activation layer, a dense layer, a normalization layer, a pooling layer, an attention layer, a dropout layer, a fully connected layer, etc.

[0025] In this disclosure, methods for training an AI model may include supervised learning, unsupervised learning, reinforcement learning, federated learning, etc. Supervised learning may refer to the process of training a model from inputs and corresponding labels. Unsupervised learning may refer to the process of training a model without labeled data. Reinforcement learning may refer to the process of training a model from inputs (i.e., states) and feedback signals (i.e., rewards) resulting from the model's outputs (i.e., actions) in an environment with which the model interacts.

[0026] In the present disclosure, terms such as generate, calculate, derive, etc. may be interchangeable. In the present disclosure, terms such as implement, operate, operate, execute, etc. may be interchangeable. In the present disclosure, terms such as train, learn, update, retrain, etc. may be interchangeable. In the present disclosure, terms such as infer, after-training, live use, actual use, etc. may be interchangeable. In the present disclosure, signal may be interchangeable with signal / channel.

[0027] FIG. 1 is a diagram illustrating an example of a framework for managing AI models. In this example, each stage related to an AI model is shown as a block. This example is also referred to as AI model life cycle management (LCM).

[0028] The data collection stage corresponds to a stage of collecting data for generating / updating an AI model. The data collection stage may include data organization (e.g., determining which data to transfer for model training / model inference), data transfer (e.g., transferring data to an entity (e.g., UE, gNB) that performs model training / model inference), etc.

[0029] Note that data collection may refer to a process in which data is collected by a network node, a management entity, or a UE for the purpose of AI model training / data analysis / inference. In this disclosure, the terms "process" and "procedure" may be interchangeable. Also, in this disclosure, collection may refer to obtaining a data set (e.g., usable as input / output) for AI model training / inference based on measurements (e.g., channel measurements, beam measurements, radio link quality measurements, position estimation, etc.).

[0030] In the present disclosure, offline field data may be data collected from the field (real world) and used for offline training of an AI model. Also, in the present disclosure, online field data may be data collected from the field (real world) and used for online training of an AI model.

[0031] In the model training stage, model training is performed based on the data (training data) transferred from the collection stage. This stage may include data preparation (e.g., performing data preprocessing, cleaning, formatting, transformation, etc.), model training / validation, model testing (e.g., verifying whether the trained model meets a performance threshold), model exchange (e.g., transferring the model for distributed learning), and model deployment / update (deploying / updating the model to the entity that will perform model inference).

[0032] It should be noted that AI model training may refer to a process for training an AI model in a data-driven manner and obtaining a trained AI model for inference.

[0033] AI model validation may also refer to a sub-process of training that evaluates the quality of an AI model using a dataset different from the dataset used to train the model, which helps select model parameters that generalize beyond the dataset used to train the model.

[0034] AI model testing may also refer to a sub-process of training for evaluating the performance of the final AI model using a dataset different from that used for model training / validation. Note that, unlike validation, testing does not necessarily require subsequent model tuning.

[0035] In the model inference stage, model inference is performed based on the data (inference data) transferred from the collection stage. This stage may include data preparation (e.g., performing data preprocessing, cleaning, formatting, transformation, etc.), model inference, model monitoring (e.g., monitoring the performance of model inference), model performance feedback (feeding back model performance to the entity training the model), and output (providing model output to the actor).

[0036] Additionally, AI model inference may refer to the process of using a trained AI model to produce a set of outputs from a set of inputs.

[0037] Also, a UE side model may refer to an AI model whose inference is performed entirely in the UE, and a network side model may refer to an AI model whose inference is performed entirely in the network (e.g., gNB).

[0038] Also, a one-sided model may refer to a UE-side model or a network-side model. A two-sided model may refer to a pair of AI models in which joint inference is performed. Here, joint inference may include AI inference in which the inference is performed jointly across the UE and the network, e.g., a first part of the inference may be performed first by the UE and the remaining part by the gNB (or vice versa).

[0039] In addition, AI model monitoring may refer to a process for monitoring the inference performance of an AI model, and may be interchangeably read as model performance monitoring, performance monitoring, etc.

[0040] Note that model registration may refer to assigning a version identifier to a model and making the model executable (registering) the model by compiling it into the specific hardware used in the inference stage. Also, model deployment may refer to distributing (or activating in) a runtime image (or an image of an execution environment) of a fully developed and tested model to (or enabling in) a target (e.g., UE / gNB) where inference will be performed.

[0041] An actor stage may include action triggers (e.g., deciding whether to trigger an action on another entity), feedback (e.g., feeding back information needed for training data / inference data / performance feedback), etc.

[0042] For example, training of a model for mobility optimization may be performed in, for example, Operation, Administration and Maintenance (Management) (OAM) / gNodeB (gNB) in a network (NW). In the former case, interoperability, large-capacity storage, operator manageability, and model flexibility (feature engineering, etc.) are advantageous. In the latter case, the latency of model updates and the need for data exchange for model deployment are advantageous. Inference of the above model may be performed in, for example, a gNB.

[0043] The entity that performs training / inference may vary depending on the use case (i.e., the function of the AI ​​model), which may include beam management, beam prediction, autoencoder (or information compression), CSI feedback, positioning, etc.

[0044] For example, for AI-assisted beam management based on measurement reports, the OAM / gNB may perform model training and the gNB may perform model inference.

[0045] For AI-assisted UE-assisted positioning, a Location Management Function (LMF) may perform model training and the LMF may perform model inference.

[0046] For CSI feedback / channel estimation using an autoencoder, the OAM / gNB / UE may perform model training and the gNB / UE may perform model inference (jointly).

[0047] For AI-assisted beam management or AI-assisted UE-based positioning based on beam measurements, the OAM / gNB / UE may perform model training and the UE may perform model inference.

[0048] Note that model activation may mean activating an AI model for a specific function, model deactivation may mean disabling an AI model for a specific function, and model switching may mean deactivating a currently active AI model for a specific function and activating a different AI model.

[0049] Model transfer may also refer to distributing an AI model over the air interface. This distribution may include distributing parameters of a model structure already known at the receiving end, or a new model with parameters, or both. This distribution may include a complete model or a partial model. Model download may refer to transferring a model from the network to the UE. Model upload may refer to transferring a model from the UE to the network.

[0050] 2 is a diagram showing an example of specifying an AI model. In this example, a UE and a NW (e.g., a base station (BS)) can recognize models #1 and #2 (although they do not need to fully understand the details of the models). The UE may report, for example, the capabilities of model #1 and model #2 to the NW, and the NW may instruct the UE on the AI ​​model to use.

[0051] (UE positioning using AI technology) Fingerprinting localization, which estimates the location of wireless devices using the propagation characteristics of wireless signals, is widely used in both Line Of Site (LOS) and Non-Line Of Site (NLOS) scenarios.

[0052] In this disclosure, LOS may mean that the UE and base station are in an environment where they can see each other (or there are no obstructions), and NLOS may mean that the UE and base station are not in an environment where they can see each other (or there are obstructions).

[0053] Fingerprinting location estimates the UE's location based on a database / AI model from the fingerprints of the UE's multiple transmission paths (multipath).

[0054] The multipath information may be, for example, information regarding the Angle of Arrival (AoA) / Angle of Departure (AoD) of the signal for optimal / candidate transmission paths.

[0055] In the present disclosure, the information on AoA may include, for example, information on at least one of azimuth angles of arrival and zenith angles of arrival, and the information on AoD may include, for example, information on at least one of azimuth angles of departure and zenith angles of departure.

[0056] 3GPP Rel. 16 NR supports the following positioning technologies: DL / UL Time Difference Of Arrival (TDOA) based positioning, Angle (DL AoD / UL AoA) based positioning, Multi-Round Trip Time (RTT) based positioning, and Enhanced Cell ID (E-CID) based positioning.

[0057] FIG. 3 is a diagram showing an example of positioning based on DL / UL TDOA. For example, assume that multiple base stations (TRP#0-#2) are arranged around a UE. In this positioning method, the location of the UE is estimated (measured) using a measurement value of the Reference Signal Time Difference (RSTD). For example, the RSTD (T ｉ -T ｊ ) is a certain value (k ｉ，ｊ ) to draw a hyperbola H ｉ，ｊ The intersection of multiple such hyperbolas (H ０，１、 H １，２、 H ２，０ The location of the UE may be estimated by using the RSRP of the reference signal.

[0058] 4 shows an example of DL AoD / UL AoA-based positioning. In this positioning method, the UE's location is estimated using DL AoD measurements (e.g., θ or φ) or UL AoA measurements (e.g., θ or φ). The UE's location may also be estimated using RSRP.

[0059] 5 is a diagram showing an example of multi-RTT-based positioning. In this positioning method, the location of a UE is estimated using multiple RTTs calculated from the Tx / Rx time difference of reference signals (and additionally RSRP, RSRQ, etc.). For example, geometric circles based on the RTTs can be drawn with each base station at its center. The intersection of these multiple circles may be estimated as the location of the UE.

[0060] Figure 6 shows an example of E-CID based positioning, in which the UE location is estimated based on the geometrical location of the serving cell / neighbor cells and additional measurements (Tx-Rx time difference, RSRP, RSRQ, etc.).

[0061] The positioning in the above-mentioned DL (DL TDOA, DL AoD) may be performed on the UE side or the LMF side. For example, in UE-based positioning, the UE may calculate the UE position based on various measurement results of the UE and assistance information from the LMF. Also, in UE-assisted positioning, the UE may report various measurement results to the LMF, and the LMF may calculate the UE position. The assistance information may be information for assisting in estimating the UE's position.

[0062] The above-mentioned UL (UL TDOA, UL AoA) positioning may be performed on the LMF side. In this case, the base station may report various measurement results to the LMF, and the LMF may calculate the UE's position.

[0063] The above-mentioned positioning in DL and UL (multi-RTT, E-CID) may be performed on the LMF side. In this case, the UE / base station may report various measurement results to the LMF, and the LMF may calculate the UE's position.

[0064] Furthermore, in 3GPP Rel. 17, a positioning method using assistance information is proposed for the purpose of further improving positioning accuracy. The assistance information may be transmitted between the UE, the base station, and the LMF as measurement information for the above-mentioned DL / UL-TDOA, DL-AoD / UL-AoA, multi-RTT, and E-CID.

[0065] The assistance information may include information regarding at least one of the following: Timing Error Group (TEG); RSRPP (path-specific RSRP); Expected angle; Adjacent beam information; TRP antenna / beam information; LOS / NLOS indicator; Additional path reports.

[0066] The TEG may indicate one or more Positioning Reference Signal (PRS) resources whose Rx / Tx timing errors are within a certain margin.

[0067] RSRPP may indicate the measurement result of RSRP on the first pass.

[0068] In UL positioning, the assistance information regarding the expected angle may indicate an expected UL-AoA / ZoA. The assistance information may be transmitted to the base station from an LMF. The assistance information may support at least one of UL TDOA, UL AoA, and multi-RTT positioning.

[0069] In DL positioning, assistance information regarding expected angles may include information regarding expected DL-AoA / ZoA or DL-AoD / ZoD. The assistance information may be transmitted from an LMF to a UE. The assistance information may also support at least one of DL TDOA, DL AoA, and multi-RTT positioning. This improves the accuracy of angle-based UE positioning and enables optimization of Rx beamforming of the UE or base station.

[0070] The assistance information regarding the predicted angles may include, in addition to the information on the values ​​of the AoA / ZoA / AoD / ZoD themselves as described above, information indicating the uncertainty range of these values.

[0071] As additional beam information, the neighboring beam information may include information about a subset of DL-PRS resources for the purpose of prioritizing DL-AoD reports (Option 1) or the boresight direction of each DL-PRS resource (Option 2), allowing for optimization of UE Rx beam sweeping and DL-AoD measurements.

[0072] As additional beam information, the assistance information may also include PRS beam pattern information, which may include information regarding the relative power between DL-PRS resources for each angle for each TRP.

[0073] The LOS / NLOS indicator may indicate information regarding Line Of Site (LOS) / Non-Line Of Site (NLOS).

[0074] In addition, in order to improve the positioning delay of the UE, pre-configured measurement gaps (MG), activation of the MG via lower layers, MG-less location, PRS Rx / Tx in RRC_INACTIVE state, or on-demand PRS may be configured for the UE (or may be used by the UE).

[0075] In 3GPP Rel. 17 NR, it is agreed that the UE measures / reports the RSRP of neighboring beams to improve the accuracy of UE location estimation. For example, in a UE-assisted DL-AoD positioning method, the LMF can indicate at least one of the following options 1 and 2 in the assistance information:

[0076] Option 1: A subset of PRS resources for DL-AoD reporting prioritization. The subset may be configured for each PRS resource depending on UE capabilities. The UE may include PRS measurements requested for a subset of PRSs in the DL-AoD additional measurements if required PRS measurements for the associated PRS are reported. The required PRS measurements may be DL PRS RSRP / path PRS RSRP. The UE may report PRS measurements only for a subset of PRS resources. Note that the subset associated with a PRS resource may be in the same / different PRS resource set as the PRS resource. Option 2: Information about the boresight direction configured for each PRS resource depending on UE capabilities.

[0077] In 3GPP Rel. 16 NR, it is agreed that the expected RSTD and its uncertainty range will be indicated from the LMF to the UE. Furthermore, in Rel. 17, it is agreed that the expected angle and its uncertainty range will be indicated from the LMF to the UE in order to reduce errors and complexity in AoA / AoD measurements.

[0078] 3GPP Rel. 17 NR is considering the introduction of a Positioning Reference Unit (PRU) for positioning. The PRU is being discussed as a reference (reference) device with a known location to mitigate transmission and reception timing errors of UEs / gNBs. The PRU may also be read as UE / gNB / TRP (transmission reception point) / TP (transmission point).

[0079] For example, the PRU may support at least one of the following: - Measuring DL PRS and reporting related measurements (e.g., RSTD / transmission time difference / RSRP) to the LMF; - Transmitting SRS and enabling the TRP to measure and report measurements related to the reference device (e.g., Relative Time of Arrival (RTOA) / transmission time difference, AOA) to the LMF; - Operation, measurements, various parameters (parameters related to transmission and reception timing delay, AoD and AOA enhancement, and measurement calibration); - Reporting location coordinate information of the reference device to the LMF if the LMF does not have the location coordinate information; - A reference device with a known location is a UE / gNB; - The accuracy with which the location of the reference device can be known.

[0080] Positioning using AI models has two use cases, for example: Direct AI / ML positioning, and AI / ML assisted positioning.

[0081] Direct AI / ML positioning outputs, for example, UE positioning, while AI / ML-assisted positioning outputs, for example, intermediate features, which may be input back into the AI / ML model.

[0082] Examples of outputs of the AI / ML assisted positioning described above may include at least one of the following: - LOS / NLOS identification (probability of LOS / NLOS), - ToA (time of arrival of PRS / SRS), - Rx-Tx (transmit / receive) time difference, - AoA / AoD, - Number of waves, Rx-Tx (transmit / receive) phase difference (Rel. 18 phase measurement), - DL RSTD / UL TDOA, - DL-PRS / UL-SRS, RSRPs / RSRPPs, - Likelihood of the above values ​​(e.g., ToA probability).

[0083] (UE-based DL positioning) This section describes a case where the UE performs positioning using the UE-side AI model. The UE may use the AI ​​model to infer UE positioning (UE positioning, location). The UE may report the inferred positioning result to the LMF.

[0084] The inference of the AI ​​model in the UE may be performed based on a data set that the UE has. The input information for the AI ​​model (input information) may include raw data (information about characteristics) measured by the UE / assistance information. The UE may receive the assistance information from a base station or an LMF.

[0085] The raw measurement results (which may also be referred to as measured features) measured by the UE may include data (information) related to at least one of the following (note that information listed with a comma does not necessarily have to be included in its entirety; the same applies hereinafter in this disclosure): UE DL-PRS-RSRP (or DL-PRS-RSSI / RSRQ) measurement results of multiple reference TRPs, DL-RSTD measurement results of multiple reference TRPs, Time of Arrival (ToA) measurement results of the reference TRPs, AoD measurement results of the reference TRPs, quality of various measurements, timestamps (times) of the measurement results, Physical Cell ID (PCI), Global Cell ID (GCI), Absolute Radio Frequency Channel Number (ARFCN), PRS resource ID, PRS resource set ID, and PRS ID for each measurement, UE Rx TEG for DL ​​RSTD measurement. ID, TRP Tx TEG ID, UE Tx TEG ID, UE RxTx TEG ID, TRP Rx TEG ID, TRP RxTx TEG ID, DL-PRS receive beam index, First path DL-PRS-RSRP measurement results, Channel impulse response / Channel frequency response, Coded channel impulse response / Coded channel frequency response, Phase difference at different antennas in the antenna array, Received multipath characteristics of the reference TRP (e.g., each path DL-PRS-RSRP measurement results), amplitude / mean / Cumulative Distribution Function (CDF) of estimated multipath amplitude, path loss, root-mean-square (rms) delay spread, kurtosis of channel impulse response.

[0086] Note that GCI may be interchangeably read as NR Cell Global Identity (NCGI).

[0087] The assistance information notified by the base station / LMF may include information on at least one of the following: - Noise of the ground truth dataset (such as the expected variance of noise); - Physical cell ID (PCI), global cell ID (GCI), ARFCN, and PRS ID of the NR TRP candidate to be measured; - Timing relative to the serving (reference) TRP of the NR TRP candidate; - SSB information of the TRP (time / frequency occupancy of the SSB); - DL-PRS configuration of the candidate NR TRP; - Spatial direction information of the DL-PRS resource of the TRP provided by the base station (azimuth angle, elevation angle, etc.); - Geographical coordinates of the TRP provided by the base station (including the transmission reference position for each DL-PRS resource ID, the reference position of the transmitting antenna of the reference TRP, and the reference positions of the transmitting antennas of other TRPs); - Candidate NR Fine timing relative to the serving (reference) TRP; PRS-specific transmission point indication (TP indication); Association information between TRP Tx TEG ID and DL-PRS resources; Information on LOS / NLOS for each DL-PRS-RSRP / DL-PRS-RSSI / DL-RSTD / ToA / AoD / phase difference measurement; On-demand DL-PRS configuration; TRP beam / antenna information (including azimuth angle, zenith angle, and relative power between PRS resources for each angle in each TRP); Expected angle assistance information; PRS priority list.

[0088] The above-mentioned ground truth dataset may be interpreted as a dataset representing location information used for AI training / validation / testing.

[0089] The UE may output the UE location from the AI ​​model. The above AI model on the UE side having the UE location as an output can also be applied to UL-based positioning, DL UL-based positioning, etc. These inputs may include, for example, UE / gNB measurement results, extracted features, etc., or may include features extracted on the gNB side, or may include features extracted on the LMF side, etc.

[0090] (Positioning Classification) Positioning using AI models may be classified as follows: (1) UE-based positioning; (2) AI / ML-assisted positioning; and (3) NG-RAN (Next Generation-Radio Access Network) node-assisted positioning.

[0091] (1) UE-based positioning can be further classified as follows: (1-1) Direct AI / ML positioning in the UE-side model, (1-2) AI / ML-assisted positioning in the UE-side model, and non-AI-based positioning in the UE-side algorithm.

[0092] (2) AI / ML assisted positioning can be further classified as follows: (2-1) AI / ML assisted positioning in the UE side model and non-AI based positioning in the LMF side algorithm, (2-2) direct AI / ML positioning in the LMF side model.

[0093] (3) NG-RAN node assisted positioning can be further classified as follows: (3-1) AI / ML assisted positioning in the gNB side model and non-AI based positioning in the LMF side algorithm, (3-2) direct AI / ML positioning in the LMF side model.

[0094] For each of the above-mentioned positioning methods, the following are being considered: - Type of measurement as model inference input (new measurement / existing measurement); - For positioning methods (1) and (2) above, it is assumed that the UE will measure as model inference input; - For positioning method (3) above, it is assumed that the TRP (gNB / LMF) will measure as model inference input; - Reporting measurement results as model inference input to the LMF for the LMF-side models (2-2, 3-2); - For AI / ML-assisted positioning, new measurement reports / existing measurement reports may be extended as model outputs to the LMF for UE-assisted (2-1) and NG-RAN node-assisted positioning (3-1); - Extension of assistance signaling / procedures to facilitate model inference for both the UE-side model and the NW-side model.

[0095] The positioning methods (1) to (3) above may be collectively referred to as AI / ML-based positioning. That is, positioning using an AI model, the above-mentioned positioning methods, and AI / ML-based positioning (AI-based positioning) may be interchangeable.

[0096] In addition, the following classifications are being considered for AI / ML-based positioning: - Case 1: UE-based positioning using a UE-side model (direct AI / ML positioning); - Case 2a: UE-assisted / LMF-based positioning using a UE-side model (AI / ML-assisted positioning); - Case 2b: UE-assisted / LMF-based positioning using an LMF-side model (direct AI / ML positioning); - Case 3a: NG-RAN node-assisted positioning using a gNB-side model (AI / ML-assisted positioning); - Case 3b: NG-RAN node-assisted positioning using an LMF-side model (direct AI / ML positioning).

[0097] (Performance metrics for positioning) Rel. 17 positioning specifies that the following metrics be applied for performance evaluation (TR 38.857). Note that the percentiles of positioning error may be 50%, 67%, 80%, and 90%.

[0098] Horizontal accuracy: Horizontal accuracy may indicate the difference between the calculated horizontal position of the UE and the actual horizontal position of the UE. For example, the horizontal accuracy may be less than 0.2 meters for 90% of the UEs.

[0099] Vertical accuracy: Vertical accuracy may indicate the difference between the calculated vertical position of the UE and the actual vertical position of the UE. For example, the vertical accuracy may be less than 1 meter for 90% of the UEs.

[0100] Latency The latency may be, for example, the end-to-end latency for UE location estimation. The latency may be less than 100 milliseconds (more preferably, on the order of 10 milliseconds). The latency may include processing delays of the various nodes involved (UE, gNB, AMF, LMF, etc.) and signaling delays between the nodes. Another latency may include the physical layer latency for UE location estimation. The latency may be, for example, less than 10 milliseconds.

[0101] Performance metrics for model monitoring can be at least one of the following: Performance, Latency, Complexity.

[0102] Performance may include at least one of the following: Horizontal accuracy (meters) of AI / ML based positioning; Vertical accuracy (meters) of AI / ML based positioning; Accuracy of intermediate feature (meters) of AI / ML based positioning.

[0103] The horizontal accuracy may indicate the difference between the calculated horizontal position of the UE and the actual horizontal position of the UE. For example, the horizontal accuracy may be less than 0.2 m for 90% of the UEs.

[0104] The vertical accuracy may indicate the difference between the calculated vertical position of the UE and the actual vertical position of the UE. For example, the vertical accuracy may be less than 1 m for 90% of the UEs.

[0105] The intermediate feature accuracy may indicate the difference between the inferred intermediate value and the intermediate value derived based on the actual UE location, and may be indicated by at least one of the following: accuracy of the LOS / NLOS indicator (error rate %), ToA (milliseconds), AoA (degrees), RSTD (milliseconds), RSRP (dBm), etc.

[0106] Latency may include at least one of the following: Physical layer latency (ms); End-to-end latency (ms).

[0107] The latency in the positioning procedure may be defined according to a predetermined rule, and the latency (start time / end time) of the physical layer may be defined individually depending on the positioning method.

[0108] For example, in the case of UE-based positioning, the start time may be the timing when the UE transmits a PUSCH including an MG request (Alt1), the timing when the gNB transmits an LPP message including assistance data using a PDSCH (Alt2), or the timing when the UE starts receiving DL PRS (Alt3). In addition, the end time in this case may be the timing when the gNB successfully decodes a PUSCH including an LPP Provide Location Information message, or if not successful, the timing when the UE performs a location estimation calculation.

[0109] In the case of UE-assisted positioning / LMF-based positioning, the start time may be the timing when the gNB transmits a PDSCH including an LPP Request Location Information message. In addition, the end time in this case may be the timing when the gNB successfully decodes a PUSCH including an LPP Provide Location Information message.

[0110] In the case of NG-RAN node-assisted positioning, the start time may be the timing when the gNB receives the NRPPa measurement request message, and the end time in this case may be the timing when the gNB transmits the NRPPa measurement response message.

[0111] In addition, in terms of the latency of an AI model, the start time may be the time when the UE / NW receives the input of the model inference, and the end time may be the time when the NW / UE receives the output of the AI ​​model.

[0112] The end-to-end latency may be the latency for the UE's location estimation.

[0113] The latency may also include higher layer latency as another latency. The latency may include processing delays of various related nodes (UE, gNB, AMF, LMF, etc.) and signaling delays between nodes. The definitions of each latency described above may follow the definitions in Rel. 17.

[0114] Complexity may be defined by the computational complexity (floating point operations (FLOPs)) of model inference. The complexity of an AI model may also be defined by, for example, the data size (Mbytes) of the model or the number of parameters associated with the AI ​​model.

[0115] (Performance Monitoring (Model Monitoring) for Positioning) Performance metrics calculation In UE-based positioning, model monitoring for direct AI / ML positioning using a UE-side model may be performed by at least one of the following: <1> Performance metrics calculation in the UE, <2> Performance metrics calculation in the LMF.

[0116] In UE-based positioning, model monitoring for AI / ML-assisted positioning using a UE-side model may be performed by at least one of the following: <3> Performance metric calculation in the UE; <4> Performance metric calculation in the LMF.

[0117] In UE-assisted positioning, model monitoring for AI / ML-assisted positioning by a UE-side model may be performed by: <5> Performance metrics calculation in the LMF.

[0118] In NG-RAN node assisted positioning, model monitoring for AI / ML assisted positioning using a gNB-side model may be performed by at least one of the following: <6> Performance metric calculation at the gNB, <7> Performance metric calculation at the LMF.

[0119] <<Performance Indicator Calculation in Direct AI / ML Positioning Using UE-Side Model>> The performance metrics calculation (model monitoring) in <1> above may be performed according to the following steps: Step 1: The UE obtains a noisy ground truth UE position (a true value related to the UE location). Step 1': The UE obtains an estimated UE position from model inference. Step 2: The UE calculates performance metrics for model monitoring. Step 3: The UE reports the performance metrics for model monitoring. Step 3': The UE requests model activation / deactivation / switching from the LMF. Step 4: The UE receives an instruction for model activation / deactivation / switching from the LMF. Step 5: The UE performs model activation / deactivation / switching.

[0120] The performance metrics calculation (model monitoring) of <2> above may be performed according to the following steps: Step 1: The UE obtains and reports the UE position from model inference. Step 1': The LMF obtains a noisy ground truth UE position (a true value related to the UE location). Step 2: The LMF calculates the performance metrics for model monitoring. Step 3: The UE receives an instruction for model activation / deactivation / switching from the LMF. Step 4: The UE performs model activation / deactivation / switching.

[0121] <<Performance Indicator Calculation in AI / ML Assisted Positioning Using UE-Side Models>> The performance metrics calculation (model monitoring) in <3> above may be performed according to the following steps: Step 1: The UE obtains noisy ground truth data (the true value for certain data (e.g., the UE position)). Step 1': The UE obtains estimated data from model inference. Step 2: The UE calculates performance metrics for model monitoring. Step 3: The UE reports the performance metrics for model monitoring. Step 3': The UE requests model activation / deactivation / switching from the LMF. Step 4: The UE receives a model activation / deactivation / switching instruction from the LMF. Step 5: The UE performs model activation / deactivation / switching.

[0122] The performance metrics calculation (model monitoring) of <4> and <5> above may be performed according to the following steps: Step 1: The UE obtains and reports estimated data from model inference. Step 2: The LMF obtains ground truth data (the true value for certain data (e.g., the UE position)). Step 3: The LMF calculates performance metrics for model monitoring. Step 4: The UE receives an instruction for model activation / deactivation / switching from the LMF. Step 5: The UE performs model activation / deactivation / switching.

[0123] <<Performance Indicator Calculation in AI / ML Assisted Positioning Using a gNB-Side Model>> The performance metrics calculation (model monitoring) in <6> above may be performed according to the following steps. Step 1: The gNB obtains and reports estimated data from model inference. Step 1': The gNB obtains ground truth data (the true value for certain data (e.g., UE position)). Step 2: The gNB calculates performance metrics for model monitoring. Step 3: The gNB reports the performance metrics for model monitoring. Step 3': The gNB requests model activation / deactivation / switching from the LMF. Step 4: The gNB receives an instruction for model activation / deactivation / switching from the LMF. Step 5: The gNB performs model activation / deactivation / switching.

[0124] The performance metric calculation (model monitoring) of <7> above may be performed according to the following steps: Step 1: The gNB obtains and reports estimated data from model inference. Step 2: The LMF obtains ground truth data (the true value for certain data (e.g., UE position)). Step 3: The LMF calculates performance metrics for model monitoring. Step 4: The gNB receives an instruction for model activation / deactivation / switching from the LMF. Step 5: The gNB performs model activation / deactivation / switching.

[0125] <<Application of Performance Metrics on the UE Side>> When model inference is performed on the NW / UE side, the UE may determine whether the requirements of the performance metrics are met.

[0126] The UE may report at least one of the following monitoring information to the NW via higher layer signaling / physical layer signaling such as LPP / MAC CE / DCI / RRC / NRPPa: - Accuracy of the output information of the monitored AI model; - Difference between the inferred horizontal position / vertical position and the actual horizontal position / vertical position; - Difference between the inferred output (ToA, AoA, RSTD, RSRP, etc.) and the output obtained from the actual position (ToA, AoA, RSTD, RSRP, etc.); - Latency difference; - Complexity of the AI ​​model and latency, required complexity; - Binary indicator indicating whether the performance metric requirements are met; - Calculated estimation accuracy (horizontal accuracy / vertical accuracy / intermediate feature accuracy); - Information on the reliability (value) of the estimation accuracy.

[0127] Furthermore, the above-mentioned monitoring information may be reported based on at least one of the following options: <Option 1> - When some of multiple conditions are met (e.g., when performance metrics do not meet certain requirements); <Option 2> - Always report after monitoring (report unconditionally); <Option 3> - Report based on configuration / NW instructions (e.g., periodically, semi-permanently, aperiodically).

[0128] <<Application of performance metrics on the gNB side>> When model inference is performed on the UE / gNB / LMF side, the gNB may determine whether the performance metric requirements are met.

[0129] The UE / LMF may report at least one of the following output information of the AI ​​model to the gNB / LMF via higher layer signaling / physical layer signaling such as LPP / MAC CE / DCI / RRC / NRPPa. In this case, the UE may report to the gNB via the LMF: Inferred UE coordinates, Inferred ToA, Inferred LOS / NLOS indicator, Inferred AoA, RSTD, RSRP, Complexity of the AI ​​model, Latency.

[0130] After comparing the information reported by the UE / information instructed by the LMF with the actual information, the gNB may instruct the UE / LMF of at least one of the above-mentioned monitoring information (UE-side information) via higher layer signaling / physical layer signaling such as LPP / MAC CE / DCI / RRC / NRPPa.

[0131] <<Application of performance metrics on the LMF side>> When model inference is performed on the UE / gNB / LMF side, the LMF may determine whether the performance metric requirements are met.

[0132] The UE / gNB may report at least one of the following output information of the AI ​​model to the NW via higher layer signaling / physical layer signaling such as LPP / MAC CE / DCI / RRC / NRPPa: - Inferred UE coordinates, - Inferred ToA, - Inferred LOS / NLOS indicator, - Inferred AoA, RSTD, RSRP, - Complexity of the AI ​​model, - Latency.

[0133] After comparing the information reported from the UE / gNB with the actual information, the LMF may instruct the UE / gNB of at least one of the above-mentioned monitoring information (UE-side information) via higher layer signaling / physical layer signaling such as LPP / MAC CE / DCI / RRC / NRPPa.

[0134] With regard to the application of performance metrics on the UE / gNB / LMF side as described above, it is preferable that model monitoring and model inference are performed in the same entity (UE / gNB / LMF).

[0135] <Issues in Model Monitoring> With regard to model monitoring of AI / ML-based positioning, consideration is being given to determining for which entities the calculation / application of the above-mentioned performance metrics should be specified.

[0136] For monitoring of AI / ML models in lifecycle management, at least the following metrics / methods are being considered for each use case: - Monitoring based on inference accuracy (including metrics related to intermediate KPIs), - Monitoring based on system performance (including metrics related to system performance KPIs), - Other monitoring, <Option 1> - Monitoring based on data distribution, (a) Input-based: For example, monitoring the validity of AI / ML input (out-of-distribution detection, input data drift detection, SNR, delay spread, etc.), (b) Output-based: For example, output data drift detection, <Option 2> - Monitoring based on application conditions

[0137] Metric calculation for model monitoring may be performed by the UE / NW.

[0138] As mentioned above, when it comes to monitoring AI / ML models for positioning, consideration is being given to who will calculate various metrics, how they will be calculated, and how they will be reported.

[0139] <<Entity that derives performance indicators (monitoring metrics)>> In addition to the above, for performance monitoring of AI / ML-based positioning, the following entities can be used to derive monitoring indicators: UE for Case 1 / Case 2a with (using) the UE-side model; gNB for Case 3a with (using) the gNB-side model; LMF for Case 2b / Case 3b with (using) the LMF-side model.

[0140] Additionally, the following entities are also considered: LMF for case 2a (using UE-side model) and case 3a (using gNB-side model), where monitoring is performed based at least on the provided ground truth labels (or an approximation thereof).

[0141] For example, in cases 2a and 3a, the UE side / gNB side model can be monitored by the UE / gNB or LMF.

[0142] The present disclosure is not limited to the above-described cases, but can also be applied to cases where the model in Case 1 can be monitored by the LMF-side model.

[0143] (Life Cycle Management (LCM)) In future wireless communication systems (for example, Rel. 18 and later), the introduction of multiple LCMs is being considered.

[0144] The plurality of LCMs may include a functionality-based LCM and a model-ID-based LCM. The functionality-based LCM may be referred to as a functionality-based LCM, and the model-ID-based LCM may be referred to as a model-ID-based LCM.

[0145] In functionality-based LCM, the network (e.g., a base station / network node) may instruct operations related to the functionality of the AI / ML (e.g., at least one of activation, deactivation, fallback operation, and switch).

[0146] The UE may perform model-level LCM (eg, model switching and / or model selection) among the indicated functionality.

[0147] Among other things, the functionality may be transparent as to which models are activated / deactivated.

[0148] UE Capability information reporting may be used to signal supported functionality.

[0149] In model-ID-based LCM, a network (e.g., a base station / network node) may instruct an operation (e.g., at least one of activation, deactivation, fallback operation, and switch) related to an individual AI / ML model by a model ID.

[0150] The UE may perform model-level LCM (e.g., at least one of model switching and model selection) based on instructions from the NW.

[0151] A model may be defined in the NW by a model identifier (ID).

[0152] (Cases Related to Model Delivery / Transfer) Regarding model delivery / transfer, when a model is trained by a network, three cases are defined in existing scenarios.

[0153] [Case y] First, the NW trains a model and distributes the model to the UE outside the 3GPP network through offline engineering by multiple vendors.

[0154] The UE then reports its support for the delivered model (this step may be called model identification).

[0155] [Case z2] First, the NW trains a model through offline engineering of multiple vendors and saves it in a proprietary format, which may mean a format defined for each vendor.

[0156] The UE then reports the use of the stored model in the 3GPP network (this step may be called model identification).

[0157] A model transport is then carried out.

[0158] The UE then reports support for the transferred model (this step may be called model identification).

[0159] [Case z4] First, the UE reports the supported model structure (this step may be called model identification).

[0160] The NW then forwards the model parameters of the supported model structures.

[0161] The UE then reports support for the transferred model (this step may be called model identification).

[0162] (Meta Information) The UE may receive at least one of the following as meta information.

[0163] Note that the term "meta information" in this disclosure is merely an example, and meta information may refer to at least one of specific setting information, scenario information, environmental information, support information, and model information. In this disclosure, meta information, setting information, scenario information, environmental information, support information, and model information may be read interchangeably.

[0164] The meta information used by the UE / NW may include at least one of information regarding NW configuration / deployment, information regarding the environment, information regarding the AL / ML model on the NW side, and information regarding the model required by the NW.

[0165] The information regarding network settings / deployment may include, for example, information regarding antenna settings.

[0166] The information regarding the antenna configuration may indicate, for example, 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.

[0167] The information regarding network configuration / deployment may include, for example, information regarding beam configuration.

[0168] The information about the beam setting may include, for example, at least one of the beam width, the number of beams, and the beam direction.

[0169] The information regarding network configuration / deployment may include, for example, information regarding TRP.

[0170] The information regarding the beam setting may include, for example, at least one of the altitude of the TRP and the relative positions of the multi-TRP.

[0171] The information about the environment may include, for example, information about the deployment scenario.

[0172] The information regarding the deployment scenario may indicate, for example, at least one of Urban Macro (UMa), Urban Micro (Umi), and Indoor Hotspot (InH).

[0173] The information about the environment may include, for example, information about indoors or outdoors.

[0174] The information about indoor or outdoor may indicate, for example, an indoor / outdoor probability.

[0175] The information about the environment may for example be information about objects around the UE / base station.

[0176] The information about the objects around the UE / base station may, for example, indicate the location of the objects around the UE / base station.

[0177] The information about the environment may include, for example, the scenario setting format (meta information) described below.

[0178] Use cases using AI models may be associated with a scenario-setting format that consists of long-term features.

[0179] Note that long-term features may be interchangeably read as short-term / mid-term / long-term features, simply features, etc. Furthermore, scenario setting format may be interchangeably read as meta information, meta information format, scenario and configuration format, scenario configuration format, scenario format, setting format, use case format, environment format, meta format, etc. Furthermore, format may be interchangeably read as type, mode, data, setting, etc.

[0180] The above features may include one or more combinations of the following elements: - Scenario / model (Urban Macro (UMa), Urban Micro (Umi), indoor, outdoor, indoor hotspot (InH), etc.); - Frequency / frequency range; - Numerology (or subcarrier spacing); - Distribution / set of general channel parameters (e.g., inter-site distances (ISD), gNB height, delay spread, angle spread, Doppler spread, etc.) in a single scenario / model; - UE distribution; - UE speed; - UE orbit; - Number of transmit beams / receive beams; - UE rotation pattern; - gNB / UE antenna configuration (e.g., transmit and receive antenna vectors); - Number of cells / sectors; - Bandwidth; - UE payload; - Channel quality (e.g., RSRP, SINR); - Beam configuration ID; - Physical Cell ID (PCI). Global Cell ID (GCI) Absolute Radio Frequency Channel Number (ARFCN) Line Of Site (LOS) / Non-Line Of Site (NLOS) probability.

[0181] A UE may be expected to be configured / registered with a model whose associated scenario configuration format matches the UE's configuration / status.

[0182] The UE may also be expected to activate a model whose associated scenario configuration format matches the UE's configuration / status.

[0183] The correspondence between a use case and a scenario configuration format may be specified in a standard, and information on the correspondence may be notified to the UE. Also, the features included in the scenario configuration format corresponding to the use case may be specified in a standard, and information on the features may be notified to the UE.

[0184] The information on the AL / ML model on the NW side may include, for example, information on paired models available on the NW side.

[0185] The information about the paired model available on the NW side may indicate, for example, a paired decoder for CSI compression.

[0186] The information about the AL / ML model on the NW side may include, for example, information about pre-processing / post-processing available on the NW side.

[0187] The information regarding pre-processing / post-processing available on the NW side may include, for example, at least one of quantization / dequantization processing, DFT transformation, IDFT transformation, FFT transformation, and IFFT transformation.

[0188] UE Assistance Information The UE may report assistance information / metadata (meta-information) for the AI / ML model.

[0189] The assistance information may include, for example, at least one of the following information (user status): overheating assistance information; DRX parameter preference; priority regarding maximum aggregate bandwidth; preference for maximum number of MIMO layers.

[0190] The AI / ML model may be an AI / ML model that is registered / configured / compiled / activated in the UE.

[0191] The aiding information / metadata for the AI / ML model may be transmitted together with or instead of the beam information, which may be, for example, information about the UE's antennas / beams.

[0192] The supporting information / metadata of the AI / ML model may be at least one of the information described below.

[0193] The supporting information / metadata for the AI / ML model may be the ID of the AI / ML model.

[0194] The ID of the AI / ML model may be a global / local AI / ML model ID.

[0195] The supporting information / metadata for the AI / ML model may be information regarding the applicable bandwidth corresponding to the AI / ML model ID.

[0196] The bandwidth may be indicated as the applicable minimum / maximum bandwidth.

[0197] The information about the bandwidth may include, for example, information indicating a band indicator (e.g., "freqBandIndicatorNR"). The information indicating the band indicator may be represented by a specific number of bits (e.g., 10 bits).

[0198] The information about the bandwidth may include, for example, information indicating the bandwidth of the RS associated with the corresponding AI / ML model (eg, "supportedBandwidth").

[0199] The information indicating the bandwidth of the RS associated with the corresponding AI / ML model may indicate the frequency for each frequency range (for example, FR1 / FR2 (FR2-1 / FR2-2) / FR3 / FR4 / FR5).

[0200] The supporting information / metadata of the AI / ML model may be information about the applicable area corresponding to the AI / ML model ID.

[0201] The information regarding the applicable area corresponding to the AI / ML model may include at least one of the following information (list of information): Area ID. Cell global ID (in NR). Physical cell ID (Identifier) ​​(in NR). ARFCN (Absolute Radio Frequency Channel Number). Evolved Cell Global ID (ECGI).

[0202] The area ID may include at least one of the global ID of the NR cell, the physical cell ID of the NR, and the ARFCN.

[0203] The support information / metadata for the AI / ML model may be antenna setting / beam information corresponding to the AI / ML model ID.

[0204] (KPI) Common Key Performance Indicators (KPIs) are being considered for monitoring the performance of AI models.

[0205] Below is an initial list of common KPIs for evaluating the performance impact of AI / ML models: Performance, Intermediate KPIs, Link-level and system-level performance, Generalization performance, Over-the-air overhead, Assistance information overhead, Data collection overhead, Model delivery / transfer overhead, Other AI / ML model related signaling overhead, Inference complexity, Model inference computational complexity: floating point operations (FLOPs) (note that the s is lowercase), Pre- and post-processing computational complexity, Model complexity (number of parameters / data size (e.g., Mbyte), etc.), Training complexity, LCM related complexity. complexity) and storage overhead, and latency (e.g., inference latency). Note that the above KPIs are merely examples, and other KPIs may be added to the list (e.g., KPIs related to model training, use-case-specific KPIs considered for a given use case, etc.).

[0206] Of the above-mentioned KPIs, KPIs related to performance may be called performance KPIs.

[0207] Functionality Related Information A UE may report the functionality it supports (or is supported).

[0208] A functionality may be associated with, include, or belong to a particular piece of information.

[0209] In this disclosure, "be associated with," "include," "belong to," and "correspond to" may be used interchangeably.

[0210] The specific information may be at least one of the following: The specific information and the functionality-related information may be interchangeable.

[0211] The particular information may be information regarding applicable conditions / settings.

[0212] The specific information may be, for example, applicable NW configurations. For example, the applicable parameters related to the NW configurations may be at least one of parameters of system information and parameters of assistance information.

[0213] The specific information may for example be applicable UE configuration, for example applicable parameters of the UE configuration may be higher layer (e.g. RRC) parameters.

[0214] The specific information may be, for example, information about an applicable scenario, such as information indicating at least one of NLOS / LOS, UE distribution, SINR, RSRP, bandwidth, frequency, indoor / outdoor, and deployment scenario (e.g., at least one of Urban Macro (UMa), Urban Micro (Umi), and Indoor Hotspot (InH)).

[0215] The specific information may be, for example, information about an applicable arrangement, which may be information indicating at least one of an applicable antenna configuration (e.g., at least one of the number of antenna elements / panels in the horizontal / vertical directions, the number of ports, the antenna spacing, the antenna position, the panel position, and the transceiver unit (TxRU) mapping), a beam configuration (e.g., at least one of the beam width, the number of beams, and the beam direction), and TRP information (e.g., at least one of the altitude of the TRP and the relative positions of multiple TRPs).

[0216] The specific information may be, for example, information about an applicable site, or may be information indicating at least one of an area ID, an NR cell global ID, an NR physical cell ID, a specific frequency (e.g., ARFCN), and an ECGI.

[0217] The specific information may be, for example, information about an applicable paired model. The paired model may be, for example, two (two or more) network-side models (combinations of models). The specific information may be, for example, information indicating a paired model for CSI compression.

[0218] The specific information may be, for example, information about an applicable time. The information may be, for example, information indicating an applicable period / interval / duration. The period may be, for example, indicated using a specific time unit (e.g., slot / symbol / subslot / millisecond / second).

[0219] The applicable conditions / settings may be predefined in the specification or may be determined / identified using a specific ID / token. For example, the applicable settings / scenarios / conditions may be defined as specific parameters (e.g., test parameters) or may be represented using a specific ID / token.

[0220] The performance of a functionality (e.g., prediction accuracy, location error) may be assumed to be better (e.g., higher) than a particular threshold under applicable conditions / settings, which may be, for example, the conditions / settings of a particular test.

[0221] The thresholds / performance requirements may be pre-specified or may be determined / identified using a specific ID / token.

[0222] If the threshold / performance requirements are based on a specific ID / token, each vendor / operator can define and utilize the desired thresholds / performance.

[0223] (Measurement Settings for Positioning) The following can be exemplified as [information relating to] measurement settings for positioning.

[0224] The information related to the frequency layer may include at least one of the following: dl-PRS-SubcarrierSpacing, dl-PRS-CyclicPrefix, dl-PRS-PointA (lowest subcarrier), dl-PRS-StartPRB (start PRB index from reference point A), and dl-PRS-ResourceBandwidth (24 PRBs to 272 PRBs, 4 PRBs step).

[0225] The information about the resource set may include at least one of the following: nr-DL-PRS-ResourceSetID dl-PRS-Periodicity-and-ResourceSetSlotOffset dl-PRS-ResourceRepetitionFactor dl-PRS-ResourceTimeGap dl-PRS-MutingOption1 and dl-PRS-MutingOption2 NR-DL-PRS-SFN0-Offset: offset of SFN0 slot0 of cell with respect to SFN0 slot0 of reference cell dl-PRS-CombSizeN dl-PRS-NumSymbol: size of the downlink PRS resource in the time domain {2,4,6,12}

[0226] The information about the resource may include at least one of the following: nr-DL-PRS-ResourceID, dl-PRS-SequenceID, starting RE offset, starting slot offset, starting symbol offset, and QCL info.

[0227] (Analysis) In future wireless communication systems (for example, Rel. 19 and later), data collection for AI / ML-based positioning is being considered.

[0228] As a premise for this data collection, in the case of the UE-side model (e.g., case 1 / 2a above), it is considered that the ground truth label can be provided by the non-PRU UE together with the estimated location. In this procedure, the estimated location or intermediate value of the UE may not be reported to the LMF.

[0229] In UE-based positioning, the estimated location of the UE may be reported to the LMF as a ground truth label, and in UE-assisted positioning, measured intermediate values ​​of the UE (e.g., timing information and / or LOS / NLOS indicators) may be reported to the LMF as a ground truth label.

[0230] For non-PRU UEs, positioning methods for generating a ground truth label for the estimated location include: GNSS / Assisted-(A-)GNSS positioning; Barometric pressure sensor positioning; Terrestrial Beacon Systems (TBS) positioning; Motion sensor positioning; Wireless Local Area Network (WLAN) positioning; Bluetooth positioning.

[0231] In GNSS / A-GNSS positioning, the information shown in Fig. 7 may be transmitted / transferred from the UE to the LMF. Fig. 7 shows the information to be transmitted / transferred in each of UE-assisted positioning and UE-based ( / standalone) positioning ("Yes" means transmitted, and "No" means not transmitted, and the same applies to the following figures).

[0232] In the barometric pressure sensor positioning, the information shown in Fig. 8 may be transmitted / transferred from the UE to the LMF. Fig. 8 shows the information transmitted / transferred in each of the UE-assisted positioning and the UE-based positioning.

[0233] In the TBS positioning, the information shown in Fig. 9 may be transmitted / transferred from the UE to the LMF. Fig. 9 shows information transmitted / transferred in each of the UE-assisted positioning and the UE-based positioning.

[0234] In the motion sensor positioning, the information shown in Fig. 10 may be transmitted / transferred from the UE to the LMF. Fig. 10 shows the information transmitted / transferred in each of the UE-assisted positioning and the UE-based positioning.

[0235] In WLAN positioning, the information shown in Fig. 11 may be transmitted / transferred from the UE to the LMF. Fig. 11 shows information transmitted / transferred in each of UE-assisted positioning and UE-based positioning.

[0236] In Bluetooth positioning, the information shown in Fig. 12 may be transmitted / transferred from the UE to the LMF. Fig. 12 shows information transmitted / transferred in each of UE-assisted positioning and UE-based positioning.

[0237] In addition, in DL TDOA positioning, the UE ( / NW) may receive a request for location information (e.g., RequestLocationInformation), [perform measurements], and report corresponding information (response information, e.g., ProvideLocationInformation).

[0238] As shown in Figures 13 and 14, the request regarding the location information (e.g., NR-DL-TDOA-RequestLocationInformation-r16 / NR-DL-TDOA-ReportConfig-r16) includes various information.

[0239] Also, as shown in FIG. 15, the response information (for example, NR-DL-TDOA-ProvideLocationInformation-r16) includes various types of information.

[0240] Furthermore, existing NR (up to Rel. 18) specifies requirements for measurement accuracy. For example, the accuracy requirements for RSTD measurements in FR1 for an AWGN (additive white Gaussian noise) channel are specified in the table shown in Figure 16. "Accuracy" in this table corresponds to the accuracy value X.

[0241] However, in each case of AI / ML-based positioning (for example, at least one of the above cases 1 to 3b), there are cases that have not been sufficiently considered.

[0242] <Issue 1> For AI / ML-based positioning, in each case on the UE / gNB / LMF side (e.g., at least one of cases 1 to 3b above), the existing NR framework is considered as the baseline, and generally, in all LCM procedures, the existing NR positioning request procedure is commonly considered in each case.

[0243] However, in the UE-side model, UE-side measurements are not necessarily reported to the LMF in the differential LCM procedure. If the UE estimates the location / mean values ​​for training data collection, these information do not need to be reported to the LMF.

[0244] In this case, if the same settings as the existing ones are used, there is a risk that signaling / procedures will become redundant. Therefore, it is necessary to consider how to specify / support different UE behaviors in different LCM procedures, but this has not been sufficiently studied.

[0245] <Issue 2> In data collection for AI / ML-based positioning, it is being considered that a combination of multiple measurements / reports and / or post-report processing of measurements reported from a UE are not excluded. In this case, the NW needs to recognize whether the data collected on the UE side is a processed measurement result.

[0246] If post-processed result reporting is supported, it is necessary to consider whether the definition of post-processing in the specification, the instructions for post-processing result reporting, and the accuracy requirements are the same, but this has not been sufficiently considered.

[0247] <Issue 3> When the network does not recognize the model / functionality of the UE side, it would be beneficial to introduce a procedure to indicate whether the positioning information reported by the UE was generated by an AI / ML model. However, there has been insufficient consideration on how to achieve this.

[0248] Unless these issues are clearly addressed, it may not be possible to achieve optimal overhead reduction, channel estimation, or resource utilization, which could hinder improvements in communication throughput and communication quality.

[0249] Therefore, the present inventors came up with a method for solving these problems.

[0250] (Various Reinterpretations) In the present disclosure, a word enclosed in "( )" in a sentence may indicate an explanation of the word immediately preceding it (for example, an explanation of spelling), a paraphrase, a specific example, a supplementary explanation, etc. Furthermore, in the present disclosure, a word enclosed in "[ ]" in a sentence may be interpreted including the word in the meaning of the entire sentence, or may be interpreted excluding (ignoring) the word in the meaning of the entire sentence. Note that "( )" and "[ ]" may also be used for purposes / meanings other than those mentioned above.

[0251] In the present disclosure, "A / B" and "at least one of A and B" may be interpreted interchangeably. Also, in the present disclosure, "A / B / C" may mean "at least one of A, B, and C."

[0252] In the present disclosure, terms such as notify, activate, deactivate, indicate (or indicate), select, configure, update, and determine may be read interchangeably. In the present disclosure, terms such as support, control, controllable, operate, and operate may be read interchangeably.

[0253] In the present disclosure, Radio Resource Control (RRC), RRC parameters, RRC messages, higher layer parameters, fields, information elements (IEs), settings, etc. may be interchangeable. In the present disclosure, Medium Access Control (MAC) control elements (CEs), update commands, activation / deactivation commands, etc. may be interchangeable.

[0254] In the present disclosure, the 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 (e.g., NR Positioning Protocol A (NRPPa) / LTE Positioning Protocol (LPP)) messages), or a combination thereof.

[0255] In the present disclosure, MAC signaling may use, for example, a MAC Control Element (MAC CE), a MAC Protocol Data Unit (PDU), etc. Broadcast information may be, for example, a Master Information Block (MIB), a System Information Block (SIB), Remaining Minimum System Information (RMSI), Other System Information (OSI), etc.

[0256] In the present disclosure, physical layer signaling may be, for example, Downlink Control Information (DCI), Uplink Control Information (UCI), and the like.

[0257] In the present disclosure, the terms index, identifier (ID), indicator, resource ID, etc. may be interchangeable. In the present disclosure, the terms sequence, list, set, group, cluster, subset, etc. may be interchangeable.

[0258] In the present disclosure, the terms 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 (PUCCH) group, PUCCH resource group), resource (e.g., reference signal resource, SRS resource), resource set (e.g., reference signal resource set), CORESET pool, 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), QCL assumption, etc. may be read as interchangeable.

[0259] In the present disclosure, CSI-RS, non-zero power (NZP) CSI-RS, zero power (ZP) CSI-RS, and CSI interference measurement (CSI-IM) may be interchangeable. Furthermore, CSI-RS may include other reference signals.

[0260] In this disclosure, a measured / reported RS may refer to an RS that is measured / reported for a CSI report.

[0261] In the present disclosure, timing, time, duration, slot, subslot, symbol, subframe, etc. may be read interchangeably.

[0262] In the present disclosure, the terms direction, axis, dimension, domain, polarization, polarization component, etc. may be read interchangeably.

[0263] In the present disclosure, estimation, prediction, and inference may be used interchangeably. Also, in the present disclosure, estimate, predict, and infer may be used interchangeably.

[0264] In the present disclosure, the terms autoencoder, encoder, decoder, etc. may be replaced with at least one of a model, an ML model, a neural network model, an AI model, an AI algorithm, etc. Furthermore, the term autoencoder may be replaced with any autoencoder, such as a stacked autoencoder or a convolutional autoencoder. The encoder / decoder of the present disclosure may employ a model such as a Residual Network (ResNet), a DenseNet, or a RefineNet.

[0265] In the present disclosure, the terms bit, bit string, bit sequence, sequence, value, information, value obtained from a bit, information obtained from a bit, etc. may be read interchangeably.

[0266] In the present disclosure, a layer (for an encoder) may be interchangeably read as a layer (such as an input layer or an intermediate layer) used in an AI model. The layer in the present disclosure may correspond to at least one of an input layer, an intermediate layer, an output layer, a batch normalization layer, a convolutional layer, an activation layer, a dense layer, a normalization layer, a pooling layer, an attention layer, a dropout layer, a fully connected layer, etc.

[0267] In the present disclosure, RSRP may be interchangeably read as any parameter related to received power / received quality, etc. (e.g., RSRQ, SINR, CSI), etc.

[0268] In the present disclosure, the RS may be, for example, a CSI-RS, an SS / PBCH block (SS block (SSB)), etc. Also, the RS index may be a CSI-RS Resource Indicator (CRI), an SS / PBCH Block Indicator (SSBRI), etc.

[0269] In the present disclosure, channel measurement / estimation may be performed using at least one of, for example, a Channel State Information Reference Signal (CSI-RS), a Synchronization Signal (SS), a Synchronization Signal / Physical Broadcast Channel (SS / PBCH) block, a Demodulation Reference Signal (DMRS), a Sounding Reference Signal (SRS), and the like.

[0270] In the present disclosure, the terms "receive beam assumption," "number of receive beams," "index of receive beam," "receive beam selection," "receive beam setting," and "receive beam instruction" may be interchangeable. In the present disclosure, the terms "receive beam," "transmit beam," "DL receive beam," "DL transmit beam," and "pair of transmit beam and receive beam" may be interchangeable. In the present disclosure, the terms "transmit / receive beam" may be interchangeable with the terms "transmit / receive beam for beam prediction" and "transmit / receive beam for CSI measurement / reporting for beam prediction."

[0271] In this disclosure, functionality may refer to the use of a model or the physical meaning of the model's input / output. Multiple models may have the same functionality. Monitoring (checking performance), activation, deactivation, switching, fallback, and updating may be instructed (controlled) based on the functionality (e.g., for each function).

[0272] In this disclosure, functionality may refer to features (requiring AI / ML capabilities) (e.g., CSI prediction / CSI compression / temporal beam prediction / reporting information based on spatial domain beam prediction).

[0273] A model ID may also refer to an identifier for a model (or a set of models). Multiple models may be assigned the same model ID in an actual deployment. In this case, these models may actually be different models (e.g., have different numbers of layers) but may be treated as the same model.

[0274] In the present disclosure, the model ID may be interchangeably referred to as a meta information (or a set of meta information) ID. The meta information (or meta information ID) may be associated with information about the applicability of the model / functionality, the environment, the UE / gNB settings, etc.

[0275] In the present disclosure, functionality may be simply read as "function."

[0276] In the present disclosure, functionality, function, functionality ID, model, and model ID may be read interchangeably.

[0277] In the present disclosure, applicable update, updating applicable models / functionality, updating applicability of models / functionality, and applicable update may be read interchangeably.

[0278] In the present disclosure, update, report, and send may be read interchangeably.

[0279] In the present disclosure, applicability report and applicability report may be read interchangeably.

[0280] In the present disclosure, meta information, assistance information, sensing information, KPI, performance KPI, UE status, and status may be read interchangeably.

[0281] In the present disclosure, the terms drop, abort, cancel, puncture, rate match, postpone, do not transmit, etc. may be read interchangeably.

[0282] In the present disclosure, an ID may represent an ID corresponding to (for identifying) at least one of a dataset, a model, and a property of a channel / RS. That is, in the present disclosure, an ID, a dataset ID, a model ID, and a property ID of a channel / RS may be read as interchangeable.

[0283] In the present disclosure, the terms entity, specific entity, UE, NW, gNB, and LMF may be read interchangeably.

[0284] In the present disclosure, NW, LMF, gNB, BS, and NG-RAN may be read interchangeably.

[0285] In the present disclosure, the terms performance index and monitoring index may be read interchangeably.

[0286] In the following embodiments, to explain the AI ​​model for communication between UE / gNB (NG-RAN) / LMF, the relevant entities are UE / gNB / LMF, but the application of each embodiment of the present disclosure is not limited to this. For example, for communication between different entities (e.g., communication between UEs), the UE / gNB / LMF in the following embodiments may be replaced with a first UE, a second UE, a third UE, etc. In other words, the UE / gNB / LMF in the present disclosure may be replaced with any UE / gNB / LMF. Furthermore, the NW / BS / gNB / LMF may be replaced with each other.

[0287] (Wireless communication method) The UE / NW (gNB / LMF) may perform positioning by applying each of the following embodiments.

[0288] The UE may receive various settings for positioning / reporting, and may further report / transmit corresponding prediction (positioning) results to the NW.

[0289] The NW may send various settings for positioning / reporting to the UE, and may also receive corresponding prediction results (reports) from the UE.

[0290] First Embodiment The first embodiment relates to a solution to the above-mentioned issue 1.

[0291] <<Embodiment 1-1>> For a UE, settings for training / monitoring / inference of an AI / ML model and AI / ML functionality (eg, location information request) may be separately defined / configured.

[0292] The UE may perform model / function training / monitoring / inference operations based on the configuration.

[0293] For example, a setting for collecting data for AI / ML-based positioning (e.g., a location information request) may include a parameter indicating usage.

[0294] The parameters may, for example, indicate whether the data is used for training / inference / monitoring or whether the data is used for measurement / labeling.

[0295] Different parameters / information elements may be defined / configured within the configuration for different uses (eg, data collection for training / monitoring or data collection for inference).

[0296] For example, if data collection is initiated by the UE, the UE may send a request for settings for data collection along with or separately from the data collection request.

[0297] It should be noted that this embodiment may be applied, for example, in specific cases (e.g., case 1 / 2a) or in any case using UE-side (AI / ML) models / functions.

[0298] For example, the settings to be used for the data collection settings (e.g., requesting location information) may be determined according to the parameters indicating the purpose. For example, the parameters included in the data collection settings (e.g., requesting location information) may be determined according to the parameters indicating the purpose.

[0299] For example, for data collection for a specific application (e.g., model inference), the same settings as an existing location request may be reused.

[0300] For example, a new configuration may be used for data collection for a different purpose (e.g., model training / monitoring), where the new configuration may be a configuration in which certain (unnecessary) parameters in the existing configuration have been deleted.

[0301] For example, when model inference is performed in the UE-side model, all parameters included in the configuration (NR-DL-TDOA-RequestLocationInformation-r16) as shown in Figure 17 may be included (used). Also, when model training / monitoring is performed in the UE-side model, certain parameters included in the configuration (NR-DL-TDOA-RequestLocationInformation-r16) as shown in Figure 17 may not be included (used).

[0302] Note that a parameter indicating the use (for example, usage-r19) may be included in the information element shown in FIG.

[0303] For example, the base station may control whether or not to include a specific parameter indicating the intended use in a setting (NR-DL-TDOA-RequestLocationInformation) in which the parameter indicates a specific value (e.g., for model training / monitoring / inference).

[0304] For example, the UE may assume that a configuration (NR-DL-TDOA-RequestLocationInformation) in which a parameter indicating the intended use indicates a specific value (e.g., for model training / monitoring / inference) includes / does not include the specific parameter.

[0305] The specific parameter may be, for example, at least one of a parameter indicating the presence or absence of assistance (e.g., nr-AssistanceAvailability-r16), a parameter related to the reporting configuration (e.g., nr-DL-TDOA-ReportConfig-r16), a parameter related to the first additional path (e.g., additionalPaths-r16), a parameter related to a request for a receiving TEG (e.g., nr-UE-RxTEG-Request-r17), a parameter related to a second additional path (e.g., additionalPathsExt-r17), a parameter related to a request for an additional path of the RSRP of the DL PRS (e.g., additionalPathsDL-PRS-RSRP-Request-r17), and a parameter related to multiple measurements in the same report (e.g., multiMeasInSameReport-r17).

[0306] For one UE-side model, the existing configuration may be reused when the UE performs data collection for a first application (e.g., model inference), and a "new" configuration without specific parameters may be used when the UE performs data collection for a second application (e.g., model training / monitoring).

[0307] According to embodiment 1-1, signaling overhead can be reduced depending on the purpose specified in the data collection settings (for example, location information request).

[0308] <<Embodiment 1-2>> Location information requests in existing NRs may be commonly defined / configured for model inference / training / monitoring.

[0309] The UE may determine a positioning operation based on the state of the model procedure (model procedure state). For example, the UE may determine to perform model / function training / monitoring / inference operations and / or corresponding reporting operations based on the state of the model procedure.

[0310] For example, the UE may decide whether to report measurements based on the state of the model procedure.

[0311] In this disclosure, "X phase" may be read as "a period for reporting measurement / inference results for X."

[0312] For example, in the training phase, if the model training is performed on the UE side, the UE may not need to report measurement results to the LMF.

[0313] For example, in the training phase, if model training is performed on the LMF side, the UE may report the measurement results as the existing NR positioning.

[0314] For example, in the case of the inference phase, the UE may report the output of the inference to the LMF.

[0315] For example, in the monitoring phase, if the metric calculation for monitoring is performed on the UE side, the UE may not need to report the measurement results to the LMF.

[0316] For example, in the monitoring phase, if the metric calculation for monitoring is performed on the LMF side, the UE may report the measurement results as the existing NR positioning.

[0317] For different data collection applications, the information / parameters that are ignored may be different.

[0318] The state of the model procedure may be known in the UE / NW, for example, via an LCM procedure. The configurations included in the configuration may be determined, for example, based on predefined / specified rules.

[0319] Note that this embodiment may be applied when a UE or a third party communicating with the UE (e.g., an OTT (Over The Top, e.g., content / services / functions provided by an independent provider / vendor bypassing a telecommunications carrier's network) server / SUPL (Secure User Plane Location, e.g., used for secure exchange of location data) server) performs model training / inference / monitoring (calculation of performance metrics).

[0320] According to embodiment 1-2, signaling overhead can be reduced depending on the state of the model procedure.

[0321] According to the first embodiment described above, it is possible to contribute to solving the above-mentioned issue 1.

[0322] Second Embodiment The second embodiment relates to a solution to the above issue 2.

[0323] For AI / ML based positioning with UE side models, the processed measurements may be applied as collected data and used as input for model inference.

[0324] Post-processing of measurement results for AI / ML-based positioning may also be defined.

[0325] The post-processing inputs may be, for example, measurements / ground truth labels used in AI / ML-based positioning. The post-processing inputs may include, for example, at least one of the following: UE location. Rx-Tx time difference. DL RSTD. UL RTOA. RSRP / RSRPP. RSCP. LOS / NLOS indicators.

[0326] The output of the post-processing may be, for example, processed measurements for the input (e.g., measurements / ground truth labels). The output of the post-processing may consist of, for example, higher accuracy / larger dataset size (compared to the ground truth labels).

[0327] The UE may decide whether to report physical measurements (without post-processing) or post-processed measurements based on the network configuration / instructions and predefined rules / conditions.

[0328] For example, the UE may be instructed / configured to perform post-processing from the NW via DCI / MAC CE / RRC / LPP.

[0329] The instructions / configurations may include at least one of the following: Instructions regarding reporting of post-processed measurements or raw physical data, Information regarding the (expected) output of the post-processed dataset (e.g. at least one of the information type, by which entity technology it was generated, required dataset size, processing unit, and required accuracy / confidence level).

[0330] The UE may report capabilities regarding at least one of the following: Whether it supports post-processing Whether it supports reporting of post-processed measurements

[0331] The UE may follow predefined rules / conditions.

[0332] For example, if the accuracy requirement is higher than a certain threshold and / or the dataset size is smaller than a certain threshold, the UE may determine that post-processing is applied to UE-side data collection for AI / ML-based positioning.

[0333] For example, the UE may report an indicator as to whether the reported results involve post-processing.

[0334] If the UE decides to report data with post-processing, the UE may report the post-processed measurements.

[0335] In this case, for example, the UE may also report the raw physical data set before any post-processing is performed.

[0336] In this case, for example, the UE may also report the difference in dataset size / processing unit / accuracy / confidence level of the post-processed measurements compared to the physical data.

[0337] In this case, for example, the UE may also report the dataset size / processing unit / accuracy / confidence level of the post-processed measurements.

[0338] If post-processing is applied, corresponding (new) accuracy requirements may be applied, which may for example be defined at least for RSTD measurements / PRS-RSRP measurements / UE receive-transmit time difference measurements.

[0339] The accuracy requirements may be higher / stricter / finer granularity compared to the existing (up to Rel. 18) accuracy requirements for each measurement.

[0340] For example, for RSTD measurements, a new correspondence (table) may be defined with a smaller precision value (eg, X).

[0341] For example, the reference sensitivity conditions specified in the specification are met, the RSTD measurement conditions are met for the corresponding bands of each associated PRS resource configured for measurement as specified in the specification, and the UE may not perform positioning measurements with a reduced number of samples.

[0342] In this case, the UE may perform post-processing of positioning measurements [according to specific technologies / (AI / ML) models / functions] [if the absolute value of the accuracy requirement is higher than a threshold].

[0343] According to the second embodiment, post-processing can be appropriately defined / instructed, which can contribute to solving the above-mentioned issue 2.

[0344] <Third Embodiment> The third embodiment relates to a solution to the above issue 3.

[0345] The UE side model / function for AI / ML-based positioning described in the following embodiments 3-1 / 3-2 may be applied / utilized.

[0346] <<Embodiment 3-1>> The UE may report to the LMF side whether or not the reported positioning information is generated by the AI / ML model.

[0347] The UE may include in the report information about at least one of the following: - An indicator whether the reported information was generated by an AI / ML model; - Validity timer / period of the UE-side model / function; - Validity area of ​​the UE-side model / function; - Local ID / Associated ID of the applied UE-side model / function.

[0348] For this validity timer / period, this reporting may be performed / applicable for each measurement, e.g., X measurements and / or multiple (e.g., all) measurements within a specific period, e.g., the value of X and / or the length of the specific period may be reported by the UE.

[0349] This report to the LMF side may be reported together with the UE side location information or separately from the UE side location information.

[0350] <<Embodiment 3-2>> The UE may transmit a specific setting request to the NW.

[0351] The particular configuration request may be, for example, a configuration request to facilitate a UE-side (AI / ML) model / function.

[0352] For example, the UE may transmit information to notify the NW about the UE side (AI / ML) model / function information.

[0353] Also, for example, the UE may request certain parameters specific to the UE side (AI / ML) model / function for positioning configuration.

[0354] The settings for the specific parameters may be different from the existing settings for positioning (up to Rel. 18).

[0355] The specific parameters may be, for example, parameters relating to at least one of the following: - New measurements (e.g., new channel measurements such as Channel Impulse Response (CIR) / Power Delay Profile (PDP) / Delay Profile (DP)); - Higher accuracy of existing measurements; - New parameters (e.g., number of samples or larger number of paths); - New reporting format (compared to existing).

[0356] According to the third embodiment described above, it is possible to contribute to solving the above-mentioned issue 3.

[0357] <Supplementary Information> <<AI Model Information>> In the present disclosure, AI model information may mean information including at least one of the following: - Input / output information of the AI ​​model. - Pre-processing / post-processing information for the input / output of the AI ​​model. - Parameter information of the AI ​​model. - Training information for the AI ​​model. - Inference information for the AI ​​model. - Performance information regarding the AI ​​model.

[0358] Here, the input / output information of the AI ​​model may include information on at least one of the following: - Contents of the input / output data (e.g., RSRP, SINR, amplitude / phase information in the channel matrix (or precoding matrix), information on the angle of arrival (Angle of Arrival (AoA)), information on the angle of departure (Angle of Departure (AoD)), location information); - Auxiliary information of the data (which may be called meta-information); - Type of the input / output data (e.g., immutable value, floating-point number); - Bit width of the input / output data (e.g., 64 bits for each input value); - Quantization interval (quantization step size) of the input / output data (e.g., 1 dBm for L1-RSRP); - Range that the input / output data can take (e.g., [0, 1]).

[0359] In the present disclosure, the information on AoA may include information on at least one of an azimuth angle of arrival and a zenith angle of arrival (ZoA). The information on AoD may include information on at least one of an azimuth angle of departure and a zenith angle of departure (ZoD).

[0360] In the present disclosure, location information may be location information related to a UE / NW. The location information may include at least one of information (e.g., latitude, longitude, altitude) obtained using a positioning system (e.g., a satellite positioning system (Global Navigation Satellite System (GNSS), Global Positioning System (GPS), etc.)), information about a BS neighboring (or serving) the UE (e.g., a BS / cell identifier (ID), a BS-UE distance, a 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.), a specific address of the UE (e.g., an Internet Protocol (IP) address), etc. The location information of the UE is not limited to information based on the position of the BS, and may be information based on a specific point.

[0361] The location information may include information about its implementation (e.g., location / position / orientation of antennas, location / orientation of antenna panels, number of antennas, number of antenna panels, etc.).

[0362] The location information may include mobility information, which may include information indicating at least one of information indicating a mobility type, a moving speed of the UE, an acceleration of the UE, and a moving direction of the UE.

[0363] Here, the mobility type may correspond to at least one of a fixed location UE, a movable / moving UE, a no mobility UE, a low mobility UE, a middle mobility UE, a high mobility UE, a cell-edge UE, a not-cell-edge UE, etc.

[0364] In the present disclosure, environmental information (for data) may be information about the environment in which the data is acquired / used, and may correspond to, for example, frequency information (such as a band ID), environmental type information (information indicating at least one of indoor, outdoor, Urban Macro (UMa), Urban Micro (Umi), etc.), information indicating Line Of Site (LOS) / Non-Line Of Site (NLOS), etc.

[0365] Here, LOS may mean that the UE and the BS are in an environment where they can see each other (or there is no obstruction), and NLOS may mean that the UE and the BS are not in an environment where they can see each other (or there is an obstruction). The information indicating LOS / NLOS may indicate a soft value (e.g., the probability of LOS / NLOS) or a hard value (e.g., either LOS or NLOS).

[0366] In the present disclosure, meta-information may mean, for example, information regarding input / output information suitable for an AI model, information regarding acquired / acquirable data, etc. Specifically, meta-information may include information regarding beams of RS (e.g., CSI-RS / SRS / SSB, etc.) (e.g., the pointing angle of each beam, the 3 dB beam width, the shape of the pointed beam, the number of beams), layout information of the gNB / UE antenna, frequency information, environmental information, meta-information ID, etc. Note that meta-information may be used as input / output of the AI ​​model.

[0367] The pre-processing / post-processing information for the input / output of the AI ​​model may include information on at least one of the following: Whether to apply normalization (e.g., Z-score normalization (standardization), min-max normalization); Parameters for normalization (e.g., mean / variance for Z-score normalization, min / max for min-max normalization); Whether to apply a specific numerical conversion method (e.g., one hot encoding, label encoding, etc.); Selection rules for whether to use as training data.

[0368] For example, Z-score normalization (x) is performed as a preprocessing step for input information x. ｎｅｗ = (x - μ) / σ, where μ is the mean of x and σ is the standard deviation) ｎｅｗ may be input to the AI ​​model, and the output y ｏｕｔ may be subjected to post-processing to obtain the final output y.

[0369] The information on the parameters of the AI ​​model may include information on at least one of the following: - Information on weights in the AI ​​model (e.g., neuron coefficients (connection coefficients)); - Structure of the AI ​​model; - Type of the AI ​​model as a model component (e.g., Residual Network (ResNet), DenseNet, RefineNet, Transformer model, CRBlock, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU)); - Function of the AI ​​model as a model component (e.g., decoder, encoder).

[0370] Note that the weight information in the AI ​​model may include information on at least one of the following: - Bit width (size) of the weight information; - Quantization interval of the weight information; - Granularity of the weight information; - Range that the weight information can take; - Weight parameters in the AI ​​model; - Information on the difference from the AI ​​model before update (if updating); - Weight initialization method (e.g., zero initialization, random initialization (based on normal distribution / uniform distribution / truncated normal distribution), Xavier initialization (for sigmoid function), He initialization (for rectified linear units (ReLU))).

[0371] The structure of the AI ​​model may also include information about at least one of the following: the number of layers, the type of layer (e.g., convolutional layer, activation layer, dense layer, normalization layer, pooling layer, attention layer), layer information, time series specific parameters (e.g., bidirectionality, time step), parameters for training (e.g., type of function (e.g., L2 regularization, dropout function, etc.), where (e.g., after which layer) to place this function).

[0372] The layer information may include information about at least one of the following: Number of neurons in each layer; Kernel size; Stride for pooling / convolutional layers; Pooling method (MaxPooling, AveragePooling, etc.); Residual block information; Number of heads; Normalization method (Batch normalization, instance normalization, layer normalization, etc.); Activation function (Sigmoid, tanh function, ReLU, leaky ReLU information, Maxout, Softmax).

[0373] An AI model may be included as a component of another AI model, for example, an AI model that includes model component #1, ResNet, model component #2, a Transformer model, a dense layer, and a normalization layer in that order.

[0374] The training information for the AI ​​model may include information about at least one of the following: - Information for the optimization algorithm (e.g., type of optimization (Stochastic Gradient Descent (SGD)), AdaGrad, Adam, etc.), parameters of the optimization (learning rate, momentum information, etc.); - Information on the loss function (e.g., information on metrics of the loss function (Mean Absolute Error (MAE)), Mean Square Error (MSE), Cross Entropy Loss, NLL Loss, Kullback-Leibler (KL) Divergence, etc.)); - Parameters to be frozen for training (e.g., layers, weights); - Parameters to be updated (e.g., layers, weights); - Parameters to be (used as) initial parameters for training (e.g., layers, weights); - Method of training / updating the AI ​​model (e.g., (recommended) number of epochs, batch size, number of data to use for training).

[0375] The inference information for the AI ​​model may include information regarding decision tree branch pruning, parameter quantization, and functions of the AI ​​model, etc. Here, the functions of the AI ​​model may correspond to at least one of, for example, time domain beam prediction, spatial domain beam prediction, an autoencoder for CSI feedback, and an autoencoder for beam management.

[0376] An autoencoder for CSI feedback may be used as follows: The UE inputs the CSI / channel matrix / precoding matrix into the AI ​​model of the encoder and transmits the encoded bits as CSI feedback (CSI report). The BS inputs the received encoded bits into the AI ​​model of the decoder to reconstruct the CSI / channel matrix / precoding matrix, which is the output.

[0377] In spatial domain beam prediction, the UE / BS may input measurements (beam quality, e.g., RSRP) based on sparse (or thick) beams into an AI model and output dense (or thin) beam quality.

[0378] In time domain beam prediction, the UE / BS may input time series (past, present, etc.) measurement results (beam quality, e.g., RSRP) into an AI model and output future beam quality.

[0379] The performance information regarding the AI ​​model may include information regarding the expected value of a loss function defined for the AI ​​model.

[0380] The AI ​​model information in the present disclosure may include information regarding the application range (applicable range) of the AI ​​model. The application range may be indicated by a physical cell ID, a serving cell index, etc. The information regarding the application range may be included in the above-mentioned environment information.

[0381] AI model information regarding a specific AI model may be predetermined in a standard or may be notified to a UE from a network (NW). An AI model defined in a standard may be referred to as a reference AI model. AI model information regarding a reference AI model may be referred to as reference AI model information.

[0382] Note that the AI ​​model information in the present disclosure may include an index for identifying the AI ​​model (which may be referred to as, for example, an AI model index, an AI model ID, a model ID, etc.). The AI ​​model information in the present disclosure may include an AI model index in addition to / instead of the input / output information of the AI ​​model described above. The association between the AI ​​model index and the AI ​​model information (for example, input / output information of the AI ​​model) may be predetermined in a standard, or may be notified to the UE from the NW.

[0383] The AI ​​model information in the present disclosure may be associated with an AI model and may be referred to as AI model relevant information, simply relevant information, etc. The AI ​​model relevant information does not need to explicitly include information for identifying the AI ​​model. The AI ​​model relevant information may be information that includes only meta information, for example.

[0384] In the present disclosure, the model ID may be interchangeably read as an ID (model set ID) corresponding to a set of AI models. Furthermore, in the present disclosure, the model ID may be interchangeably read as a meta information ID. The meta information (or the meta information ID) may be associated with information about a beam (beam setting) as described above. For example, the meta information (or the meta information ID) may be used by the UE to select an AI model taking into account which beam the BS is using, or may be used to notify the BS of which beam to use to apply the AI ​​model deployed by the UE. Furthermore, in the present disclosure, the meta information ID may be interchangeably read as an ID (meta information set ID) corresponding to a set of meta information.

[0385] In this disclosure, functionality may refer to a set of parameters (e.g., a set of parameters for CSI prediction / beam prediction / CSI compression) that can be supported based on conditions dictated by UE capabilities.

[0386] The UE may report parameter values ​​related to the functionality / model as conditions to the NW via higher layer signaling (e.g., RRC, MAC CE) / physical layer signaling (e.g., DCI). For example, the UE may report the conditions using UE capability / feature / feature group reporting.

[0387] The UE may report or instruct the parameter values ​​related to the functionality / model as additional conditions to the NW using higher layer signaling / physical layer signaling or methods other than signaling via the air interface of the NW (e.g., operator's configurations, pre-configured messages, etc.).

[0388] The UE may report or be instructed on information / instructions about the parameters corresponding to these additional conditions (e.g., parameter names) as information / instructions about the additional conditions using higher layer signaling / physical layer signaling or methods other than signaling via the air interface of the network (e.g., operator's configurations, pre-configured messages, etc.).

[0389] For example, the UE may report a device ID, a vendor ID, etc. as additional information. The UE may also be notified of a cell ID as an additional condition. The UE may also report or be instructed to report information such as a cell ID / UE ID instead of a parameter name.

[0390] Methods other than signaling via the air interface of the network may be methods related to pre-configuration of the UE (for example, configuration by the UE vendor) or operator configuration provided by the network operator.

[0391] <<Notification of Information to UE>> In the above-described embodiments, notification of any information to the UE [from a Network (NW) (e.g., a Base Station (BS))] (in other words, reception of any information from the BS by the UE) may be performed using physical layer signaling (e.g., DCI), higher layer signaling (e.g., RRC signaling, MAC CE), a specific signal / channel (e.g., PDCCH, PDSCH, reference signal), or a combination thereof.

[0392] When the notification is performed by a MAC CE, the MAC CE may be identified by including a new Logical Channel ID (LCID) in the MAC subheader, which is not defined in existing standards.

[0393] When the notification is made by DCI, the notification may be made by a specific field of the DCI, a Radio Network Temporary Identifier (RNTI) used to scramble Cyclic Redundancy Check (CRC) bits assigned to the DCI, the format of the DCI, etc.

[0394] Furthermore, notification of any information to the UE in the above embodiments may be performed periodically, semi-persistently, or aperiodically.

[0395] <<Notification of Information from UE>> In the above-described embodiments, notification of any information from the UE [to the NW] (in other words, transmission / report of any information from the UE to the BS) may be performed using physical layer signaling (e.g., UCI), higher layer signaling (e.g., RRC signaling, MAC CE), a specific signal / channel (e.g., PUCCH, PUSCH, PRACH, reference signal), or a combination thereof.

[0396] When the notification is performed by a MAC CE, the MAC CE may be identified by including a new LCID, which is not defined in existing standards, in the MAC subheader.

[0397] If the notification is made by UCI, the notification may be transmitted using PUCCH or PUSCH.

[0398] Furthermore, any information in the above-described embodiments may be notified from the UE periodically, semi-persistently, or aperiodically.

[0399] <<Regarding Application of Each Embodiment>> In a UE / BS, specific (one or more) processes / operations / controls / assumptions / information for at least one of the above-mentioned embodiments may be applied (used) when one or more of the following conditions are met: - a higher layer parameter indicating the specific processes / operations / controls / assumptions / information is configured; - the specific processes / operations / controls / assumptions / information is determined based on related higher layer parameters; - the specific processes / operations / controls / assumptions / information is specified / activated / triggered by a MAC CE / DCI / UCI / resource / channel / RS; - a specific UE capability indicating (or related to) the specific processes / operations / controls / assumptions / information is reported or supported; - the application of the specific processes / operations / controls / assumptions / information is determined based on specific conditions.

[0400] The particular UE capability may indicate that the particular process / action / control / assumption / information is supported.

[0401] Furthermore, the above-mentioned specific UE capability may be a capability that is applied across all frequencies (commonly regardless of frequency), or may be a capability for each frequency (e.g., one or a combination of a cell, a band, a band combination, a BWP, a component carrier, etc.), or may be a capability for each frequency range (e.g., Frequency Range 1 (FR1), FR2, FR3, FR4, FR5, FR2-1, FR2-2), or may be a capability for each subcarrier spacing (SubCarrier Spacing (SCS)), or may be a capability for each Feature Set (FS) or Feature Set Per Component-carrier (FSPC).

[0402] Furthermore, the specific UE capability may be a capability that is applied to all duplexing methods (commonly regardless of the duplexing method), or may be a capability for each duplexing method (e.g., Time Division Duplex (TDD) or Frequency Division Duplex (FDD)).

[0403] If the above conditions are not met, the UE / BS may follow the behavior specified in existing 3GPP releases.

[0404] (Supplementary Notes) The following inventions are supplementary notes regarding one embodiment of the present disclosure. [Supplementary Note 1] A terminal having a receiving unit that receives configuration information for data collection in artificial intelligence (AI)-based positioning, and a control unit that controls at least one of execution of inference, training, and exercises of a model and reporting on at least one of the inference, training, and exercises based on a parameter indicating an application included in the configuration information or a state of a model procedure. [Supplementary Note 2] The terminal described in Supplementary Note 1, wherein the control unit assumes that a specific parameter is not included in the configuration information if the parameter indicates a specific value. [Supplementary Note 3] The terminal described in Supplementary Note 1 or Supplementary Note 2, wherein the receiving unit receives an instruction regarding execution of post-processing on measurement results. [Supplementary Note 4] The terminal described in any of Supplements 1 to 3, wherein the control unit controls to report whether or not the reported positioning information is generated by an AI model.

[0405] (Wireless Communication System) The configuration of a wireless communication system according to an embodiment of the present disclosure will be described below. In this wireless communication system, communication is performed using any one of the wireless communication methods according to the above embodiments of the present disclosure or a combination thereof.

[0406] 18 is a diagram illustrating an example of a schematic configuration of a wireless communication system according to an embodiment. The wireless communication system 1 (which may be simply referred to as system 1) may be a system that realizes communication using Long Term Evolution (LTE) or 5th generation mobile communication system New Radio (5G NR) specified by the Third Generation Partnership Project (3GPP).

[0407] The wireless communication system 1 may also support dual connectivity between multiple Radio Access Technologies (RATs) (Multi-RAT Dual Connectivity (MR-DC)). MR-DC may 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.

[0408] In EN-DC, the LTE (E-UTRA) base station (eNB) is the master node (Master Node (MN)), and the NR base station (gNB) is the secondary node (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.

[0409] The wireless communication system 1 may support dual connectivity between multiple base stations within the same RAT (for example, dual connectivity in which both the MN and SN are NR base stations (gNBs) (NR-NR Dual Connectivity (NN-DC))).

[0410] The wireless communication system 1 may include a base station 11 that forms a macrocell C1 with a relatively wide coverage, and base stations 12 (12a-12c) that are located within the macrocell C1 and form small cells C2 that are smaller than the macrocell C1. A user terminal 20 may be located within at least one of the cells. The location, number, shape, size, etc. of each cell and user terminal 20 are not limited to the embodiment shown in the figure. Hereinafter, when there is no need to distinguish between the base stations 11 and 12, they will be collectively referred to as the base station 10.

[0411] The wireless communication system 1 may utilize multi-input multi-output (MIMO). For example, one cell may be formed by one antenna / base station 10, or may be formed by multiple antennas / base stations 10. One [virtual] cell (which may be called, for example, a supercell) may be composed of multiple [virtual] cells (which may be called, for example, subcells). A supercell may correspond to a cell with a fixed physical range, and a subcell may correspond to a cell with a quasi-static / dynamically variable physical range. In this case, the wireless communication system 1 may be called a cell-free system.

[0412] The user terminal 20 may be connected to at least one of the multiple base stations 10. The user terminal 20 may utilize at least one of carrier aggregation (CA) using multiple component carriers (CCs) and dual connectivity (DC).

[0413] Each CC may be included in at least one of a first frequency band (Frequency Range 1 (FR1)) and a second frequency band (Frequency Range 2 (FR2)). The macro cell C1 may be included in FR1, and the small cell C2 may be included in FR2. For example, FR1 may be a frequency band of 6 GHz or less (sub-6 GHz), and FR2 may be a frequency band higher than 24 GHz (above-24 GHz). Note that the frequency bands and definitions of FR1 and FR2 are not limited to these, and for example, FR1 may correspond to a higher frequency band than FR2.

[0414] Furthermore, the user terminal 20 may perform communication using at least one of time division duplex (TDD) and frequency division duplex (FDD) in each CC.

[0415] The multiple base stations 10 may be connected by wire (e.g., optical fiber compliant with the Common Public Radio Interface (CPRI), an X2 / Xn interface, etc.) or wirelessly (e.g., NR communication). For example, when NR communication is used as a backhaul between the base stations 11 and 12, the base station 11 corresponding to the upper station may be called an Integrated Access Backhaul (IAB) donor, and the base station 12 corresponding to the relay station (relay) may be called an IAB node.

[0416] The base station 10 may be connected to the core network 30 directly or via another base station 10. The core network 30 may include, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), a Next Generation Core (NGC), and the like.

[0417] The core network 30 may include network functions (Network Functions (NF)) such as a User Plane Function (UPF), an Access and Mobility management Function (AMF), a Session Management Function (SMF), a Unified Data Management (UDM), an Application Function (AF), a Data Network (DN), a Location Management Function (LMF), and Operation, Administration and Maintenance (Management) (OAM). A single network node may provide multiple functions. Communication with an external network (e.g., the Internet) may also be performed via the DN.

[0418] The user terminal 20 may be a terminal that supports at least one of communication methods such as LTE, LTE-A, and 5G.

[0419] An Orthogonal Frequency Division Multiplexing (OFDM)-based radio access scheme may be used in the wireless communication system 1. For example, Cyclic Prefix OFDM (CP-OFDM), Discrete Fourier Transform Spread OFDM (DFT-s-OFDM), Orthogonal Frequency Division Multiple Access (OFDMA), Single Carrier Frequency Division Multiple Access (SC-FDMA), or the like may be used in at least one of the downlink (DL) and uplink (UL).

[0420] The radio access scheme may also be called a waveform. Note that in the wireless communication system 1, other radio access schemes (e.g., other single-carrier transmission schemes, other multi-carrier transmission schemes) may be used as the UL and DL radio access schemes.

[0421] In the wireless communication system 1, a downlink shared channel (Physical Downlink Shared Channel (PDSCH)) shared by each user terminal 20, a broadcast channel (Physical Broadcast Channel (PBCH)), a downlink control channel (Physical Downlink Control Channel (PDCCH)), etc. may be used as the downlink channel.

[0422] Furthermore, in the wireless communication system 1, an uplink shared channel (Physical Uplink Shared Channel (PUSCH)) shared by each user terminal 20, an uplink control channel (Physical Uplink Control Channel (PUCCH)), a random access channel (Physical Random Access Channel (PRACH)), or the like may be used as an uplink channel.

[0423] The base station 10 may be separated into three elements: a radio unit (RU), a distributed unit (DU), and a central unit (CU). For example, the RU may implement RF processing (digital beamforming, digital-to-analog conversion, analog beamforming, etc.) and lower-level functions of the physical layer (precoding, IFFT, FFT, etc.). The DU may implement higher-level functions of the physical layer (coding to resource element mapping, etc.), MAC layer functions, and RLC layer functions. The CU may implement the functions of the PDCP layer, Service Data Adaptation Protocol (SDAP) layer, and RRC layer.

[0424] In the present disclosure, the base station 10 may include a single device that realizes all of the functions of the RU, DU, and CU, or may include multiple devices that each realize some of the functions of the RU, DU, and CU and are connected to each other. In the present disclosure, the base station 10 may be interchangeably read as RU / DU / CU.

[0425] The PDSCH transmits user data, higher layer control information, a System Information Block (SIB), etc. The PUSCH may transmit user data, higher layer control information, etc. Furthermore, the PBCH may transmit a Master Information Block (MIB).

[0426] Lower layer control information may be transmitted by the PDCCH. The lower layer control information may include, for example, Downlink Control Information (DCI) including scheduling information for at least one of the PDSCH and the PUSCH.

[0427] Note that the DCI for scheduling the PDSCH may be referred to as a DL assignment, a DL DCI, etc., and the DCI for scheduling the PUSCH may be referred to as a UL grant, a UL DCI, etc. Note that the PDSCH may be replaced with DL data, and the PUSCH may be replaced with UL data.

[0428] A control resource set (CORESET) and a search space may be used to detect the PDCCH. The CORESET corresponds to resources for searching for DCI. The search space corresponds to a search region and a search method for PDCCH candidates. One CORESET may be associated with one or more search spaces. The UE may monitor the CORESET associated with a certain search space based on the search space configuration.

[0429] One search space may correspond to PDCCH candidates corresponding to one or more aggregation levels. One or more search spaces may be referred to as a search space set. Note that the terms "search space," "search space set," "search space configuration," "search space set configuration," "CORESET," "CORESET configuration," and the like in the present disclosure may be read interchangeably.

[0430] The PUCCH may transmit uplink control information (UCI) including at least one of channel state information (CSI), delivery confirmation information (which may be called, for example, Hybrid Automatic Repeat reQuest ACKnowledgement (HARQ-ACK), ACK / NACK, etc.), and scheduling request (SR). The PRACH may transmit a random access preamble for establishing a connection with a cell.

[0431] In the present disclosure, downlink, uplink, etc. may be expressed without adding "link." Also, various channels may be expressed without adding "Physical" to the beginning.

[0432] In the wireless communication system 1, a synchronization signal (SS), a downlink reference signal (DL-RS), etc. may be transmitted. In the wireless communication system 1, as the DL-RS, a cell-specific reference signal (CRS), a channel state information reference signal (CSI-RS), a demodulation reference signal (DMRS), a positioning reference signal (PRS), a phase tracking reference signal (PTRS), etc. may be transmitted.

[0433] The synchronization signal may be, for example, at least one of a Primary Synchronization Signal (PSS) and a Secondary Synchronization Signal (SSS). A signal block including an SS (PSS, SSS) and a PBCH (and a DMRS for the PBCH) may be referred to as an SS / PBCH block, an SS Block (SSB), or the like. Note that the SS, SSB, and the like may also be referred to as a reference signal.

[0434] Furthermore, in the wireless communication system 1, a sounding reference signal (SRS), a demodulation reference signal (DMRS), or the like may be transmitted as an uplink reference signal (UL-RS). Note that the DMRS may also be called a user equipment-specific reference signal (UE-specific reference signal).

[0435] 19 is a diagram showing an example of the configuration of a base station according to an embodiment. The base station 10 includes a control unit 110, a transceiver unit 120, a transceiver antenna 130, and a transmission line interface 140. Note that the base station may include one or more of each of the control unit 110, the transceiver unit 120, the transceiver antenna 130, and the transmission line interface 140.

[0436] In this example, the functional blocks of the characteristic parts of the present embodiment are mainly shown, and it may be assumed that the base station 10 also has other functional blocks necessary for wireless communication. Some of the processing of each unit described below may be omitted.

[0437] The control unit 110 performs overall control of the base station 10. The control unit 110 can be configured from a controller, a control circuit, and the like that are explained based on common understanding in the technical field to which the present disclosure relates.

[0438] The control unit 110 may control signal generation, scheduling (e.g., resource allocation, mapping), etc. The control unit 110 may control transmission and reception using the transceiver unit 120, the transceiver antenna 130, and the transmission path interface 140, measurement, etc. The control unit 110 may generate data, control information, sequences, etc. to be transmitted as signals, and transfer them to the transceiver unit 120. The control unit 110 may perform call processing (setting up, releasing, etc.) of communication channels, status management of the base station 10, management of radio resources, etc.

[0439] The transceiver unit 120 may include a baseband unit 121, a radio frequency (RF) unit 122, and a measurement unit 123. The baseband unit 121 may include a transmission processing unit 1211 and a reception processing unit 1212. The transceiver unit 120 may be configured with a transmitter / receiver, an RF circuit, a baseband circuit, a filter, a phase shifter, a measurement circuit, a transceiver circuit, etc., which are described based on common understanding in the technical field related to the present disclosure.

[0440] The transmitting / receiving unit 120 may be configured as an integrated transmitting / receiving unit, or may be configured from a transmitting unit and a receiving unit. The transmitting unit may be configured from a transmission processing unit 1211 and an RF unit 122. The receiving unit may be configured from a reception processing unit 1212, the RF unit 122, and a measurement unit 123.

[0441] The transmitting and receiving antenna 130 can be configured from an antenna described based on common understanding in the technical field to which the present disclosure relates, such as an array antenna.

[0442] The transceiver 120 may transmit the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc. The transceiver 120 may receive the above-mentioned uplink channel, uplink reference signal, etc.

[0443] The transceiver 120 may form at least one of the transmit beam and the receive beam using digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), or the like.

[0444] The transmitter / receiver unit 120 (transmission processing unit 1211) may perform Packet Data Convergence Protocol (PDCP) layer processing, Radio Link Control (RLC) layer processing (e.g., RLC retransmission control), Medium Access Control (MAC) layer processing (e.g., HARQ retransmission control), etc. on data, control information, etc. obtained from the control unit 110, and generate a bit string to be transmitted.

[0445] The transmitter / receiver unit 120 (transmission processing unit 1211) may perform transmission processing such as channel coding (which may include error correction coding), modulation, mapping, filtering, Discrete Fourier Transform (DFT) processing (if necessary), Inverse Fast Fourier Transform (IFFT) processing, precoding, and digital-to-analog conversion on the bit string to be transmitted, and output a baseband signal.

[0446] The transceiver unit 120 (RF unit 122) may perform modulation, filtering, amplification, etc. on the baseband signal to a radio frequency band, and transmit the radio frequency band signal via the transceiver antenna 130.

[0447] On the other hand, the transceiver unit 120 (RF unit 122) may perform amplification, filtering, demodulation to a baseband signal, etc. on the radio frequency band signal received by the transceiver antenna 130.

[0448] The transceiver 120 (reception processing unit 1212) may apply reception processing such as analog-to-digital conversion, Fast Fourier Transform (FFT) processing, Inverse Discrete Fourier Transform (IDFT) processing (if necessary), filtering, demapping, demodulation, decoding (which may include error correction decoding), MAC layer processing, RLC layer processing, and PDCP layer processing to the acquired baseband signal, thereby acquiring user data, etc.

[0449] The transceiver 120 (measurement unit 123) may perform measurements on the received signal. For example, the measurement unit 123 may perform Radio Resource Management (RRM) measurements, Channel State Information (CSI) measurements, etc. based on the received signal. The measurement unit 123 may 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 may be output to the control unit 110.

[0450] The transmission path interface 140 may transmit and receive signals (backhaul signaling) between devices included in the core network 30 (e.g., network nodes that provide NF), other base stations 10, etc., and may acquire and transmit user data (user plane data), control plane data, etc. for the user terminal 20.

[0451] The transmitting section and receiving section of the base station 10 in the present disclosure may be configured by at least one of the transmitting / receiving section 120, the transmitting / receiving antenna 130, and the transmission path interface 140.

[0452] The base station 10 may be separated into three elements: a radio unit (RU), a distributed unit (DU), and a central unit (CU). For example, the RU may implement RF processing (digital beamforming, digital-to-analog conversion, analog beamforming, etc.) and lower-level functions of the physical layer (precoding, IFFT, FFT, etc.). The DU may implement higher-level functions of the physical layer (coding to resource element mapping, etc.), MAC layer functions, and RLC layer functions. The CU may implement the functions of the PDCP layer, Service Data Adaptation Protocol (SDAP) layer, and RRC layer.

[0453] In the present disclosure, the base station 10 may include a single device that realizes all of the functions of the RU, DU, and CU, or may include multiple devices that each realize some of the functions of the RU, DU, and CU and are connected to each other. In the present disclosure, the base station 10 may be interchangeably read as RU / DU / CU.

[0454] The transceiver 120 may transmit setting information for data collection in artificial intelligence (AI)-based positioning. The control unit 110 may use parameters indicating the application included in the setting information or the state of a model procedure to instruct at least one of performing inference, training, and exercises of the model and reporting on at least one of the inference, training, and exercises (first embodiment).

[0455] (User Terminal) Fig. 20 is a diagram showing an example of the configuration of a user terminal according to one embodiment. The user terminal 20 includes a control unit 210, a transceiver unit 220, and a transceiver antenna 230. Note that the user terminal 20 may include one or more of each of the control unit 210, the transceiver unit 220, and the transceiver antenna 230.

[0456] In this example, the functional blocks of the characteristic parts of the present embodiment are mainly shown, and it may be assumed that the user terminal 20 also has other functional blocks necessary for wireless communication. Some of the processing of each unit described below may be omitted.

[0457] The control unit 210 performs overall control of the user terminal 20. The control unit 210 can be configured from a controller, a control circuit, etc., which are described based on common understanding in the technical field to which the present disclosure relates.

[0458] The control unit 210 may control signal generation, mapping, etc. The control unit 210 may control transmission and reception, measurement, etc. using the transceiver unit 220 and the transceiver antenna 230. The control unit 210 may generate data, control information, sequences, etc. to be transmitted as signals and transfer them to the transceiver unit 220.

[0459] The transceiver unit 220 may include a baseband unit 221, an RF unit 222, and a measurement unit 223. The baseband unit 221 may include a transmission processing unit 2211 and a reception processing unit 2212. The transceiver unit 220 may be configured with a transmitter / receiver, an RF circuit, a baseband circuit, a filter, a phase shifter, a measurement circuit, a transceiver circuit, etc., which are described based on common understanding in the technical field related to the present disclosure.

[0460] The transmitting / receiving unit 220 may be configured as an integrated transmitting / receiving unit, or may be composed of a transmitting unit and a receiving unit. The transmitting unit may be composed of a transmission processing unit 2211 and an RF unit 222. The receiving unit may be composed of a reception processing unit 2212, an RF unit 222, and a measurement unit 223.

[0461] The transmitting / receiving antenna 230 can be configured from an antenna described based on common understanding in the technical field to which the present disclosure relates, such as an array antenna.

[0462] The transceiver 220 may receive the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc. The transceiver 220 may transmit the above-mentioned uplink channel, uplink reference signal, etc.

[0463] The transceiver unit 220 may form at least one of the transmit beam and the receive beam using digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), or the like.

[0464] The transceiver unit 220 (transmission processing unit 2211) may perform PDCP layer processing, RLC layer processing (e.g., RLC retransmission control), MAC layer processing (e.g., HARQ retransmission control), etc. on data, control information, etc. obtained from the control unit 210, and generate a bit string to be transmitted.

[0465] The transmitter / receiver unit 220 (transmission processing unit 2211) may perform transmission processing such as channel coding (which may include error correction coding), modulation, mapping, filtering, DFT processing (if necessary), IFFT processing, precoding, and digital-to-analog conversion on the bit string to be transmitted, and output a baseband signal.

[0466] Whether or not to apply DFT processing may be based on the setting of transform precoding. When transform precoding is enabled for a certain channel (e.g., PUSCH), the transceiver unit 220 (transmission processing unit 2211) may perform DFT processing as the transmission processing to transmit the channel using a DFT-s-OFDM waveform, and if not, it may not be necessary to perform DFT processing as the transmission processing.

[0467] The transceiver unit 220 (RF unit 222) may perform modulation, filtering, amplification, etc. on the baseband signal to a radio frequency band, and transmit the radio frequency band signal via the transceiver antenna 230.

[0468] On the other hand, the transceiver unit 220 (RF unit 222) may perform amplification, filtering, demodulation to a baseband signal, etc. on the radio frequency band signal received by the transceiver antenna 230.

[0469] The transceiver unit 220 (reception processing unit 2212) may apply reception processing such as analog-to-digital conversion, FFT processing, IDFT processing (if necessary), filtering, demapping, demodulation, decoding (which may include error correction decoding), MAC layer processing, RLC layer processing, and PDCP layer processing to the acquired baseband signal, and acquire user data, etc.

[0470] The transceiver 220 (measurement unit 223) may perform measurements on the received signal. For example, the measurement unit 223 may perform RRM measurements, CSI measurements, etc. based on the received signal. The measurement unit 223 may 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 may be output to the control unit 210.

[0471] The measurement unit 223 may derive channel measurements for CSI calculation based on the channel measurement resources. The channel measurement resources may be, for example, non-zero power (NZP) CSI-RS resources. The measurement unit 223 may also derive interference measurements for CSI calculation based on the interference measurement resources. The interference measurement resources may be at least one of an NZP CSI-RS resource for interference measurement, a CSI-Interference Measurement (IM) resource, etc. Note that CSI-IM may be referred to as CSI-Interference Management (IM) or may be interchangeably read as Zero Power (ZP) CSI-RS. Note that in the present disclosure, CSI-RS, NZP CSI-RS, ZP CSI-RS, CSI-IM, CSI-SSB, etc. may be interchangeably read as interchangeable.

[0472] Note that the transmitting unit and receiving unit of the user terminal 20 in the present disclosure may be configured by at least one of the transmitting / receiving unit 220 and the transmitting / receiving antenna 230.

[0473] The transceiver 220 may receive setting information for data collection in artificial intelligence (AI)-based positioning. The controller 210 may control at least one of execution of inference, training, and practice of the model and reporting on at least one of the inference, training, and practice, based on parameters indicating the application included in the setting information or the state of the model procedure (first embodiment).

[0474] If the parameter indicates a specific value, the control unit 210 may assume that the specific parameter is not included in the setting information (first embodiment).

[0475] The transceiver 220 may receive an instruction to perform post-processing on the measurement results (second embodiment).

[0476] The control unit 210 may also control the device to report whether the reported positioning information was generated by an AI model (third embodiment).

[0477] (Hardware Configuration) Note that the block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method for realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wires, wirelessly, etc.) and these multiple devices. The functional block may be realized by combining software with the single device or the multiple devices.

[0478] Here, the functions include, but are not limited to, judgment, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, election, establishment, comparison, assumption, expectation, deeming, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs transmission may be called a transmitting unit, transmitter, etc. As described above, the implementation method of each is not particularly limited.

[0479] For example, a base station, a user terminal, etc. according to an embodiment of the present disclosure may function as a computer that performs processing of the wireless communication method of the present disclosure. Fig. 21 is a diagram illustrating an example of the hardware configuration of a base station and a user terminal according to an embodiment. The above-described base station 10 and user terminal 20 may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, etc.

[0480] In the present disclosure, the terms apparatus, circuit, device, section, unit, etc. may be used interchangeably. The hardware configurations of the base station 10 and the user terminal 20 may be configured to include one or more of the devices shown in the drawings, or may be configured to exclude some of the devices.

[0481] For example, although only one processor 1001 is shown, there may be multiple processors. Furthermore, processing may be performed by one processor, or processing may be performed by two or more processors simultaneously, serially, or in other ways. Furthermore, processor 1001 may be implemented by one or more chips.

[0482] Each function in the base station 10 and the user terminal 20 is realized, for example, by loading specified software (programs) onto hardware such as a processor 1001 and a memory 1002, causing the processor 1001 to perform calculations, control communication via the communication device 1004, and control at least one of reading and writing data in the memory 1002 and the storage 1003.

[0483] The processor 1001, for example, runs an operating system to control the entire computer. The processor 1001 may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. For example, at least a part of the above-mentioned control unit 110 (210), transceiver unit 120 (220), etc. may be realized by the processor 1001.

[0484] The processor 1001 also reads programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002 and executes various processes in accordance with these. The programs used are those that cause a computer to execute at least some of the operations described in the above-described embodiments. For example, the control unit 110 (210) may be implemented by a control program stored in the memory 1002 and running on the processor 1001, and the other functional blocks may be implemented in a similar manner.

[0485] The memory 1002 is a computer-readable recording medium and may be configured by at least one of, for example, Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically EEPROM (EEPROM), Random Access Memory (RAM), or other suitable storage medium. The memory 1002 may also be referred to as a register, cache, main memory, etc. The memory 1002 may store executable programs (program codes), software modules, etc. for implementing a wireless communication method according to an embodiment of the present disclosure.

[0486] Storage 1003 is a computer-readable recording medium and may be composed of at least one of, for example, a flexible disk, a floppy disk, a magneto-optical disk (e.g., a compact disc (e.g., a Compact Disc ROM (CD-ROM)), a digital versatile disc, a Blu-ray disc), a removable disk, a hard disk drive, a smart card, a flash memory device (e.g., a card, a stick, a key drive), a magnetic stripe, a database, a server, or other suitable storage medium. Storage 1003 may also be referred to as an auxiliary storage device.

[0487] The communication device 1004 is hardware (transmission / reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as, for example, a network device, a network controller, a network card, or a communication module. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc. to realize at least one of frequency division duplex (FDD) and time division duplex (TDD). For example, the above-mentioned transmission / reception unit 120 (220), transmission / reception antenna 130 (230), etc. may be realized by the communication device 1004. The transmission / reception unit 120 (220) may be implemented as a transmission unit 120a (220a) and a reception unit 120b (220b) that are physically or logically separated.

[0488] The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that accepts input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, a light emitting diode (LED) lamp, etc.) that outputs to the outside. Note that the input device 1005 and the output device 1006 may be integrated into one device (e.g., a touch panel).

[0489] Furthermore, each device, such as the processor 1001 and the memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

[0490] Furthermore, the base station 10 and the user terminal 20 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized using this hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

[0491] In addition, the devices included in the core network 30 (for example, network nodes that provide NF) may also be realized by the above-mentioned functional block / hardware configuration.

[0492] (Modifications) Note that terms described in the present disclosure and terms necessary for understanding the present disclosure may be replaced with terms having the same or similar meanings. For example, a channel, a symbol, and a signal (signal or signaling) may be interchangeable. A signal may also be a message. A reference signal may be abbreviated as RS, and may also be called a pilot, pilot signal, etc. depending on the applicable standard. A component carrier (CC) may also be called a cell, frequency carrier, carrier frequency, etc.

[0493] A radio frame may be composed of one or more periods (frames) in the time domain. Each of the one or more periods (frames) constituting a radio frame may be called a subframe. Furthermore, a subframe may be composed of one or more slots in the time domain. A subframe may have a fixed time length (e.g., 1 ms) that is independent of numerology.

[0494] Here, the numerology may be a communication parameter applied to at least one of transmission and reception of a signal or channel, and may indicate at least one of, for example, Subcarrier Spacing (SCS), bandwidth, symbol length, cyclic prefix length, Transmission Time Interval (TTI), number of symbols per TTI, radio frame structure, specific filtering performed by a transceiver in the frequency domain, and specific windowing performed by a transceiver in the time domain.

[0495] A slot may be composed of one or more symbols (such as an Orthogonal Frequency Division Multiplexing (OFDM) symbol or a Single Carrier Frequency Division Multiple Access (SC-FDMA) symbol) in the time domain. A slot may also be a time unit based on numerology.

[0496] A slot may include multiple minislots. Each minislot may consist of one or multiple symbols in the time domain. A minislot may also be called a subslot. A minislot may consist of fewer symbols than a slot. A PDSCH (or PUSCH) transmitted in a time unit larger than a minislot may be called PDSCH (PUSCH) mapping type A. A PDSCH (or PUSCH) transmitted using a minislot may be called PDSCH (PUSCH) mapping type B.

[0497] A radio frame, a subframe, a slot, a minislot, and a symbol all represent time units for transmitting signals. The radio frame, the subframe, the slot, the minislot, and the symbol may be referred to by other names corresponding to the radio frame, the subframe, the slot, the minislot, and the symbol. Note that the time units such as a frame, a subframe, a slot, a minislot, and a symbol in the present disclosure may be interchangeable.

[0498] For example, one subframe may be referred to as a TTI, or multiple consecutive subframes may be referred to as a TTI, or one slot or one minislot may be referred to as a TTI. That is, at least one of the subframe and the TTI may be a subframe (1 ms) in existing LTE, a period shorter than 1 ms (for example, 1-13 symbols), or a period longer than 1 ms. Note that the unit representing the TTI may be called a slot, minislot, etc. instead of a subframe.

[0499] Here, TTI refers to, for example, the smallest time unit for scheduling in wireless communication. For example, in an LTE system, a base station performs scheduling to allocate radio resources (such as frequency bandwidth and transmission power that can be used by each user terminal) to each user terminal in TTI units. Note that the definition of TTI is not limited to this.

[0500] The TTI may be a transmission time unit for a channel-encoded data packet (transport block), a code block, a code word, etc., or may be a processing unit for scheduling, link adaptation, etc. When a TTI is given, the time interval (e.g., the number of symbols) to which a transport block, a code block, a code word, etc. is actually mapped may be shorter than the TTI.

[0501] When one slot or one minislot is called a TTI, one or more TTIs (i.e., one or more slots or one or more minislots) may be the minimum time unit for scheduling. Also, the number of slots (minislots) constituting the minimum time unit for scheduling may be controlled.

[0502] A TTI having a time length of 1 ms may be called a regular TTI (TTI in 3GPP Rel. 8-12), normal TTI, long TTI, regular subframe, normal subframe, long subframe, slot, etc. A TTI shorter than a regular TTI may be called a shortened TTI, short TTI, partial or fractional TTI, shortened subframe, short subframe, minislot, subslot, slot, etc.

[0503] In addition, a long TTI (e.g., a normal TTI, a subframe, etc.) may be interpreted as a TTI having a time length of more than 1 ms, and a short TTI (e.g., a shortened TTI, etc.) may be interpreted as a TTI having a TTI length shorter than the TTI length of a long TTI and greater than or equal to 1 ms.

[0504] A resource block (RB) is a resource allocation unit in the time domain and the frequency domain, and may include one or more consecutive subcarriers in the frequency domain. The number of subcarriers included in an RB may be the same regardless of numerology, for example, 12. The number of subcarriers included in an RB may be determined based on numerology.

[0505] In addition, an RB may include one or more symbols in the time domain and may have a length of one slot, one minislot, one subframe, or one TTI, each of which may be composed of one or more resource blocks.

[0506] In addition, one or more RBs may be referred to as a physical resource block (PRB), a sub-carrier group (SCG), a resource element group (REG), a PRB pair, an RB pair, etc.

[0507] Furthermore, a resource block may be composed of one or more resource elements (REs). For example, one RE may be a radio resource region of one subcarrier and one symbol.

[0508] A Bandwidth Part (BWP), which may also be referred to as a partial bandwidth, may represent a subset of contiguous common resource blocks (RBs) for a given numerology on a given carrier, where the common RBs may be identified by their index relative to a Common Reference Point of the carrier. PRBs may be defined in a BWP and numbered within the BWP.

[0509] The BWP may include a UL BWP (BWP for UL) and a DL BWP (BWP for DL). One or more BWPs may be configured for a UE within one carrier.

[0510] At least one of the configured BWPs may be active, and the UE may not expect to transmit or receive a given signal / channel outside the active BWP. Note that the terms "cell," "carrier," etc. in this disclosure may be read as "BWP."

[0511] The above-described structures of radio frames, subframes, slots, minislots, symbols, etc. are merely examples. For example, the number of subframes included in a radio frame, the number of slots per subframe or radio frame, the number of minislots included in a slot, the number of symbols and RBs included in a slot or minislot, the number of subcarriers included in an RB, the number of symbols in a TTI, the symbol length, the cyclic prefix (CP) length, etc. may be changed in various ways.

[0512] Furthermore, the information, parameters, etc. described in the present disclosure may be expressed using absolute values, may be expressed using relative values ​​from a predetermined value, or may be expressed using other corresponding information. For example, a radio resource may be indicated by a predetermined index.

[0513] The names used for parameters and the like in this disclosure are not intended to be limiting in any way. Furthermore, the mathematical expressions and the like using these parameters may differ from those explicitly disclosed in this disclosure. The various channels (PUCCH, PDCCH, etc.) and information elements may be identified by any suitable names, and therefore the various names assigned to these various channels and information elements are not intended to be limiting in any way.

[0514] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0515] Furthermore, information, signals, etc. may be output from a higher layer to a lower layer and / or from a lower layer to a higher layer. Information, signals, etc. may be input / output via multiple network nodes.

[0516] Input and output information, signals, etc. may be stored in a specific location (for example, memory) or may be managed using a management table. Input and output information, signals, etc. may be overwritten, updated, or added. Output information, signals, etc. may be deleted. Input information, signals, etc. may be transmitted to another device.

[0517] With respect to any information (e.g., variables, constants, parameters) described in the present disclosure, even if not specifically stated in the above embodiments, any first device (e.g., UE / base station) may notify any second device (e.g., base station / UE) of information indicating / specifying (or relating to) the value of the any information.

[0518] The notification of information is not limited to the aspects / embodiments described in the present disclosure, and may be performed using other methods. For example, the notification of information in the present disclosure may be performed by physical layer signaling (e.g., Downlink Control Information (DCI) and Uplink Control Information (UCI)), higher layer signaling (e.g., Radio Resource Control (RRC) signaling, broadcast information (Master Information Block (MIB) and System Information Block (SIB)), Medium Access Control (MAC) signaling), other signals, or a combination thereof.

[0519] Note that the physical layer signaling may be referred to as Layer 1 / Layer 2 (L1 / L2) control information (L1 / L2 control signal), L1 control information (L1 control signal), etc. Furthermore, the RRC signaling may be referred to as an RRC message, such as an RRC Connection Setup message or an RRC Connection Reconfiguration message. Furthermore, the MAC signaling may be notified using, for example, a MAC Control Element (CE).

[0520] Furthermore, notification of specified information (e.g., notification that "it is X") is not limited to explicit notification, but may be made implicitly (e.g., by not notifying the specified information or by notifying other information).

[0521] The determination may be made by a value represented by one bit (0 or 1), by a Boolean value represented by true or false, or by a comparison of numerical values ​​(e.g., comparison with a predetermined value).

[0522] Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0523] Software, instructions, information, etc. may also be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), these wired and / or wireless technologies are included within the definition of transmission media.

[0524] As used in this disclosure, the terms "system" and "network" may be used interchangeably. A "network" may refer to devices included in the network (e.g., base stations).

[0525] In this disclosure, terms such as "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," "beam width," "beam angle," "antenna," "antenna element," "panel," "UE panel," "transmitting entity," "receiving entity," etc. may be used interchangeably.

[0526] In the present disclosure, the term "antenna port" may be interchangeably read as an antenna port for any signal / channel (e.g., a demodulation reference signal (DMRS) port). In the present disclosure, the term "resource" may be interchangeably read as a resource for any signal / channel (e.g., a reference signal resource, an SRS resource, etc.). The resource may include time / frequency / code / space / power resources. Furthermore, the spatial domain transmission filter may include at least one of a spatial domain transmission filter and a spatial domain reception filter.

[0527] The group may include, for example, at least one of a spatial relationship group, a Code Division Multiplexing (CDM) group, a Reference Signal (RS) group, a Control Resource Set (CORESET) group, a PUCCH group, an antenna port group (e.g., a DMRS port group), a layer group, a resource group, a beam group, an antenna group, a panel group, and the like.

[0528] In addition, in the present disclosure, beam, SRS Resource Indicator (SRI), CORESET, CORESET pool, PDSCH, PUSCH, codeword (CW), transport block (TB), RS, etc. may be read as interchangeable terms.

[0529] In addition, in the present disclosure, the terms TCI state, downlink TCI state (DL TCI state), uplink TCI state (UL TCI state), unified TCI state, common TCI state, joint TCI state, etc. may be read interchangeably.

[0530] Furthermore, in the present disclosure, terms such as "QCL," "QCL assumption," "QCL relationship," "QCL type information," "QCL property / properties," "specific QCL type (e.g., Type A, Type D) property," and "specific QCL type (e.g., Type A, Type D)" may be interchangeable.

[0531] In the present disclosure, terms such as index, identifier (ID), indicator, indication, and resource ID may be interchangeable. In the present disclosure, terms such as sequence, list, set, group, cluster, and subset may be interchangeable.

[0532] Furthermore, the spatial relationship information identifier (ID) (TCI state ID) and the spatial relationship information (TCI state) may be interchangeable. The "spatial relationship information (TCI state)" may be interchangeable with "set of spatial relationship information (TCI state)", "one or more pieces of spatial relationship information", etc. The TCI state and the TCI may be interchangeable. The spatial relationship information and the spatial relationship may be interchangeable.

[0533] In the present disclosure, terms such as "base station (BS)," "radio 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," "component carrier," etc. may be used interchangeably. Base stations may also be referred to by terms such as macrocell, small cell, femtocell, picocell, etc.

[0534] A base station can accommodate one or more (e.g., three) cells. When a base station accommodates multiple cells, the overall coverage area of ​​the base station can be partitioned into multiple smaller areas, and each smaller area can be provided with communication service by a base station subsystem (e.g., a small indoor base station (Remote Radio Head (RRH))). The terms "cell" or "sector" refer to part or all of the coverage area of ​​a base station and / or base station subsystem that provides communication service within that coverage.

[0535] In the present disclosure, a base station transmitting information to a terminal may be interpreted as the base station instructing the terminal to control / operate based on the information.

[0536] In this disclosure, the terms "Mobile Station (MS)," "user terminal," "User Equipment (UE)," "terminal," etc. may be used interchangeably.

[0537] A mobile station may also be referred to as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable terminology.

[0538] At least one of the base station and the mobile station may be called a transmitting device, a receiving device, a wireless communication device, etc. Note that at least one of the base station and the mobile station may be a device mounted on a moving object, the moving object itself, etc.

[0539] The mobile body is a movable object that can move at any speed and naturally includes cases where the mobile body is stationary. Examples of the mobile body include, but are not limited to, vehicles, transport vehicles, automobiles, motorcycles, bicycles, connected cars, excavators, bulldozers, wheel loaders, dump trucks, forklifts, trains, buses, handcars, rickshaws, ships and other watercraft, airplanes, rockets, satellites, drones, multicopters, quadcopters, balloons, and objects mounted thereon. The mobile body may also be a mobile body that moves autonomously based on an operation command.

[0540] The mobile object may be a vehicle (e.g., a car, an airplane, etc.), an unmanned mobile object (e.g., a drone, an autonomous vehicle, etc.), or a robot (manned or unmanned). Note that at least one of the base station and the mobile station may also include devices that do not necessarily move during communication operations. For example, at least one of the base station and the mobile station may be an Internet of Things (IoT) device such as a sensor.

[0541] 22 is a diagram showing an example of a vehicle according to an 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, axles 48, an electronic control unit 49, various sensors (including a current sensor 50, an RPM sensor 51, an air 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.

[0542] The drive unit 41 is configured with at least one of an engine, a motor, and a hybrid of an engine and a motor, for example. The steering unit 42 includes at least a steering wheel (also called a handle) and is configured to steer at least one of the front wheels 46 and the rear wheels 47 based on the operation of the steering wheel operated by a user.

[0543] The electronic control unit 49 is composed of a microprocessor 61, memory (ROM, RAM) 62, and a communication port (for example, an input / output (IO) port) 63. Signals are input to the electronic control unit 49 from various sensors 50-58 provided in the vehicle. The electronic control unit 49 may also be called an Electronic Control Unit (ECU).

[0544] The signals from the various sensors 50-58 include a current signal from a current sensor 50 that senses the current of the motor, a rotation speed signal of the front wheels 46 / rear wheels 47 obtained by a rotation speed sensor 51, an air pressure signal of the front wheels 46 / rear wheels 47 obtained by an air pressure sensor 52, a vehicle speed signal obtained by a vehicle speed sensor 53, an acceleration signal obtained by an acceleration sensor 54, a depression amount signal of the accelerator pedal 43 obtained by an accelerator pedal sensor 55, a depression amount signal of the brake pedal 44 obtained by a brake pedal sensor 56, an operation signal of the shift lever 45 obtained by a shift lever sensor 57, and a detection signal for detecting obstacles, vehicles, pedestrians, etc. obtained by an object detection sensor 58.

[0545] The information service unit 59 is composed of various devices, such as a car navigation system, an audio system, speakers, a display, a television, and a radio, for providing (outputting) various information such as driving information, traffic information, and entertainment information, and one or more ECUs for controlling these devices. The information service unit 59 uses information acquired from external devices via the communication module 60 or the like to provide various information / services (e.g., multimedia information / multimedia services) to the occupants of the vehicle 40.

[0546] The information service unit 59 may include input devices (e.g., keyboards, mice, microphones, switches, buttons, sensors, touch panels, etc.) that accept input from the outside, and may also include output devices (e.g., displays, speakers, LED lamps, touch panels, etc.) that output to the outside.

[0547] The driving assistance system unit 64 includes various devices for providing functions to prevent accidents and reduce the driver's driving burden, such as millimeter-wave radar, Light Detection and Ranging (LiDAR), cameras, positioning locators (e.g., Global Navigation Satellite System (GNSS)), map information (e.g., High Definition (HD) maps, Autonomous Vehicle (AV) maps), gyro systems (e.g., Inertial Measurement Units (IMUs), Inertial Navigation Systems (INSs)), artificial intelligence (AI) chips, and AI processors, as well as one or more ECUs that control these devices. The driving assistance system unit 64 also transmits and receives various information via the communication module 60 to realize driving assistance functions or autonomous driving functions.

[0548] The communication module 60 can communicate with the microprocessor 61 and components of the vehicle 40 via the communication port 63. For example, the communication module 60 transmits and receives data (information) via the communication port 63 to and from the drive unit 41, steering unit 42, accelerator pedal 43, brake pedal 44, shift lever 45, left and right front wheels 46, left and right rear wheels 47, axles 48, the microprocessor 61 and memory (ROM, RAM) 62 in the electronic control unit 49, and the various sensors 50-58, which are provided in the vehicle 40.

[0549] The communication module 60 is a communication device that can be controlled by the microprocessor 61 of the electronic control unit 49 and can communicate with an external device. For example, it transmits and receives various information to and from the external device via wireless communication. The communication module 60 may be located either inside or outside the electronic control unit 49. The external device may be, for example, the base station 10 or the user terminal 20 described above. Furthermore, the communication module 60 may be, for example, at least one of the base station 10 and the user terminal 20 described above (or may function as at least one of the base station 10 and the user terminal 20).

[0550] The communication module 60 may transmit at least one of signals from the above-mentioned various sensors 50-58 input to the electronic control unit 49, information obtained based on the signals, and information based on input from the outside (user) obtained via the information service unit 59 to an external device via wireless communication. The electronic control unit 49, the various sensors 50-58, the information service unit 59, etc. may be referred to as input units that accept input. For example, the PUSCH transmitted by the communication module 60 may include information based on the above-mentioned input.

[0551] The communication module 60 receives various information (traffic information, traffic signal information, vehicle distance information, etc.) transmitted from an external device and displays it on an information service unit 59 provided in the vehicle. The information service unit 59 may also be called an output unit that outputs information (for example, outputs information to a device such as a display or speaker based on the PDSCH received by the communication module 60 (or data / information decoded from the PDSCH)).

[0552] Furthermore, the communication module 60 stores various information received from external devices in a memory 62 that can be used by the microprocessor 61. Based on the information stored in the memory 62, the microprocessor 61 may control the drive unit 41, steering unit 42, accelerator pedal 43, brake pedal 44, shift lever 45, left and right front wheels 46, left and right rear wheels 47, axles 48, various sensors 50-58, and the like provided in the vehicle 40.

[0553] Furthermore, a base station in the present disclosure may be read as a user terminal. For example, the aspects / embodiments of the present disclosure may be applied to a configuration in which communication between a base station and a user terminal is replaced with communication between multiple user terminals (which may be called, for example, Device-to-Device (D2D) or Vehicle-to-Everything (V2X)). In this case, the user terminal 20 may be configured to have the functions of the base station 10 described above. Furthermore, terms such as "uplink" and "downlink" may be read as terms corresponding to terminal-to-terminal communication (for example, "sidelink"). For example, terms such as an uplink channel and a downlink channel may be read as a sidelink channel.

[0554] Similarly, the user terminal in the present disclosure may be read as a base station, in which case the base station 10 may be configured to have the functions of the user terminal 20 described above.

[0555] In the present disclosure, an operation described as being performed by a base station may be performed by its upper node in some cases. It is apparent that in a network including one or more network nodes having a base station, various operations performed for communication with a terminal may be performed by the base station, one or more network nodes other than the base station (such as, but not limited to, a Mobility Management Entity (MME), a Serving-Gateway (S-GW), etc.), or a combination thereof.

[0556] Each aspect / embodiment described in this disclosure may be used alone, in combination, or switched depending on the implementation. Furthermore, the order of the processing procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed unless inconsistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0557] Each aspect / embodiment described in the present disclosure may be a technology other than 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 (x is, for example, an integer or decimal number)), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New radio access (NX), Future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.17 (WiMAX (registered trademark)), IEEE 802.19 (WiMAX (registered trademark)), IEEE 802.20 (WiMAX (registered trademark)), IEEE 802.21 (Wi-Fi (registered trademark)), IEEE 802.22 (WiMAX (registered trademark)), IEEE 802.23 (WiMAX (registered trademark)), IEEE 802.24 (WiMAX (registered trademark)), IEEE 802.25 (WiMAX (registered trademark)), IEEE 802.26 (WiMAX (registered trademark)), IEEE 802.27 (WiMAX (registered trademark)), IEEE 802.28 (WiMAX (registered trademark)), IEEE 802.29 (WiMAX (registered trademark)), IEEE 802.30 (WiMAX (registered trademark)), IEEE 802.31 (Wi-Fi (registered trademark)), IEEE 802.32 (WiMAX (registered trademark)), IEEE 802.33 (WiMAX (registered trademark)), IEEE 802. The present invention may be applied to systems that use IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), or other suitable wireless communication methods, or to next-generation systems that are expanded, modified, created, or defined based on these. Furthermore, the present invention may be applied to a combination of multiple systems (e.g., a combination of LTE or LTE-A and 5G).

[0558] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0559] As used in this disclosure, any reference to an element using a designation such as "first," "second," etc. does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must in some way precede the second element.

[0560] The term "determining" as used in this disclosure may encompass a wide variety of actions. For example, "determining" may be considered to be judging, calculating, computing, processing, deriving, investigating, looking up, search, inquiry (e.g., looking up in a table, database, or another data structure), ascertaining, etc.

[0561] Additionally, "determining" may be considered to be "determining" receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), etc.

[0562] Furthermore, "determination" may be considered to be "determining" resolving, selecting, choosing, establishing, comparing, etc. In other words, "determination" may be considered to be "determining" some kind of action. In the present disclosure, "determination" may be read interchangeably with the above-mentioned actions.

[0563] Furthermore, in this disclosure, "determine / determining" may be interchangeably read as "assume / assuming," "expect / expecting," "consider / considering," etc. Furthermore, in this disclosure, "does not expect to do..." may be interchangeably read as "assumes not to do...."

[0564] In the present disclosure, "expect" may be interchangeably read as "be expected." For example, "expect(s) ..." ("..." may be expressed, for example, as a that clause, a to-infinitive, etc.) may be interchangeably read as "be expected ..." or "do ... (if the above "..." is a to-infinitive, a verb with "to")," etc. "does not expect ..." may be interchangeably read as "be not expected ..." or "does not ... (if the above "..." is a to-infinitive, a verb with "to")," etc. Furthermore, "An apparatus A is not expected ..." may be interchangeably read as "an apparatus B other than apparatus A does not expect ... from apparatus A" (for example, if apparatus A is a UE, apparatus B may be a base station).

[0565] The "maximum transmit power" in this disclosure may mean the maximum value of transmit power, the nominal UE maximum transmit power, or the rated UE maximum transmit power.

[0566] As used in this disclosure, the terms "connected," "coupled," or any variation thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access."

[0567] In this disclosure, when two elements are connected, they may be considered to be "connected" or "coupled" to one another using one or more wires, cables, printed electrical connections, etc., as well as using electromagnetic energy having wavelengths in the radio frequency range, microwave range, light (both visible and invisible) range, etc., as some non-limiting and non-exhaustive examples.

[0568] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different."

[0569] When the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Furthermore, when the term "or" is used in this disclosure, it is not intended to be an exclusive or.

[0570] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0571] In the present disclosure, terms such as "less than or equal to," "less than," "greater than," "more than," "equal to," etc. may be interchangeable. Furthermore, in the present disclosure, terms meaning "good," "bad," "big," "small," "high," "low," "fast," "slow," "wide," "narrow," etc. may be interchangeable, not limited to the positive, comparative, and superlative. Furthermore, in the present disclosure, terms meaning "good," "bad," "big," "small," "high," "low," "fast," "slow," "wide," "narrow," etc. may be interchangeable, not limited to the positive, comparative, and superlative, as expressions with "i-th" (i is an arbitrary integer) attached (for example, "highest" may be interchangeable with "i-th highest").

[0572] In this disclosure, the terms "of," "for," "regarding," "related to," "associated with," etc. may be read interchangeably.

[0573] In the present disclosure, terms 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" may be interchangeable. Note that A, B, and the like herein may be replaced with appropriate expressions such as nouns, gerunds, and regular sentences, depending on the context. Note that the time difference between A and B may be approximately zero (immediately after or immediately before). A time offset may also be applied to the time at which A occurs. For example, "A" may be interchangeable with "before / after a time offset at which A occurs." The time offset (eg, one or more symbols / slots) may be predefined or may be specified by the UE based on signaled information.

[0574] In the present disclosure, timing, time, duration, time instance, any time unit (e.g., slot, subslot, symbol, subframe), period, occasion, resource, etc. may be read interchangeably.

[0575] Although the invention according to the present disclosure has been described in detail above, it is clear to those skilled in the art that the invention according to the present disclosure is not limited to the embodiments described in the present disclosure. The description of the present disclosure is for illustrative purposes only and does not impose any limiting meaning on the invention according to the present disclosure.

[0576] This application is based on Japanese Patent Application No. 2024-084237, filed May 23, 2024, the contents of which are incorporated herein in their entirety.

Claims

1. A terminal having a receiving unit that receives configuration information for data collection in artificial intelligence (AI)-based positioning; and a control unit that controls at least one of execution of inference, training, and exercises of a model, and reporting on at least one of the inference, training, and exercises, based on parameters indicating use included in the configuration information or the state of a model procedure.

2. The terminal according to claim 1, wherein the control unit assumes that the specific parameter is not included in the setting information if the parameter indicates a specific value.

3. The terminal according to claim 1, wherein the receiving unit receives instructions regarding the execution of post-processing on the measurement results.

4. The terminal according to claim 1, wherein the control unit controls to report whether the reported positioning information is generated by an AI model.

5. A wireless communication method for a terminal, comprising: receiving configuration information for data collection in artificial intelligence (AI)-based positioning; and controlling at least one of execution of inference, training, and exercises of a model, and reporting on at least one of the inference, training, and exercises, based on parameters indicating use included in the configuration information or the state of a model procedure.

6. A base station having a transmitter that transmits configuration information for data collection in artificial intelligence (AI)-based positioning; and a controller that uses parameters indicating use or a model procedure status included in the configuration information to instruct at least one of performing inference, training, and exercises of a model, and reporting on at least one of the inference, training, and exercises.

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