Terminal, wireless communication method, and base station

Abstract

A terminal according to one embodiment of the present disclosure is characterized by being provided with: a receiving unit for receiving a performance index for performance monitoring with respect to positioning by artificial intelligence (AI); and a control unit that controls the performance monitoring, the control unit determining whether a specific operation after the performance monitoring can be executed. According to one mode of the present disclosure, appropriate overhead reduction / channel estimation / resource utilization can be realized.

Description

Technical Field

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

[0002] In the Universal Mobile Telecommunications System (UMTS) network, Long Term Evolution (LTE) has been standardized for the purpose of further high-speed data rates and low latency (Non-Patent Document 1). In addition, LTE-Advanced (3GPP Rel. 10-14) has been standardized for the purpose of further increasing the capacity and advancement of LTE (Release (Rel.) 8 and 9 of the Third Generation Partnership Project (3GPP (registered trademark))).

[0003] Successor systems of LTE (also called, for example, fifth generation mobile communication system (5G), 5G+(plus), sixth generation mobile communication system (6G), New Radio (NR), 3GPP Rel.15 and later, etc.) are also being studied.

[0004] Prior art literature

[0005] Non-patent literature

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

[0007] Problems to be solved by the invention

[0008] Regarding future wireless communication technologies, research is underway to utilize artificial intelligence (AI) technologies such as machine learning (ML) for network / device control and management. For example, research is underway to utilize AI technologies to determine (estimate) the location (positioning) of user terminals (user equipment (UE)).

[0009] In addition, research is underway on performance monitoring of AI models (model monitoring). Performance monitoring of AI models can be performed in both terminals (terminal, user terminal, user equipment (UE)) and base stations (Base Station (BS)). However, research has not yet progressed on the specific lifecycle management of performance monitoring in UE / BS for positioning using AI models.

[0010] If the method for implementing performance monitoring is not appropriately defined, appropriate overhead reduction / high-precision channel estimation / efficient resource utilization cannot be achieved, and there is a possibility that improvements in communication throughput / communication quality may be suppressed.

[0011] 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 appropriate overhead reduction / channel estimation / resource utilization.

[0012] Means for solving problems

[0013] A terminal involved in one method of the present disclosure is characterized in that it has: a receiving unit that receives performance indicators for performance monitoring regarding positioning based on artificial intelligence (AI); and a control unit that controls the performance monitoring, and the control unit determines whether a specific operation after the performance monitoring can be performed.

[0014] Effects of the Invention

[0015] According to one aspect of the present disclosure, appropriate overhead reduction / channel estimation / resource utilization can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0017] Figure 2 This is a diagram showing an example of specifying an AI model.

[0018] Figure 3 This is a diagram showing an example of a UE positioning method.

[0019] Figure 4 This is a diagram showing an example of a UE positioning method.

[0020] Figure 5 This is a diagram showing an example of a UE positioning method.

[0021] Figure 6 This is a diagram showing an example of a UE positioning method.

[0022] Figure 7 This is a diagram showing an example of the start time and end time of delay (Latency) according to the first embodiment.

[0023] Figure 8 This is a diagram showing an example of a schematic configuration of a wireless communication system according to an embodiment.

[0024] Fig. 9 This is a diagram showing an example of the configuration of a base station according to an embodiment.

[0025] Fig.10 This is a diagram showing an example of a configuration of a user terminal according to an embodiment.

[0026] Fig.11 This is a diagram showing an example of the hardware configuration of a base station and a user terminal involved in one embodiment.

[0027] Fig.12 This is a diagram showing an example of a vehicle according to an embodiment. DETAILED DESCRIPTION

[0028] (Application of Artificial Intelligence (AI) technology to wireless communications)

[0029] Regarding future wireless communication technologies, research is underway into the use of AI technologies such as machine learning (ML) for control and management of networks and devices.

[0030] For example, the use of AI technology in terminals (terminal, user terminal, User Equipment (UE)) / base stations (Base Station (BS)) is being studied 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 domain / spatial domain), and improve position measurement (e.g., improved position estimation / prediction).

[0031] The AI ​​model may also output at least one information such as an estimated value, a predicted value, a selected operation, a classification, etc. based on the input information. The UE / BS may also input channel state information, reference signal measurement values, etc. into the AI ​​model, and output high-precision channel state information / measurement values / beam selection / position, future channel state information / radio link quality, etc.

[0032] In addition, in the present disclosure, AI can also be rewritten as an object (also referred to as an object, object, data, function, program, etc.) having (implementing) at least one of the following characteristics:

[0033] estimates based on observed or collected information,

[0034] · Selection based on observed or collected information,

[0035] Predictions based on observed or collected information.

[0036] In the present disclosure, estimation, prediction, and inference may be replaced with each other. In addition, in the present disclosure, estimation, prediction, and inference may be replaced with each other.

[0037] In the present disclosure, an object may also be, for example, a device, such as a UE or a BS, etc. In addition, in the present disclosure, an object may also correspond to a program / model / entity operating in the device.

[0038] In addition, in the present disclosure, the AI ​​model may also be rewritten as an object having (implementing) at least one of the following features:

[0039] Generate estimates by feeding information,

[0040] By providing information, forecasting estimates,

[0041] ·By providing information, discovering features,

[0042] · Select an action by providing information.

[0043] Furthermore, in the present disclosure, an AI model may also refer to a data-driven algorithm that applies AI technology to generate a set of outputs based on a set of inputs.

[0044] In addition, in the present disclosure, AI models, models, ML models, predictive analytics, predictive analytics models, tools, autoencoders, encoders, decoders, neural network models, AI algorithms, schemes, etc. can also be rewritten one from another. In addition, the AI ​​model can also be derived using at least one of regression analysis (e.g., linear regression analysis, multiple regression analysis, logistic regression analysis), support vector machines, random forests, neural networks, deep learning, etc.

[0045] In the present disclosure, the autoencoder can also be rewritten with any autoencoder such as stacked autoencoder, convolutional autoencoder, etc. The encoder / decoder of the present disclosure can also adopt models such as residual network (ResidualNetwork (ResNet)), DenseNet (dense connection network), RefineNet (multi-path reinforcement network), etc.

[0046] In addition, in the present disclosure, encoder, encoding, encoding / encoded, correction / change / control based on encoder, compression, compressing / compressed, generating, generating / generated, etc. can also be rewritten to each other.

[0047] In addition, in the present disclosure, decoder, decoding, decoding / decoding, correction / change / control based on the decoder, decompression, decompression / decompressed, reconstruction, reconstruction / reconstruction, etc. can also be rewritten to each other.

[0048] In the present disclosure, the layers (regarding the AI ​​model) may also be mutually rewritten with the layers (input layer, intermediate layer, etc.) used in the AI ​​model. The layers of the present disclosure may also correspond to at least one of the input layer, intermediate layer, output layer, batch normalization layer, convolution layer, activation layer, dense layer, normalization layer, pooling layer, attention layer, dropout layer, fully connected layer, etc.

[0049] In the present disclosure, the training method of the AI ​​model may also include supervised learning, unsupervised learning, reinforcement learning, federated learning, etc. Supervised learning may also refer to the process of training a model based on input and corresponding labels. Unsupervised learning may also refer to the process of training a model without labeled data. Reinforcement learning may also refer to the process of training a model based on input (in other words, state) and feedback signals (in other words, rewards) generated from the output (in other words, actions) of the model in an environment where the model interacts.

[0050] In the present disclosure, generation, calculation, and export, etc. may also be mutually replaced. In the present disclosure, implementation, operation, operation, and execution, etc. may also be mutually replaced. In the present disclosure, training, learning, updating, and retraining, etc. may also be mutually replaced. In the present disclosure, reasoning, after-training, formal utilization, and actual utilization, etc. may also be mutually replaced. In the present disclosure, signal may also be mutually replaced with signal / channel.

[0051] Figure 1 This is a diagram showing an example of a framework for managing AI models. In this example, each stage associated with an AI model is represented by a block. This example is also represented as the life cycle management (LCM) of an AI model.

[0052] The data collection phase corresponds to the phase of collecting data for the generation / update of AI models. The data collection phase may also include data collation (e.g., deciding which data to migrate for model training / model inference), data migration (e.g., migrating data to entities that perform model training / model inference (e.g., UE, gNB)), etc.

[0053] In addition, data collection may also mean: the process of collecting data by a network node, management entity or UE for the purpose of AI model training / data analysis / inference. In the present disclosure, processing and process may also be rewritten to each other.

[0054] In the model training phase, model training is performed based on the data (training data) migrated from the collection phase. This phase may also include data preparation (e.g., implementation of data preprocessing, cleaning, formatting, conversion, etc.), model training / validation (validation), model testing (e.g., confirming whether the trained model meets the performance threshold), model exchange (e.g., migration of models for distributed learning), model deployment / update (deployment / update of models to entities that perform model inference), etc.

[0055] In addition, AI model training can also mean: a process used to train an AI model through a data-driven approach and obtain a trained AI model for reasoning.

[0056] In addition, AI model validation can also refer to a sub-process of training for evaluating the quality of an AI model using a dataset different from the one used in model training. This sub-process helps select model parameters that generalize beyond the dataset used in model training.

[0057] In addition, AI model testing can also mean a sub-process of training that uses a different dataset from the one used in model training / validation to evaluate the performance of the final AI model. In addition, testing is different from validation and may not be based on subsequent model tuning.

[0058] In the model inference phase, model inference is performed based on the data (inference data) migrated from the collection phase. This phase may also include data preparation (e.g., implementation of data preprocessing, cleaning, formatting, conversion, etc.), model inference, model monitoring (e.g., monitoring the performance of model inference), model performance feedback (feedback of model performance to entities performing model training), output (providing model output to actors), etc.

[0059] In addition, AI model inference can also refer to the process of using a trained AI model to generate a set of outputs based on a set of inputs.

[0060] In addition, a UE side model may also refer to an AI model whose reasoning is fully implemented in the UE. A network side model may also refer to an AI model whose reasoning is fully implemented in the network (e.g., gNB).

[0061] In addition, a one-sided model may also refer to a UE-side model or a network-side model. A two-sided model may also refer to a paired AI model that performs joint inference. Here, joint inference may also include AI inference that is performed jointly across the UE and the network, for example, the first part of the inference may be performed by the UE first, and the remaining part may be performed by the gNB (or vice versa).

[0062] In addition, AI model monitoring can also refer to the processing used to monitor the reasoning performance of the AI ​​model, and can also be interchanged with model performance monitoring, performance monitoring, etc.

[0063] In addition, model registration can also mean assigning a version identifier to the model and making it executable by compiling it in the specific hardware used in the inference phase. In addition, model deployment can also mean distributing the runtime image (or image of the execution environment) of a fully developed and tested model to (or activating in) the target (e.g., UE / gNB) where inference is performed.

[0064] The actor phase may also include action triggers (e.g., deciding whether to trigger an action on other entities), feedback (e.g., providing training data / inference data / information required for performance feedback), etc.

[0065] In addition, training of models, for example, for mobility optimization, can also be performed in operation and maintenance management (OAM) / gNodeB (gNB) in the network (NW). In the former case, interoperability, large-capacity storage, operator manageability, model flexibility (feature engineering, etc.) are advantageous. In the latter case, there is no need for latency in model updates, data exchange for model decompression, etc., which is advantageous. Reasoning of the above-mentioned model can also be performed in the gNB, for example.

[0066] The entity doing the training / inference can also vary depending on the use case (in other words, the function of the AI ​​model). The functions of the AI ​​model can also include beam management, beam prediction, autoencoder (or information compression), CSI feedback, position positioning, etc.

[0067] For example, for AI-assisted beam management based on measurement reports, OAM / gNB can also perform model training and gNB can perform model inference.

[0068] For AI-assisted UE assisted positioning (assisted positioning), the location management function (LMF) can also perform model training, and the LMF performs model reasoning.

[0069] For CSI feedback / channel estimation using autoencoders, OAM / gNB / UE can also perform model training, and gNB / UE (jointly) perform model inference.

[0070] For AI-assisted beam management based on beam measurement or AI-assisted UE-based positioning, OAM / gNB / UE can also perform model training and UE can perform model inference.

[0071] In addition, model activation may also mean activating an AI model for a specific function. Model deactivation may also mean deactivating an AI model for a specific function. Model switching may also mean deactivating a currently activated AI model for a specific function and activating a different AI model.

[0072] In addition, model transfer can also mean the distribution of AI models over the air interface. The distribution can also include distributing one or both of the following in the receiving side: parameters of a known model structure, or a new model with parameters. In addition, the distribution can also include a complete model or a partial model. Model download can also mean model migration from the network to the UE. Model upload can also mean model migration from the UE to the network.

[0073] Figure 2 This is a diagram showing an example of specifying an AI model. In this example, the UE and the NW (e.g., the base station (BS)) can identify model #1 and #2 (the details of the model may not be fully understood). Alternatively, the UE may report the performance of model #1 and the performance of model #2 to the NW, and the NW may instruct the UE on the AI ​​model to be used.

[0074] (UE positioning using AI technology)

[0075] Fingerprinting localization, which uses the propagation characteristics of wireless signals to estimate the location of wireless devices, is being widely used in both line-of-site (LOS) and non-line-of-site (NLOS) scenarios.

[0076] In the present disclosure, LOS may also mean that the UE and the base station are in an environment where they can see each other (or there are no obstructions), and NLOS may also mean that the UE and the base station are not in an environment where they can see each other (or there are obstructions).

[0077] In fingerprint positioning, the UE’s location is estimated based on the fingerprints of multiple transmission paths (multipath) of the UE and based on a database / AI model.

[0078] The multipath information may be, for example, information on the angle of arrival (Angle of Arrival (AoA)) / angle of departure (Angle of Departure (AoD)) of a signal in an optimal / candidate transmission path.

[0079] In addition, in the present disclosure, information related to AoA may include, for example, information related to at least one of azimuth angles of arrival and zenith angles of arrival. In addition, information related to AoD may include, for example, information related to at least one of azimuth angles of departure and zenith angles of departure.

[0080] In 3GPP Rel.16 NR, the following positioning technologies are supported.

[0081] Positioning based on DL / UL Time Difference Of Arrival (TDOA),

[0082] Angle-based (DL AoD / UL AoA) positioning,

[0083] Positioning based on multiple round trip times (RTT),

[0084] Enhanced Cell ID (E-CID) based positioning.

[0085] Figure 3 This is a diagram showing an example of positioning based on DL / UL TDOA. For example, assume that multiple base stations (TRP#0-#2) are configured around the UE. In this positioning method, the measurement value of the received reference signal time difference (RSTD) is used to estimate (measure) the position of the UE. For example, RSTD (TRP#i, TRP#j) for specific two base stations (TRP#i, TRP#j (i, j are integers)) is connected. i -T j ) takes a certain value (k i,j ) points, we can draw the hyperbola H i,j The intersection of multiple such hyperbolas (in this case, H 0,1 , H 1,2 , H 2,0 The intersection of the reference signal and the reference signal may also be used to estimate the position of the UE. In addition, the RSRP of the reference signal may also be used to estimate the position of the UE.

[0086] Figure 4 This is a diagram showing an example of positioning based on DL AoD / UL AoA. In this positioning method, the position of the UE is estimated using the measured value of DL AoD (e.g., θ or φ) or the measured value of UL AoA (e.g., θ or φ). In addition, the position of the UE can also be estimated using RSRP.

[0087] Figure 5 This is a diagram showing an example of positioning based on multiple RTTs. In this positioning method, multiple RTTs calculated based on the Tx / Rx time difference of the reference signal (and additionally based on RSRP, RSRQ, etc.) are used to estimate the position of the UE. For example, a geometric circle based on RTT can be drawn with each base station as the center. The intersection of these multiple circles can also be estimated as the position of the UE.

[0088] Figure 6 This is a diagram showing an example of E-CID-based positioning. In this positioning method, the UE position is estimated based on the geometric positions of the serving cell / neighboring cells and additional measurement results (Tx-Rx time difference, RSRP, RSRQ, etc.).

[0089] The positioning in the above DL (DL TDOA, DL AoD) can also be implemented on the UE side or the LMF side. For example, in UE-based positioning, the UE position can also be calculated by the UE based on various measurement results of the UE and assistance information from the LMF. In addition, in UE assisted positioning, the UE can report various measurement results to the LMF, and the LMF can calculate the UE position. The assistance information can also be information used to assist the UE position estimation.

[0090] The positioning in the UL (UL TDOA, UL AoA) mentioned above can also be implemented on the LMF side. In this case, the base station can also report various measurement results to the LMF, and the LMF calculates the position of the UE.

[0091] The above positioning in DL and UL (multi-RTT, E-CID) can also be implemented on the LMF side. In this case, the UE / base station can also report various measurement results to the LMF, and the LMF calculates the UE's position.

[0092] In addition, in 3GPP Rel.17, a positioning method using auxiliary information is proposed for the purpose of further improving positioning accuracy. Auxiliary information can also be used as measurement information for the above-mentioned DL / UL-TDOA, DL-AoD / UL-AoA, multi-RTT, and E-CID, and transmitted between UE, base station, and LMF.

[0093] The auxiliary information may also include information related to at least one of the following:

[0094] Timing Error Group (TEG),

[0095] RSRPP (path-specific RSRP),

[0096] Expected angle,

[0097] Adjacent beam information,

[0098] TRP antenna / beam information,

[0099] LOS / NLOS indicator,

[0100] Additional route reporting.

[0101] TEG may also indicate one or more PRS (Positioning Reference Signal) resources whose Rx / Tx timing errors are within a certain margin.

[0102] RSRPP may also indicate the measurement result of RSRP in the original path.

[0103] In UL positioning, the auxiliary information related to the expected angle may also indicate the expected UL-AoA / ZoA. The auxiliary information may also be sent from the LMF to the base station. In addition, the auxiliary information may also support at least one of UL TDOA, UL AoA, and multi-RTT positioning.

[0104] In DL positioning, the auxiliary information related to the expected angle may also include information related to the expected DL-AoA / ZoA (expected DL-AoA / ZoA) or the expected DL-AoD / ZoD (expected DL-AoD / ZoD). The auxiliary information may also be sent from the LMF to the UE. In addition, the auxiliary information may also support at least one of DL TDOA, DL AoA, and multi-RTT positioning. As a result, the accuracy of angle-based UE positioning is improved, and the Rx beamforming of the UE or base station can be optimized.

[0105] In addition, the auxiliary information related to the expected angle may include information indicating the uncertainty range of these values ​​in addition to the information of the values ​​of AoA / ZoA / AoD / ZoD themselves as described above.

[0106] As additional beam information, the adjacent beam information may also include a subset of DL-PRS resources for the purpose of prioritizing DL-AoD reports (option 1), or information related to the main axis (Boresight) direction of each DL-PRS resource (option 2). This can optimize the UE's Rx beam scanning and DL-AoD measurement.

[0107] In addition, the auxiliary information may include PRS beam pattern information as additional beam information. The PRS beam pattern information may also include information related to the relative power between DL-PRS resources at each angle of each TRP.

[0108] The LOS / NLOS indicator may also indicate information related to Line Of Site (LOS) / Non-Line Of Site (NLOS).

[0109] In addition, for the purpose of improving the positioning delay of the UE, pre-set measurement gaps (MG), activation of the MG via the lower layer (lower layer), MG-less location, PRS Rx / Tx in the RRC_INACTIVE state, or on-demand PRS, etc. can also be set to the UE (and can also be used by the UE).

[0110] In 3GPP Rel.17 NR, it is agreed that in order to improve the UE's position estimation accuracy, the UE measures / reports the RSRP of adjacent beams. For example, in the UE-assisted DL-AoD positioning method, the LMF can include at least one of the following options 1 to 2 in the auxiliary information.

[0111] Option 1: A subset of PRS resources for the purpose of prioritization of DL-AoD reports. The subset can be set for each PRS resource according to the UE capabilities. In the case of reporting the PRS measurement requested for the associated PRS, the UE may also include the PRS measurement requested for the subset of PRS in the additional measurement of DL-AoD. The requested PRS measurement may also be DL PRS RSRP / path PRS RSRP. The UE may also report PRS measurements only for a subset of PRS resources. In addition, the subset associated with a PRS resource may also exist in the same / different PRS resource set as the PRS resource.

[0112] Option 2: Information related to the main axis direction is set for each PRS resource according to the UE capability.

[0113] In 3GPP Rel.16 NR, it is agreed that the expected RSTD and its uncertainty range are indicated from the LMF to the UE. Furthermore, in Rel.17, in order to reduce the error and complexity in the measurement of AoA / AoD, it is agreed that the expected angle and its uncertainty range are indicated from the LMF to the UE.

[0114] In 3GPP Rel.17 NR, the introduction of positioning reference unit (PRU) is being studied for positioning. PRU is discussed as a reference device with a known location to alleviate the transmission and reception timing error of UE / gNB. PRU can also be rewritten as UE / gNB / TRP (transmission reception point) / TP (transmission point).

[0115] For example, the PRU may also support at least one of the following:

[0116] Measure DL PRS and report associated measurements (e.g. RSTD / transmit / receive time difference / RSRP) to the LMF;

[0117] Sending SRS to enable the TRP to measure measurements associated with the reference device (e.g., relative time of arrival (RTOA) / transmit / receive time difference, AOA) and report to the LMF;

[0118] Operation, measurement, various parameters (parameters related to the enhancement of transmission and reception timing delay, AoD and AOA, and correction of measurement values);

[0119] If the LMF does not have the position coordinate information, report the position coordinate information of the reference device to the LMF;

[0120] The reference device whose location is known is the UE / gNB;

[0121] • The accuracy of the position of the reference device can be known.

[0122] For example, there are two use cases for positioning using AI models:

[0123] Direct AI / ML positioning

[0124] AI / ML-assisted positioning.

[0125] Through direct AI / ML positioning, for example, UE positioning (UE location) is output. Through AI / ML assisted positioning, for example, intermediate features are output. The intermediate features can also be input into the AI / ML model again.

[0126] As an output example of the above-mentioned AI / ML-assisted positioning, at least one of the following may be included:

[0127] LOS / NLOS identification (LOS / NLOS probability);

[0128] ToA (time of arrival of PRS / SRS);

[0129] Rx-Tx (send and receive) time difference;

[0130] AoA / AoD;

[0131] Number of waves, Rx-Tx (transmit and receive) phase difference (Rel.18 phase measurement);

[0132] DL RSTD / UL TDOA;

[0133] ·DL-PRS / UL-SRS, RSRPs / RSRPPs;

[0134] ·The likelihood of the above value (e.g., probability of ToA).

[0135] (Beam information used for UE positioning)

[0136] As mentioned above, antenna (configuration) settings / beam information is considered useful for AI / Ml models.

[0137] As scenarios using antenna (configuration) settings / beam information, consider the following scenarios A and B.

[0138] [Scenario A]

[0139] Select the most appropriate AI model based on antenna settings / frequency / region.

[0140] [Scenario B]

[0141] In order for the AI ​​model to provide better performance, metadata (antenna setting information / beam information) is required as input.

[0142] In existing specifications, assistance information supporting beam information of a base station (gNB) from the network (NW) to a UE is only used for positioning.

[0143] For future wireless communication methods, the following are being studied:

[0144] Use beam information for beam management;

[0145] The beam information is also used in interfaces other than positioning protocols (e.g., LTE Positioning Protocol (LPP)).

[0146] The beam information of RS other than the Positioning Reference Signal (PRS) is used for positioning;

[0147] · Using the beam information in the UE.

[0148] In Rel.17, as beam information from LMF to UE (beam information for UE-based positioning, information related to the transmission beam of the base station), beam information indicating the direction (main axis direction) of the beam for each PRS is supported. The beam information can also be information indicating the main axis direction of each PRS.

[0149] The beam information indicating the direction of the beam of each PRS is "DL-PRS-BeamInfoElement" contained in "NR-DL-PRS-BeamInfo" of the common NR positioning information element.

[0150] The "DL-PRS-BeamInfoElement" includes information on the azimuth angle (azimuthangle) of the beam transmitted from the base station (TRP) and information on the elevation angle (elevation angle).

[0151] The information related to the azimuth angle is “dl-PRS-Azimuth” and “dl-PRS-Azimuth-fine.” “dl-PRS-Azimuth” is information expressed in units of 1° and values ​​ranging from 0° to 359°, and “dl-PRS-Azimuth-fine” is information expressed in units of 0.1° and values ​​ranging from 0° to 0.9°.

[0152] The information related to the elevation angle is “dl-PRS-Elevation” and “dl-PRS-Elevation-fine.” “dl-PRS-Elevation” is information expressed in values ​​from 0° to 180° with a granularity of 1°, and “dl-PRS-Elevation-fine” is information expressed in values ​​from 0° to 0.9° with a granularity of 0.1°.

[0153] In addition, Rel.17 supports beam information indicating the relative power of DL PRS at each angle (azimuth / elevation) as beam information from LMF to UE (beam information based on UE positioning, information related to the transmission beam of the base station).

[0154] The beam information indicating the relative power is contained in the beam antenna information of the TRP ("NR-TRP-BeamAntennaInfo") within the common NR positioning information element.

[0155] "NR-TRP-BeamAntennaInfo" includes information "NR-TRP-BeamAntennaInfoAzimuthElevation" related to the beam antenna information of the TRP for the azimuth and elevation.

[0156] “NR-TRP-BeamAntennaInfoAzimuthElevation” includes “azimuth” indicating the azimuth angle with a granularity of 1° unit, “azimuth-fine” indicating the azimuth angle with a granularity of 0.1° unit, and a list of elevation angles “elevationList”.

[0157] The elevation angle list "elevationList" includes "elevation" indicating the elevation angle with a granularity of 1° unit, "elevation-fine" indicating the elevation angle with a granularity of 0.1° unit, and a beam power list "beamPowerList".

[0158] The beam power list "beamPowerList" includes "nr-dl-prs-ResourceSetID" indicating the resource set ID of DL PRS, "nr-dl-prs-ResourceID" indicating the resource ID of DL PRS, "nr-dl-prs-RelativePower" indicating the relative power of the resource provided by "nr-dl-prs-ResourceID" at a granularity of 1dB unit, and "nr-dl-prs-RelativePowerFine" indicating the relative power of the resource provided by "nr-dl-prs-ResourceID" at a granularity of 0.1dB unit.

[0159] Furthermore, Rel. 17 supports information indicating an antenna reference point (ARP) as beam (antenna) information (information on a transmission beam of a base station) from an LMF to a UE.

[0160] This information is represented by the location information of TRP in the common NR positioning information element, namely the "referencePoint" in "NR-TRP-LocationInfo".

[0161] The location information of TRP "NR-TRP-LocationInfo" is expressed by a reference point and its relative position to the reference point.

[0162] The location of the ARP of a PRS resource is expressed by a relative position associated with the ARP location of a PRS resource set.

[0163] The antenna reference point is expressed by altitude, latitude and longitude.

[0164] In addition, in Rel.17, information related to the spatial direction of DL PRS is supported as information (information related to the transmission beam of the base station) to LMF from a base station (e.g., gNB, NG-RAN (Next Generation Radio Access Network) node).

[0165] This information includes information indicating the main axis directions of the azimuth and elevation angles of the PRS resource.

[0166] In addition, the information includes transition information from the local coordinate system (LCS) to the global coordinate system (GCS).

[0167] The GCS may also be defined for a system including multiple base stations and multiple UEs. In addition, in the LCS, an array antenna for one base station or one UE may also be defined.

[0168] The LCS is used as a reference for defining the vector far-field of each antenna element in the array. The vector far-field is a pattern and polarization. The configuration of the array within the GCS can also be defined by transforming the GCS and LCS. The GCS / LCS can also be derived based on definitions and transformations that are recognizable to those skilled in the art (specified in the specification), for example.

[0169] In addition, in Rel.17, as information from a base station (e.g., gNB) to LMF (information related to the transmission beam of the base station), information representing the beam / antenna of TRP is supported.

[0170] This information includes information indicating the relative power of the DL PRS at each angle (azimuth / elevation).

[0171] In addition, in Rel.17, as information (information related to the receiving beam of the base station) to LMF from a base station (e.g., gNB), information related to the receiving beam during UL signal measurement is supported.

[0172] The information includes at least one of a PRS resource ID, a PRS resource set ID, and an SSB index.

[0173] In addition, Rel. 17 supports information related to spatial relationships as information sent from the UE to the NW (information related to the transmission beam of the UE).

[0174] This information indicates an ID / index of a specific RS (eg, SSB / CSI-RS / SRS / DL PRS).

[0175] In addition, the number of UE receiving beams in the beam scanning for positioning is specified in Rel. 17. The UE can also report the support of UE capabilities to LMF.

[0176] For example, in FR1, the UE uses one receive beam.

[0177] Furthermore, in FR2, when the UE supports a specific UE capability, the number of beams indicated by the information "numberOfRxBeamSweepingFactor" indicating the number of Rx beam sweeping factors for FR2 is used. Otherwise, the UE uses 8 receive beams.

[0178] In addition, information about the receive beam used by the UE for measurement is supported (e.g., "nr-DL-PRS-RxBeamIndex").

[0179] For this information, when different beams are used in the DL PRS resource set, the UE may also report the measurement values ​​received through the same receive beam.

[0180] In other words, the beam information sent by the UE is information indicating whether the same beam is used between resource sets.

[0181] (AI model information)

[0182] In the present disclosure, AI model information may also mean information including at least one of the following:

[0183] Information about the input / output of the AI ​​model;

[0184] Information for pre-processing / post-processing of AI model input / output;

[0185] Information about the parameters of the AI ​​model;

[0186] Training Information for AI models

[0187] Reasoning information for AI models;

[0188] Performance information related to AI models.

[0189] Here, the input / output information of the above AI model may also include information related to at least one of the following:

[0190] Content of input / output data (e.g., RSRP, SINR, amplitude / phase information in the channel matrix (or precoding matrix), information related to the angle of arrival (AoA), information related to the angle of departure (AoD), location information);

[0191] Auxiliary information about the data (also called meta information);

[0192] The type of input / output data (e.g., immutable value, floating point number);

[0193] Quantization interval (quantization step size) of input / output data (e.g. 1 dBm for L1-RSRP);

[0194] The range that the input / output data can take (e.g., [0, 1]).

[0195] Generalization Capability (GC)

[0196] In the field of AI / ML, GC refers to the ability of an AI model to adapt to unknown data (test data) rather than just the training data provided during training (to be able to produce the expected output and to be able to predict smoothly). GC performance is also called GC performance (or generalization performance).

[0197] (KPI)

[0198] Regarding the performance monitoring of AI models, common Key Performance Indicators (KPIs) are being studied.

[0199] The following is an initial list of common KPIs for evaluating the performance of AI / ML-based models:

[0200] Performance

[0201] Intermediate KPIs,

[0202] Link-level and system-level performance,

[0203] Generalization performance,

[0204] Over-the-air expenses,

[0205] The overhead of auxiliary information,

[0206] Data collection overhead,

[0207] Model delivery / transfer overhead,

[0208] The overhead of signaling associated with other AI / ML models,

[0209] Inference complexity,

[0210] The computational complexity of model inference: floating point operations (FLOPs (where s is a lowercase letter)) (this means the number of floating point operations),

[0211] · The computational complexity of pre- and post-processing,

[0212] The complexity of the model (number of parameters / data size (e.g. Mbyte), etc.),

[0213] The complexity of the training,

[0214] LCM related complexity and storage overhead,

[0215] Latency (e.g. inference latency).

[0216] In addition, the above-mentioned KPI is only an example, and other KPIs (for example, KPIs related to model training, KPIs specific to use cases considered for provided use cases, etc.) may be added to the list.

[0217] (Performance metrics related to positioning)

[0218] In the positioning of Rel.17, the following evaluation metrics are specified for performance evaluation (TR38.857). In addition, the percentile of positioning error can also be analyzed with 50%, 67%, 80%, and 90%.

[0219] Horizontal accuracy

[0220] The horizontal accuracy may represent 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 in 90% of the UEs.

[0221] Vertical accuracy

[0222] The vertical accuracy may represent 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 in 90% of the UEs.

[0223] Latency

[0224] The delay may be, for example, an end-to-end delay for the UE's location estimation. The delay may be less than 100 milliseconds (more preferably, on the order of 10 milliseconds). The delay may also include processing delays of various related nodes (UE, gNB, AMF, LMF, etc.) and signal delays (signaling delays) between nodes. As other delays, the delay of the physical layer used for the UE's location estimation may be included. The delay may be, for example, less than 10 milliseconds.

[0225] (Performance monitoring for positioning (model monitoring))

[0226] Classification of Positioning

[0227] Positioning using AI models can also be categorized as follows:

[0228] (1) UE-based positioning;

[0229] (2) AI / ML-assisted positioning;

[0230] (3) NG-RAN (Next Generation-Radio Access Network) node assisted positioning.

[0231] (1) UE-based positioning can be further classified as follows:

[0232] (1-1) Direct AI / ML positioning in the UE-side model;

[0233] (1-2) AI / ML-assisted positioning in the UE-side model, and non-AI-based positioning in the UE-side algorithm.

[0234] (2) AI / ML-assisted positioning can be further categorized as follows:

[0235] (2-1) AI / ML-assisted positioning in the UE-side model and non-AI-based positioning in the LMF-side algorithm;

[0236] (2-2) Direct AI / ML positioning in the LMF side model.

[0237] (3) NG-RAN node assisted positioning can be further categorized as follows:

[0238] (3-1) AI / ML-assisted positioning in the gNB side model and non-AI-based positioning in the LMF side algorithm;

[0239] (3-2) Direct AI / ML positioning in the LMF side model.

[0240] 《Performance metrics calculation》

[0241] In UE-based positioning, model monitoring for direct AI / ML positioning based on UE-side models may be performed by at least one of the following:

[0242] <1> Performance indicator calculation in UE;

[0243] <2> Performance indicator calculation in LMF.

[0244] In UE-based positioning, model monitoring for AI / ML-assisted positioning based on UE-side models may be performed by at least one of the following:

[0245] <3> Calculation of performance indicators (metrics) in UE;

[0246] <4> Performance indicator calculation in LMF.

[0247] In UE-assisted positioning, model monitoring for AI / ML-assisted positioning based on UE-side models can be performed as follows:

[0248] <5> Performance indicator calculation in LMF.

[0249] In NG-RAN node assisted positioning, model monitoring for AI / ML assisted positioning based on gNB side model may be performed by at least one of the following:

[0250] <6> Performance indicator calculation in gNB;

[0251] <7> Performance indicator calculation in LMF.

[0252] 《Performance Indicator Calculation in Direct AI / ML Positioning Based on UE-side Model》

[0253] The above <1> The performance indicator calculation (model monitoring) can be performed according to the following steps.

[0254] Step 1: The UE obtains the ground truth UE location with a lot of noise (the real value related to the UE location).

[0255] Step 1': The UE obtains an estimated UE position based on model reasoning.

[0256] Step 2: The UE calculates the performance indicators monitored by the model.

[0257] Step 3: The UE reports the performance indicators monitored by the model.

[0258] Step 3': The UE requests the LMF to activate / deactivate / switch the model.

[0259] Step 4: The UE receives an indication of activation / deactivation / switching of the model from the LMF.

[0260] Step 5: The UE activates / deactivates / switches the model.

[0261] The above <2> The performance indicator calculation (model monitoring) can be performed according to the following steps.

[0262] Step 1: The UE obtains the UE position based on model inference and reports it.

[0263] Step 1': LMF obtains the true value UE position with a lot of noise (the true value related to the UE position).

[0264] Step 2: LMF calculates the performance indicators monitored by the model.

[0265] Step 3: The UE receives an indication of activation / deactivation / switching of the model from the LMF.

[0266] Step 4: The UE activates / deactivates / switches the model.

[0267] 《Performance Indicator Calculation in AI / ML Assisted Positioning Based on UE-side Model》

[0268] The above <3> The performance indicator calculation (model monitoring) can be performed according to the following steps.

[0269] Step 1: The UE obtains noisy real-value data (real values ​​related to certain data (such as UE location)).

[0270] Step 1': UE obtains estimated data based on model reasoning.

[0271] Step 2: The UE calculates the performance indicators monitored by the model.

[0272] Step 3: The UE reports the performance indicators monitored by the model.

[0273] Step 3': The UE requests the LMF to activate / deactivate / switch the model.

[0274] Step 4: The UE receives an indication of activation / deactivation / switching of the model from the LMF.

[0275] Step 5: The UE activates / deactivates / switches the model.

[0276] The above <4> , <5> The performance indicator calculation (model monitoring) can be performed according to the following steps.

[0277] Step 1: UE obtains estimated data based on model reasoning and reports it.

[0278] Step 2: LMF obtains ground truth data (real values ​​related to certain data (eg UE location)).

[0279] Step 3: LMF calculates the performance indicators monitored by the model.

[0280] Step 4: The UE receives an indication of activation / deactivation / switching of the model from the LMF.

[0281] Step 5: The UE activates / deactivates / switches the model.

[0282] 《Calculation of performance indicators in AI / ML-assisted positioning based on gNB-side model》

[0283] The above <6> The performance indicator calculation (model monitoring) can be performed according to the following steps.

[0284] Step 1: gNB obtains estimated data based on model inference and reports it.

[0285] Step 1': The gNB obtains ground truth data (real values ​​related to certain data (e.g. UE location)).

[0286] Step 2: The gNB calculates the performance indicators monitored by the model.

[0287] Step 3: The gNB reports the performance indicators monitored by the model.

[0288] Step 3': The gNB requests the LMF to activate / deactivate / switch the model.

[0289] Step 4: The gNB receives the indication of activation / deactivation / switching of the model from the LMF.

[0290] Step 5: gNB activates / deactivates / switches the model.

[0291] The above <7> The performance indicator calculation (model monitoring) can be performed according to the following steps.

[0292] Step 1: gNB obtains estimated data based on model inference and reports it.

[0293] Step 2: LMF obtains ground truth data (real values ​​related to certain data (eg UE location)).

[0294] Step 3: LMF calculates the performance indicators monitored by the model.

[0295] Step 4: The gNB receives the indication of activation / deactivation / switching of the model from the LMF.

[0296] Step 5: gNB activates / deactivates / switches the model.

[0297] (Question raised)

[0298] However, when AI models are applied to positioning, model monitoring and model updating are important processes. Therefore, the following topics are studied regarding the impact of AI-based model monitoring and model updating on specifications.

[0299] <Topic 1>

[0300] How should performance metrics for model monitoring be defined?

[0301] <Topic 1-1>

[0302] How does the UE / NW (gNB or LMF) identify the performance indicators for monitoring.

[0303] <Topic 1-2>

[0304] How UE utilizes performance indicators.

[0305] <Topic 2>

[0306] When / how the UE performs monitoring (e.g., settings for monitoring, benchmarks for comparison, etc.).

[0307] <Topic 3>

[0308] How to perform specific operations on the UE / NW after monitoring (for example, model switching / update / rollback operations).

[0309] <Topic 4>

[0310] When configuring (deploying) multiple (more than 2) AI models in a one-sided model / two-sided model, are there any regulatory impacts and are there any benchmarks for monitoring / updating? For example, in a joint training where the output of a certain AI model (set as AI model #1) (which can be an intermediate value of positioning such as ToA, RSTD, RSRP, Rx-Tx time difference, etc.) can be used as the input of another AI model (set as AI model #2), the performance of AI model #2 may be affected by the accuracy of the output of AI model #1. In the case where AI model #2 does not converge / cannot be well trained, or the tested performance does not meet the requirements, how to deal with the jointly trained model may become a problem.

[0311] In view of the above-mentioned problems, the inventors of the present invention have conceived a method of monitoring an AI model related to positioning.

[0312] (various rewrites, etc.)

[0313] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. The wireless communication methods according to the embodiments may be applied individually or in combination.

[0314] In the following implementations, the angle at which the signal arrives in the UE, the AoA in the UE, and the AoA in the base station may also be overwritten. In the present disclosure, the angle at which the signal is transmitted in the UE, the AoD in the UE, and the AoD in the base station may also be overwritten. In the present disclosure, AoA and AoD may also be overwritten. In the present disclosure, the UE and the base station may also be overwritten.

[0315] In one embodiment of the present disclosure, a terminal (terminal, user terminal, User Equipment (UE)) / base station (Base Station (BS)) trains an ML model in a training mode and implements the ML model in an inference mode (also referred to as an inference mode, etc.). In the inference mode, the accuracy of the ML model trained in the training mode may also be verified (validation).

[0316] In the present disclosure, an object may also be, for example, a device, such as a terminal or a base station, or a device, etc. In addition, in the present disclosure, an object may also correspond to a program / model / entity operating in the device.

[0317] In the present disclosure, "A / B" and "at least one of A and B" may be replaced with each other. In addition, in the present disclosure, "A / B / C" may also mean "at least one of A, B, and C".

[0318] In the present disclosure, activate, deactivate, indicate (or specify), select, configure, update, determine, etc. may also be mutually rewritten. In the present disclosure, support, control, controllable, operate, and can operate, etc. may also be mutually rewritten.

[0319] In the present disclosure, Radio Resource Control (RRC), RRC parameters, RRC messages, high-layer parameters, fields, Information Element (IE), settings, etc. may also be overwritten with each other. In the present disclosure, Medium Access Control (MAC) control elements (MAC Control Element (CE)), update commands, activation / deactivation commands, etc. may also be overwritten with each other.

[0320] In the present disclosure, high-layer signaling may be, for example, Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information, positioning protocol (e.g., NR Positioning Protocol A (NRPPa)) / LTE Positioning Protocol (LTE Positioning Protocol (LPP))) message, or any one or a combination thereof.

[0321] In the present disclosure, MAC signaling may also use, for example, MAC Control Element (MACCE), MAC Protocol Data Unit (MAC Protocol Data Unit (PDU)), etc. Broadcast information may also be, for example, Master Information Block (MIB), System Information Block (SIB), minimum system information (Remaining Minimum System Information (RMSI)), Other System Information (Other System Information (OSI)), etc.

[0322] In the present disclosure, the physical layer signaling may be, for example, downlink control information (Downlink Control Information (DCI)), uplink control information (Uplink Control Information (UCI)), etc.

[0323] In the present disclosure, an index, an identifier (ID), an indicator, a resource ID, etc. may also be overwritten with each other. In the present disclosure, a sequence, a list, a set, a group, a cluster, a subset, etc. may also be overwritten with each other.

[0324] In the present disclosure, panel, UE panel, panel group, beam, beam group, precoder, uplink (UL) transmission 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 (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 (PDSCH)), Channel (PUCCH)) group, PUCCH resource group), resources (for example, reference signal resources, SRS resources), resource sets (for example, reference signal resource sets), CORESET pool, downlink transmission configuration indication state (Transmission Configuration Indicationstate) (TCI state) (DL TCI state), uplink TCI state (UL TCI state), unified TCI state, common TCI state, Quasi-Co-Location (QCL)), QCL assumptions, etc. can also be rewritten mutually.

[0325] In the present disclosure, CSI-RS, Non Zero Power (NZP) CSI-RS, Zero Power (ZeroPower (ZP)) CSI-RS, and CSI Interference Measurement (CSI-IM) may also be replaced with each other. In addition, CSI-RS may also include other reference signals.

[0326] In the present disclosure, the measured / reported RS may also mean the RS measured / reported for CSI reporting.

[0327] In the present disclosure, timing, moment, time, time slot, sub-slot, code element, sub-frame, etc. can also be rewritten.

[0328] In the present disclosure, direction, axis, dimension, domain, polarization, polarization component, etc. can also be rewritten mutually.

[0329] In the present disclosure, estimation, prediction, and inference may be replaced by each other. In addition, in the present disclosure, estimation, prediction, and inference may be replaced by each other.

[0330] In the present disclosure, the autoencoder, encoder, decoder, etc. can also be rewritten as at least one of a model, an ML model, a neural network model, an AI model, an AI algorithm, etc. In addition, the autoencoder can also be rewritten with any autoencoder such as a stacked autoencoder, a convolutional autoencoder, etc. The encoder / decoder of the present disclosure can also adopt models such as Residual Network (ResidualNetwork (ResNet)), DenseNet (densely connected network), and RefineNet (multi-path reinforcement network).

[0331] In the present disclosure, bits, bit strings, bit sequences, sequences, values, information, values ​​obtained from bits, information obtained from bits, etc., may be replaced with each other.

[0332] In the present disclosure, the layers (about the encoder) may also be mutually rewritten with the layers (input layer, intermediate layer, etc.) used in the AI ​​model. The layers of the present disclosure may also correspond to at least one of the input layer, intermediate layer, output layer, batch normalization layer, convolution layer, activation layer, dense connection (dense) layer, normalization layer, pooling layer, attention layer, random dropout layer, fully connected layer, etc.

[0333] In the present disclosure, RSRP may be replaced with any parameter related to received power / reception quality (eg, RSRQ, SINR, CSI), etc.

[0334] (Wireless Communication Method)

[0335] In the present disclosure, positioning can also be replaced with position determination, position estimation, position prediction, etc. In the present disclosure, KPI (key performance indicator) and performance metrics can also be replaced with each other. In the present disclosure, performance metrics calculation, model monitoring, and performance monitoring can also be replaced with each other.

[0336] <First Embodiment>

[0337] The first embodiment relates to performance metrics for model monitoring.

[0338] [Implementation method 1.0]

[0339] Among the performance indicators used for model monitoring, at least one of the following can be used:

[0340] Performance

[0341] Latency

[0342] Complexity

[0343] Performance can include at least one of the following:

[0344] Horizontal accuracy of AI / ML-based positioning (meters);

[0345] Vertical accuracy of AI / ML-based positioning (meters);

[0346] Accuracy of intermediate features for AI / ML-based positioning.

[0347] The horizontal accuracy may represent 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 in 90% of the UEs.

[0348] The vertical accuracy may represent 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 in 90% of the UEs.

[0349] The intermediate feature accuracy may represent the difference between an inferenced intermediate value and an intermediate value derived based on the actual UE position. The intermediate feature accuracy may be represented, for example, by at least one of the accuracy of the LOS / NLOS indicator (error rate %), ToA (milliseconds), AoA (degrees), RSTD (milliseconds), RSRP (dBm), etc.

[0350] Latency can include at least one of the following:

[0351] Physical layer latency (milliseconds),

[0352] End-to-end latency (milliseconds).

[0353] The delay in the positioning process can also be calculated according to Figure 7 is defined as shown in the figure. Figure 7 FIG. 1 is a diagram showing an example of the start time / end time of the delay of the physical layer involved in the first embodiment. Figure 7 As shown, the delay of the physical layer can be defined independently according to the positioning method.

[0354] For example, in the case of UE-based positioning, the start time can be any one of the timing when the UE sends a PUSCH containing an MG request (Alt1), the timing when the gNB uses PDSCH to send an LPP message containing auxiliary data (Alt2), and the timing when the UE starts receiving DL PRS (Alt3). In addition, the end time in this case can be the timing when the gNB successfully decodes the PUSCH containing the LPP Provide Location Information message, or the timing when the UE performs position estimation calculation if it is unsuccessful.

[0355] In case of UE-assisted positioning / LMF-based positioning, the start time can be the timing when the gNB sends the PDSCH containing the LPP Request Location Information message. In addition, the end time in this case can be the timing when the gNB successfully decodes the PUSCH containing the LPP Provide Location Information message.

[0356] 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. In addition, the end time in this case may be the timing when the gNB sends the NRPPa measurement response message.

[0357] In addition, the delay in AI models is not limited to Figure 7 For example, the timing when UE / NW receives the input of model inference can be set as the start time, and the timing when NW / UE receives the output of the AI ​​model can be set as the end time.

[0358] The end-to-end delay may also be the delay used for UE position estimation.

[0359] In addition, the delay may also include high-level delays as other delays. The delay may also include processing delays (processing delays) of various related nodes (UE, gNB, AMF, LMF, etc.) and signal delays (signaling delays) between nodes. In addition, the definition of each delay mentioned above may also follow the definition of Rel.17.

[0360] Complexity can also be defined by the computational complexity of model inference (floating point operations (FLOPs)). In addition, the complexity of an AI model can also be defined by, for example, the data size of the model (Mbyte) and the number of parameters associated with the AI ​​model.

[0361] [Implementation method 1.1]

[0362] Implementation 1.1 relates to a method for indicating (notifying) a performance indicator.

[0363] Performance indicators can also be indicated to UE / gNBs that deploy (equipped with / apply) AI models.

[0364] <Option 1.1.1>

[0365] NW (gNB / LMF) can also notify performance indicators through signaling such as LPP / MAC CE / DCI / RRC / NRPPa.

[0366] <Option 1.1.2>

[0367] The UE / NW may also notify the performance indicators based on predefined rules.

[0368] <Changes>

[0369] The overall LCM in AI-based positioning may also include signal exchange based on LPP between UE and LMF, and signal exchange based on NRPPa between gNB and LMF.

[0370] [Implementation method 1.2]

[0371] Implementation 1.2 involves the accuracy of performance indicators, etc.

[0372] [Implementation method 1.2.1]

[0373] The above-mentioned horizontal accuracy / vertical accuracy may be defined based on at least one of the following options.

[0374] <Option 1.2.1.1>

[0375] • The difference between the one-shot inferenced value associated with the horizontal position / vertical position and the geodesic distance of the actual horizontal position / vertical position in the UE / PRU.

[0376] <Option 1.2.1.2>

[0377] The average value of the difference in geodesic distance between the inferred value associated with the horizontal position / vertical position and the actual horizontal position / vertical position in the UE / PRU within a certain time duration.

[0378] In <Option 1.2.1.2>, the certain duration may be determined based on at least one of the following rules:

[0379] Based on pre-defined rules;

[0380] Based on the indication from NW to UE / gNB via signaling such as LPP / MAC CE / DCI / RRC / NRPPa;

[0381] Based on the implementation status of NW / UE (for example, continuous monitoring in a certain time unit).

[0382] The UE may also report the actual horizontal position / vertical position to the NW via signaling such as LPP / MAC CE / DCI / RRC / NRPPa.

[0383] The actual horizontal position / vertical position of the PRU can be identified by the NW, and the NW / PRU can indicate the horizontal position / vertical position to the UE / gNB via signaling such as LPP / MAC CE / DCI / RRC / NRPPa.

[0384] As a variation, it is preferred that the UE has a Global Navigation Satellite System (GNSS) capability.

[0385] As another variation, the above-mentioned UE / PRU may also be defined as a monitoring device that is able to always participate in the reasoning / monitoring of the AI ​​model and always provide actual location information.

[0386] [Implementation method 1.2.2]

[0387] The above-mentioned accuracy of intermediate feature may also be defined based on at least one of the following options.

[0388] <Option 1.2.2.1>

[0389] ·The time difference (milliseconds) of the inference information obtained by comparing with the true value data. Here, the inference information can also be a certain moment (one-shot) or an average value of a certain time (it can also be the above-mentioned certain time duration). The true value data can also be obtained based on the actual position of the UE. In this way, the actual position of the UE can be reused. This option can also be applied when the intermediate value is ToA, RSTD, Rx-Tx time difference, etc. The true value data can also be used as the true value / value close to the true value / prediction value with high reliability of the estimated and predicted value. In addition, when the data processed as true value data is assumed to have an error from the true value, it can also be called noisy ground-truth data.

[0390] <Option 1.2.2.2>

[0391] · The angular difference (in degrees) of the inferred information compared to the true value data. This option can also be applied when the intermediate value is AoA, AoD, expected AoA / AoD, etc.

[0392] <Option 1.2.2.3>

[0393] The power difference (dBm) of the inference information compared to the true value data. This option can also be applied when the intermediate value is RSRP, RSRQ, RSS, RSRPP, etc.

[0394] <Option 1.2.2.4>

[0395] The accuracy (%) of the inference information compared to the true value data. This option can also be applied when the intermediate value is a LOS / NLOS indication containing a hard value / binary indication

[0396] <Option 1.2.2.5>

[0397] The probability or the difference in probability (%) of the inference information compared to the true value data. This option can also be applied when the intermediate value is a LOS / NLOS indication containing a soft value (probability value (soft value)) / percentage indication.

[0398] <Changes>

[0399] The above-mentioned intermediate values ​​are not limited to measured values, but may also be likelihoods (probability distribution of ToA / AoA / RSRP, etc.). For example, with respect to the output of the AI ​​model, X% may also be the probability distribution of ToA that satisfies N milliseconds. In this case, as a requirement for the output, it can be defined as X% satisfying N milliseconds. In addition, in the case where the performance indicator is defined by probability, if X≥X0, no operation is required. In addition, in the case where the performance indicator is defined by the difference in probability, if X0-X≤threshold, no operation is required.

[0400] Furthermore, in addition to the estimated accuracy (horizontal accuracy / vertical accuracy / intermediate feature accuracy) related to positioning calculated as described above, the reliability of these estimated accuracies can also be calculated / estimated.

[0401] The UE may also have GNSS capability and the ability to derive intermediate values ​​based on the position that can be obtained from the GNSS.

[0402] The above options can also be applied in combination.

[0403] [Implementation method 1.3]

[0404] Implementation 1.3 relates to the performance of performance indicators.

[0405] The performance of an application performance indicator can be expressed as at least one of the following:

[0406] The performance of a single measured value (which can also be an output value at a certain moment (single time) or an average value over a certain period of time) that can be applied to the model monitoring on the UE / NW side;

[0407] The performance of the Cumulative Distribution Function (CDF) percentage corresponding to multiple values ​​that can be applied to the model in the UE / NW side.

[0408] The above value can also be at least one of the following:

[0409] The output values ​​of multiple AI models (corresponding to the same or different UEs) at a certain moment (single time), or the average output value of an AI model within the time associated with multiple AI models;

[0410] Multiple values ​​corresponding to the output of an AI model at a certain time.

[0411] By obtaining the above-mentioned values, it is possible to reduce small influences and avoid frequent operations (such as updating / switching / rollback of the model). In addition, it is possible to appropriately implement model monitoring observed from a system other than a single UE.

[0412] In addition, the above value may also be a value of X% of a value obtained by monitoring a certain time. Here, the certain time and X may also be determined based on at least one of the following:

[0413] Based on pre-defined rules;

[0414] Based on the indication from NW to UE / gNB via signaling such as LPP / MAC CE / DCI / RRC / NRPPa;

[0415] Based on the implementation status of NW / UE (for example, continuous monitoring in a certain time unit).

[0416] The requirements of the performance index may be predefined or set by the NW. For example, the positioning requirements of Rel.17 may be reused to determine various requirements based on the results of AI-based positioning. In the present disclosure, the requirements of the performance index may be rewritten as requirements related to whether the performance index can be applied, requirements for model monitoring, etc.

[0417] According to the first embodiment, the performance index can be appropriately defined / indicated.

[0418] <Second Embodiment>

[0419] The second embodiment relates to the application of model monitoring / performance metrics. In the present disclosure, monitoring information and output information of AI models can also be overwritten.

[0420] [Implementation method 2.1]

[0421] Implementation 2.1 involves the application of performance indicators on the UE side.

[0422] When model inference is performed on the NW / UE side, the UE can also determine whether the performance indicator requirements are met.

[0423] The UE may also report at least one of the following monitoring information to the NW via signaling such as LPP / MAC CE / DCI / RRC / NRPPa:

[0424] The accuracy of the output information of the monitored AI model;

[0425] The difference between the inferred horizontal position / vertical position and the actual horizontal position / vertical position;

[0426] The difference between the inferred output (ToA, AoA, RSTD, RSRP, etc.) and the output obtained from the actual location (ToA, AoA, RSTD, RSRP, etc.);

[0427] Delay difference;

[0428] The complexity of the AI ​​model and latency, the complexity required);

[0429] A binary indicator indicating whether the performance indicator requirement is met;

[0430] Calculated estimation accuracy (horizontal accuracy / vertical accuracy / intermediate feature accuracy);

[0431] Information about the reliability of the estimate.

[0432] In addition, the above monitoring information may also be reported based on at least one of the following options:

[0433] <Option 1>

[0434] When some of the conditions are met (for example, when the performance indicator does not meet a certain requirement);

[0435] <Option 2>

[0436] Always report after monitoring (report unconditionally);

[0437] <Option 3>

[0438] • Report based on settings / NW instructions (eg, periodically, semi-continuously, aperiodically).

[0439] [Implementation method 2.2]

[0440] Embodiment 2.2 relates to the application of performance indicators on the gNB side.

[0441] When model inference is performed on the UE / gNB / LMF side, the gNB can also determine whether the performance indicator requirements are met.

[0442] The UE / LMF may also report at least one of the following AI model output information to the gNB / LMF via signaling such as LPP / MAC CE / DCI / RRC / NRPPa. At this time, the UE may also report to the gNB via LMF:

[0443] Inferred UE coordinates;

[0444] Inferred ToA

[0445] Inferred LOS / NLOS indicators;

[0446] Inferred AoA, RSTD, RSRP;

[0447] The complexity of the AI ​​model,

[0448] ·Delay.

[0449] After comparing the information reported from the UE / information indicated from the LMF with the actual information, the gNB may also indicate at least one of the above-mentioned monitoring information (information on the UE side) to the UE / LMF via signaling such as LPP / MAC CE / DCI / RRC / NRPPa.

[0450] [Implementation method 2.3]

[0451] Embodiment 2.3 relates to the application of performance indicators in the LMF side.

[0452] When model inference is performed on the UE / gNB / LMF side, LMF can also determine whether the performance indicator requirements are met.

[0453] The UE / gNB may also report at least one of the following AI model output information to the NW via signaling such as LPP / MAC CE / DCI / RRC / NRPPa:

[0454] Inferred UE coordinates;

[0455] Inferred ToA

[0456] Inferred LOS / NLOS indicators;

[0457] Inferred AoA, RSTD, RSRP,

[0458] The complexity of the AI ​​model;

[0459] ·Delay.

[0460] After comparing the information reported from the UE / gNB with the actual information, the LMF can also indicate at least one of the above-mentioned monitoring information (UE side information) to the UE / gNB via signaling such as LPP / MAC CE / DCI / RRC / NRPPa.

[0461] In the above-mentioned embodiments 2.1-2.3, model monitoring and model reasoning are preferably performed by the same entity (UE / gNB / LMF).

[0462] According to the second embodiment, model monitoring can be appropriately performed.

[0463] <Third Embodiment>

[0464] The third embodiment relates to operations after model monitoring.

[0465] [Implementation method 3.0]

[0466] The UE can perform at least one of the following options after model monitoring:

[0467] <Option 1>

[0468] The UE can perform model switching / update (fine-tuning, re-training) / fallback. In the case of performing these operations, the UE can also report the operation to be performed to the gNB / LMF.

[0469] <Option 2>

[0470] The UE may also send requests to the gNB / LMF regarding upcoming operations.

[0471] The gNB can perform at least one of the following options after model monitoring:

[0472] <Option 1>

[0473] gNB is able to perform model switching / update (fine-tuning, re-training) / fallback.

[0474] <Option 2>

[0475] The gNB may also indicate expected operations to the UE / LMF.

[0476] [Implementation method 3.1]

[0477] In Embodiment 3.1, how the UE determines to execute the operation (specific operation) described in Embodiment 3.0 above is described. In Embodiments 3.1.1 to 3.1.3, variations of the method of determining the operation are described.

[0478] [Implementation method 3.1.1]

[0479] In the case of model monitoring on the NW / UE side, the UE may also decide whether to perform a specific operation after monitoring based on information from the NW. For example, in the case of model monitoring on the NW side, the NW may also indicate the above-mentioned monitoring information to the UE via signaling such as LPP / MAC CE / DCI / RRC / NRPPa. The monitoring information may also include a threshold value for monitoring information for performing a specific operation. That is, the UE may also decide a specific operation based on this monitoring information.

[0480] When the UE determines a specific operation based on the above-mentioned monitoring information, it may report to the NW that the specific operation is to be performed / has been performed via signaling such as LPP / MACCE / DCI / RRC / NRPPa.

[0481] [Implementation method 3.1.2]

[0482] In the case of model monitoring on the NW / UE side, the UE can also identify whether to perform or how to perform a specific operation after monitoring, and send the request to the gNB / NW. Similar to "Implementation 3.1.1", the NW can also indicate monitoring information to the UE.

[0483] The UE may also send a request containing the following information to the NW via signaling such as LPP / MAC CE / DCI / RRC / NRPPa:

[0484] · A 1-bit indicator for requesting a switch / update / fallback operation of the model;

[0485] · Regarding AI models with outputs such as direct AI positioning and AI-assisted positioning, the required functionality of the AI ​​model to be switched;

[0486] ·The AI ​​model ID of the monitoring object that fails to meet the requirements of the performance index;

[0487] The AI ​​model ID that you are requesting to switch to;

[0488] Parameters in the AI ​​model that need to be updated (unqualified parameters);

[0489] Datasets used in training / updating (fine-tuning) AI models;

[0490] Timing information (e.g., timestamp) for applying a fallback solution (a fallback solution may be a set positioning method);

[0491] • Desired / requested PRS settings, desired / requested model input values ​​(eg, number of PRS ports, multipath information).

[0492] [Implementation method 3.1.3]

[0493] In the case of model monitoring on the NW / UE side, the UE may also decide on the post-monitoring operation after receiving instructions from the NW.

[0494] The UE may also send a report containing the following information to the NW via signaling such as LPP / MAC CE / DCI / RRC / NRPPa:

[0495] The ID of the monitored AI model that needs to be updated in the AI ​​model of the monitored object;

[0496] ·AI model ID that can be switched;

[0497] Parameters in the AI ​​model that need to be updated (unqualified parameters);

[0498] Datasets used in training / updating (fine-tuning) AI models;

[0499] Timing information (e.g., timestamp) for applying a fallback solution (a fallback solution may be a set positioning method);

[0500] Updated AI model input information (PRS port count, multipath information, etc.);

[0501] • Desired / requested PRS settings, desired / requested model input values ​​(eg, number of PRS ports, multipath information).

[0502] [Implementation method 3.2]

[0503] In Embodiment 3.2, how the NW (gNB / LMF) determines the execution of the operation (specific operation) shown in Embodiment 3.0 above is described. In Embodiments 3.2.1-3.2.3, changes in the method of determining the operation are described.

[0504] [Implementation method 3.2.1]

[0505] When model monitoring is performed on the UE / gNB / LMF side, the NW can also decide whether to perform specific operations after monitoring. When model monitoring is performed on the UE / gNB side, the UE / gNB can also report the above monitoring information to the NW via signaling such as LPP / MAC CE / DCI / RRC / NRPPa.

[0506] [Implementation method 3.2.2]

[0507] When model monitoring is performed on the UE / gNB / LMF side, the NW can also identify whether a specific operation is performed after monitoring and instruct the UE. Similar to "Implementation 3.2.1", the UE / gNB can also report monitoring information to the NW.

[0508] The NW may also send an indication including the following information to the UE / gNB via signaling such as LPP / MAC CE / DCI / RRC / NRPPa:

[0509] · A 1-bit indicator for requesting a switch / update / fallback operation of the model;

[0510] · Regarding AI models with outputs such as direct AI positioning and AI-assisted positioning, the required functionality of the AI ​​model to be switched;

[0511] ·The AI ​​model ID of the monitoring object that fails to meet the requirements of the performance index;

[0512] NW indicates / requests the AI ​​model ID to switch;

[0513] Parameters of the AI ​​model that need to be updated (unqualified parameters);

[0514] Datasets used in training / updating (fine-tuning) AI models;

[0515] Timing information (e.g., timestamp) for applying a fallback solution (a fallback solution may be a set positioning method or a specific positioning method that is publicly indicated);

[0516] Updated PRS settings, updated model input values ​​(e.g., number of PRS ports, multipath information).

[0517] [Implementation method 3.2.3]

[0518] In the case of model monitoring on the UE / gNB / LMF side, NW can also decide the post-monitoring operation after receiving a request from the UE.

[0519] The NW may also send an indication including the following information to the UE via signaling such as LPP / MAC CE / DCI / RRC / NRPPa:

[0520] The ID of the monitored AI model that needs to be updated;

[0521] ·AI model ID that can be switched;

[0522] Parameters of the AI ​​model that need to be updated (unqualified parameters);

[0523] Datasets used in training / updating (fine-tuning) AI models;

[0524] Timing information (e.g., timestamp) for applying a fallback solution (a fallback solution may be a set positioning method or a specific positioning method that is publicly indicated);

[0525] The number of newly configured PRS ports;

[0526] Updated AI model input information (PRS port count, multipath information, etc.).

[0527] According to the third embodiment, the operation after model monitoring can be appropriately controlled.

[0528] <Fourth Embodiment>

[0529] The fourth embodiment relates to UE behavior when a one-sided model or a two-sided model is deployed. In the present disclosure, specific requirements may be rewritten as the above-mentioned performance indicator requirements, model monitoring requirements, and the like.

[0530] In the one-sided model, when multiple coherent AI models / paired AI models are deployed, or when the two-sided model is applied, the operation of the monitoring / corresponding UE can follow at least one of the following options 4-1 to 4-3.

[0531] Here, in AI-based positioning, when multiple AI models are deployed on the same / different sides, if the output of one or more AI models may be affected by the output of other AI models, the AI ​​model can also be regarded as a coherent AI model / paired AI model.

[0532] In addition, the above-mentioned "corresponding UE operation" may mean the switching / updating / rollback operation of the AI ​​model explained in the third embodiment, a request from the UE related to upcoming operations, and the like.

[0533] <Option 4-1>

[0534] When one or more of the multiple AI models do not meet specific requirements, the UE may also report / indicate monitoring information of the AI ​​model and update the corresponding AI model.

[0535] <Option 4-2>

[0536] If a certain AI model does not meet specific requirements, the UE may also interrupt the monitoring process. In addition, in this case, the UE may also report / indicate the monitoring information of the AI ​​model and update all related AI models / paired AI models.

[0537] For example, when the output of one or more AI models is the UE coordinates, if they do not meet a specific requirement, as long as the specific requirement is met in other AI models that output intermediate values, the UE can interrupt the monitoring process of the AI ​​model that does not meet the specific requirement and report / indicate the monitoring information of the other model that meets the specific requirement. In this case, UE operations corresponding to all coherent AI models / paired AI models can also be performed.

[0538] <Option 4-3>

[0539] When a certain AI model meets certain requirements, the UE may not report / indicate monitoring information of the AI ​​model. In this case, corresponding UE operation may not be required.

[0540] For example, when the output of a certain AI model is the UE coordinates, if the AI ​​model meets specific requirements and other AI models that output intermediate values ​​cannot meet the specific requirements, the UE does not need to report / indicate monitoring information of the AI ​​model that does not meet the specific requirements, nor does it need to update the AI ​​model.

[0541] This embodiment and the first to third embodiments described above can also be applied in combination. In addition, when multiple coherent AI models are deployed on the same / different sides, this embodiment can also be followed for operations other than those described above. For example, it can also be applied to joint training of AI models. In this case, the test information can also adopt the monitoring information described in the second embodiment.

[0542] According to the fourth embodiment, it is possible to appropriately control the UE operation in a case where a one-sided model / two-sided model is deployed.

[0543] [Notification of information to UE]

[0544] Any information in the above-mentioned embodiments (notification from the network (Network (NW)) (for example, the base station (BaseStation (BS)))) to the UE (in other words, the reception of any information from the BS in the UE) can also be carried out using physical layer signaling (for example, DCI), high-layer signaling (for example, RRC signaling, MAC CE, LPP), specific signals / channels (for example, PDCCH, PDSCH, reference signals) or a combination thereof.

[0545] When the above notification is performed through MAC CE, the MAC CE may be identified by including a new logical channel ID (Logical Channel ID (LCID)) which is not specified in the existing standard in a MAC subheader.

[0546] When the notification is performed via DCI, the notification may be performed via a specific field of the DCI, a Radio Network Temporary Identifier (RNTI) used for scrambling cyclic redundancy check (CRC) bits assigned to the DCI, the format of the DCI, etc.

[0547] In addition, notification of any information in the above-mentioned embodiments to the UE may be performed periodically, semi-continuously, or aperiodically.

[0548] [Notification of information from UE]

[0549] The notification of arbitrary information from the UE (to the NW) in the above-mentioned implementation manner (in other words, the sending / reporting of arbitrary information in the UE to the BS) can also be carried out using physical layer signaling (e.g., UCI), high-layer signaling (e.g., RRC signaling, MACCE, LPP), specific signals / channels (e.g., PUCCH, PUSCH, PRACH, reference signals) or a combination thereof.

[0550] In the case where the above notification is performed via MAC CE, the MAC CE may also be identified by including a new LCID that is not specified in the existing standards in the MAC subheader.

[0551] When the notification is performed through UCI, the notification may be transmitted using PUCCH or PUSCH.

[0552] In addition, the notification of arbitrary information from the UE in the above-mentioned embodiments may be performed periodically, semi-continuously, or aperiodically.

[0553] [About application of each embodiment]

[0554] At least one of the above-mentioned embodiments may also be applied to a case where a specific condition is satisfied. The specific condition may be specified in a standard or may be notified to the UE / BS using a higher layer signaling / physical layer signaling.

[0555] At least one of the above-mentioned embodiments may also be applied only to UEs that report a specific UE capability or support the specific UE capability.

[0556] The specific UE capability may also represent at least one of the following:

[0557] Supporting specific processing / operation / control / information related to at least one of the above embodiments;

[0558] Support model monitoring (performance monitoring);

[0559] Support AI model update / switch / rollback;

[0560] Support joint training.

[0561] In addition, the above-mentioned specific UE capabilities can be capabilities that are applied across all frequencies (commonly regardless of frequency), or capabilities for each frequency (for example, one or a combination of cells, frequency bands, frequency band combinations (combinations), BWPs, component carriers, etc.), or capabilities for each frequency range (for example, frequency range 1 (Frequency Range 1 (FR1)), FR2, FR3, FR4, FR5, FR2-1, FR2-2), or capabilities for each subcarrier spacing (SubCarrier Spacing (SCS)), or capabilities for each feature set (Feature Set (FS)) or each component carrier (Feature Set Per Component-carrier (FSPC)).

[0562] Furthermore, the specific UE capability may be a capability applied across all duplex modes (commonly regardless of the duplex mode) or a capability for each duplex mode (eg, time division duplex (TDD) or frequency division duplex (FDD)).

[0563] In addition, at least one of the above-mentioned implementations may also be applied to the following situation: the UE is set / activated / triggered with specific information associated with the above-mentioned implementations (or the operation of the above-mentioned implementations is implemented) through high-layer signaling / physical layer signaling. For example, the specific information may also be information indicating activation model monitoring, performance indicators, any RRC parameters for a specific version (e.g., Rel.18 / 19), etc.

[0564] When the UE does not support at least one of the above-mentioned specific UE capabilities or is not configured with the above-mentioned specific information, the UE may also apply the operations of Rel.15 / 16, for example.

[0565] (Note A)

[0566] The following inventions are added to one embodiment (first and second embodiments) of the present disclosure.

[0567] [Note 1]

[0568] A terminal having:

[0569] a receiving unit, for receiving a performance indicator for performance monitoring, regarding positioning based on artificial intelligence (AI); and

[0570] A control unit controls the performance monitoring.

[0571] [Note 2]

[0572] A terminal as described in Appendix 1, wherein:

[0573] The performance indicator includes information related to at least one of accuracy, delay, and complexity of positioning.

[0574] [Note 3]

[0575] A terminal as described in Supplement 1 or Supplement 2, wherein:

[0576] The terminal has:

[0577] The transmitting unit reports monitoring information including information related to the requirements of the performance indicator.

[0578] [Note 4]

[0579] A terminal as described in any one of Notes 1 to 3, wherein:

[0580] The terminal has:

[0581] The sending unit reports output information related to the positioning of the terminal outputted from the AI ​​model.

[0582] (Note B)

[0583] The following inventions are added to one embodiment (third and fourth embodiments) of the present disclosure.

[0584] [Note 1]

[0585] A terminal having:

[0586] a receiving unit, for receiving a performance indicator for performance monitoring, regarding positioning based on artificial intelligence (AI); and

[0587] a control unit for controlling the performance monitoring,

[0588] The control unit determines whether a specific operation after the performance monitoring can be performed.

[0589] [Note 2]

[0590] A terminal as described in Appendix 1, wherein:

[0591] The receiving unit receives monitoring information including information related to the requirements of the performance indicator.

[0592] The control unit determines the specific operation based on the monitoring information.

[0593] [Note 3]

[0594] A terminal as described in Supplement 1 or Supplement 2, wherein:

[0595] The specific operation is at least one of switching, updating, and rolling back of the AI ​​model.

[0596] [Note 4]

[0597] A terminal as described in any one of Notes 1 to 3, wherein:

[0598] The control unit controls the performance monitoring when one or more AI models are deployed based on specific requirements related to the performance indicator.

[0599] (Wireless Communication System)

[0600] Hereinafter, a configuration of a wireless communication system according to an embodiment of the present disclosure will be described. In the wireless communication system, communication is performed using any one of the wireless communication methods according to the above-mentioned embodiments of the present disclosure or a combination thereof.

[0601] Figure 81 is a diagram showing an example of a schematic structure of a wireless communication system according to an embodiment. The wireless communication system 1 (may also be simply referred to as the system 1) may also be a system that implements communication using Long Term Evolution (LTE) standardized by the Third Generation Partnership Project (3GPP), the fifth generation mobile communication system New Radio (5GNR), or the like.

[0602] In addition, the wireless communication system 1 may also support dual connectivity (Multi-RAT Dual Connectivity (MR-DC)) between multiple radio access technologies (Radio Access Technology (RAT)). MR-DC may also include dual connectivity between LTE (Evolved Universal Terrestrial Radio Access (E-UTRA)) and NR (E-UTRA-NR Dual Connectivity (E-UTRA-NR Dual Connectivity (EN-DC))), dual connectivity between NR and LTE (NR-E-UTRA Dual Connectivity (NR-E-UTRA Dual Connectivity (NE-DC))), etc.

[0603] In EN-DC, the base station (eNB) of LTE (E-UTRA) is the master node (Master Node (MN)), and the base station (gNB) of NR is the secondary node (Secondary Node (SN)). In NE-DC, the base station (gNB) of NR is the MN, and the base station (eNB) of LTE (E-UTRA) is the SN.

[0604] The wireless communication system 1 may also support dual connectivity between multiple base stations within the same RAT (for example, dual connectivity (NR-NR Dual Connectivity (NN-DC)) where both MN and SN are NR base stations (gNB)).

[0605] The wireless communication system 1 may also include a base station 11 that forms a macro cell C1 with a relatively wide coverage, and a base station 12 (12a-12c) that is configured in the macro cell C1 and forms a small cell C2 that is narrower than the macro cell C1. The user terminal 20 may also be located in at least one cell. The configuration and number of each cell and user terminal 20 are not limited to the method shown in the figure. Hereinafter, when the base stations 11 and 12 are not distinguished, they are collectively referred to as base stations 10.

[0606] The user terminal 20 may be connected to at least one of the plurality of base stations 10. The user terminal 20 may use at least one of carrier aggregation (CA) using a plurality of component carriers (CC) and dual connectivity (DC).

[0607] Each CC may also be included in at least one of the first frequency band (Frequency Range 1 (FR1)) and the second frequency band (Frequency Range 2 (FR2)). The macro cell C1 may also be included in FR1, and the small cell C2 may also be included in FR2. For example, FR1 may be a frequency band below 6 GHz (below 6 GHz (sub-6 GHz)), and FR2 may be a frequency band higher than 24 GHz (above-24 GHz). In addition, the frequency bands and definitions of FR1 and FR2 are not limited to these. For example, FR1 may also be equivalent to a frequency band higher than FR2.

[0608] 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.

[0609] Multiple base stations 10 may also be connected by wire (e.g., optical fiber based on Common Public Radio Interface (CPRI), X2 interface, etc.) or wireless (e.g., NR communication). For example, when NR communication between base stations 11 and 12 is used as a backhaul, the base station 11 equivalent to the upper station may also be referred to as an Integrated Access Backhaul (IAB) donor, and the base station 12 equivalent to a relay station (relay) may also be referred to as an IAB node.

[0610] The base station 10 may be connected to the core network 30 via other base stations 10 or directly. The core network 30 may include, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), and a Next Generation Core (NGC).

[0611] The core network 30 may also include, for example, user plane functions (User Plane Function (UPF)), access and mobility management function (Access and Mobility management Function (AMF)), session management function (Session Management Function (SMF)), unified data management (Unified Data Management (UDM)), application function (Application Function (AF)), data network (Data Network (DN)), location management function (Location Management Function (LMF)), maintenance and operation management (Operation, Administration and Maintenance (OAM))) and other network functions (Network Functions (NF)). In addition, multiple functions can also be provided by one network node. In addition, communication with an external network (for example, the Internet) can also be carried out via the DN.

[0612] The user terminal 20 may also be a terminal that supports at least one of communication modes such as LTE, LTE-A, and 5G.

[0613] In the wireless communication system 1, a wireless access method based on orthogonal frequency division multiplexing (OFDM) may be used. For example, in at least one of the downlink (DL) and the uplink (UL), 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), etc. may be used.

[0614] The radio access scheme may also be referred to as a waveform. In addition, in the wireless communication system 1, other radio access schemes (for example, other single-carrier transmission schemes, other multi-carrier transmission schemes) may be used as the radio access schemes for UL and DL.

[0615] As downlink channels, the wireless communication system 1 may use 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.

[0616] In addition, as uplink channels, 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)), etc. can also be used in the wireless communication system 1.

[0617] User data, high-layer control information, System Information Block (SIB), etc. are transmitted through PDSCH. User data, high-layer control information, etc. can also be transmitted through PUSCH. In addition, Master Information Block (MIB) can also be transmitted through PBCH.

[0618] The lower layer control information may also be transmitted via the PDCCH. The lower layer control information may include, for example, downlink control information (Downlink Control Information (DCI)) including scheduling information of at least one of the PDSCH and the PUSCH.

[0619] In addition, the DCI for scheduling the PDSCH may also be referred to as DL allocation, DL DCI, etc., and the DCI for scheduling the PUSCH may also be referred to as UL grant, UL DCI, etc. In addition, the PDSCH may also be rewritten as DL data, and the PUSCH may also be rewritten as UL data.

[0620] In the detection of PDCCH, the control resource set (CORESET) and the search space can also be used. CORESET corresponds to the resources for searching DCI. The search space corresponds to the search area and search method of PDCCH candidates. A CORESET can also be associated with one or more search spaces. The UE can also monitor the CORESET associated with a search space based on the search space setting.

[0621] A search space may also correspond to a PDCCH candidate corresponding to one or more aggregation levels. One or more search spaces may also be referred to as a search space set. In addition, the "search space", "search space set", "search space setting", "search space set setting", "CORESET", "CORESET setting" and the like in the present disclosure may also be rewritten mutually.

[0622] Uplink control information (uplink control information (UCI)) including at least one of channel state information (CSI), delivery confirmation information (e.g., also known as hybrid automatic repeat request confirmation (HARQ-ACK), ACK / NACK, etc.), and scheduling request (SR) can also be transmitted through PUCCH. The random access preamble used to establish a connection with a cell can also be transmitted through PRACH.

[0623] In the present disclosure, downlink, uplink, etc. may be expressed without “link.” In addition, various channels may be expressed without “Physical” at the beginning.

[0624] In the wireless communication system 1, a synchronization signal (Synchronization Signal (SS)), a downlink reference signal (Downlink Reference Signal (DL-RS)), etc. can also be transmitted. As DL-RS, in the wireless communication system 1, a cell-specific reference signal (Cell-specific Reference Signal (CRS)), a channel state information reference signal (Channel State Information Reference Signal (CSI-RS)), a demodulation reference signal (DeModulation Reference Signal (DMRS)), a positioning reference signal (Positioning Reference Signal (PRS)), a phase tracking reference signal (Phase Tracking Reference Signal (PTRS)), etc. can also be transmitted.

[0625] 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 SS (PSS, SSS) and PBCH (and DMRS for PBCH) may also be referred to as SS / PBCH block, SS block (SS Block (SSB)), etc. In addition, SS, SSB, etc. may also be referred to as reference signals.

[0626] In addition, in the wireless communication system 1, as an uplink reference signal (Uplink Reference Signal (UL-RS)), a measurement reference signal (Sounding Reference Signal (SRS)), a demodulation reference signal (DMRS), etc. may also be transmitted. In addition, DMRS may also be called a user terminal specific reference signal (UE-specific Reference Signal).

[0627] (Base Station)

[0628] Fig. 9 1 is a diagram showing an example of a structure of a base station involved in one embodiment. The base station 10 includes a control unit 110, a transmitting and receiving unit 120, a transmitting and receiving antenna 130, and a transmission path interface (transmission line interface) 140. In addition, the control unit 110, the transmitting and receiving unit 120, the transmitting and receiving antenna 130, and the transmission path interface 140 may each be provided with more than one.

[0629] In addition, in this example, the functional blocks of the characteristic parts in this embodiment are mainly shown, and it is also conceivable that the base station 10 also has other functional blocks required for wireless communication. Part of the processing of each unit described below may also be omitted.

[0630] The control unit 110 controls the entire base station 10. The control unit 110 can be composed of a controller, a control circuit, and the like that are described based on common knowledge in the technical field to which the present disclosure relates.

[0631] The control unit 110 may also control signal generation, scheduling (e.g., resource allocation, mapping), etc. The control unit 110 may also control transmission and reception, measurement, etc. using the transmission and reception unit 120, the transmission and reception antenna 130, and the transmission path interface 140. The control unit 110 may also generate data, control information, sequences, etc. to be sent as signals, and forward them to the transmission and reception unit 120. The control unit 110 may also perform call processing (setting, release, etc.) of communication channels, state management of the base station 10, management of wireless resources, etc.

[0632] The transmitting and receiving unit 120 may also include a baseband unit 121, a radio frequency (RF) unit 122, and a measuring unit 123. The baseband unit 121 may also include a transmitting processing unit 1211 and a receiving processing unit 1212. The transmitting and receiving unit 120 may be composed of a transmitter / receiver, an RF circuit, a baseband circuit, a filter, a phase shifter (phase shifter), a measuring circuit, a transmitting and receiving circuit, etc., which are described based on the common knowledge in the technical field involved in the present disclosure.

[0633] The transmitting and receiving unit 120 may be configured as an integrated transmitting and receiving unit, or may be configured as a transmitting unit and a receiving unit. The transmitting unit may also be configured as a transmitting processing unit 1211 and an RF unit 122. The receiving unit may also be configured as a receiving processing unit 1212, an RF unit 122, and a measuring unit 123.

[0634] The transmitting / receiving antenna 130 can be constituted by an antenna described based on common knowledge in the technical field involved in the present disclosure, such as an array antenna.

[0635] The transmitting and receiving unit 120 may also transmit the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc. The transmitting and receiving unit 120 may also receive the above-mentioned uplink channel, uplink reference signal, etc.

[0636] The transmitting and receiving unit 120 may also use digital beamforming (eg, precoding), analog beamforming (eg, phase rotation), etc. to form at least one of a transmitting beam and a receiving beam.

[0637] The sending and receiving unit 120 (sending processing unit 1211) may also perform processing on the Packet Data Convergence Protocol (PDCP) layer, processing on the Radio Link Control (RLC) layer (e.g., RLC retransmission control), processing on the Medium Access Control (MAC) layer (e.g., HARQ retransmission control), etc., for data and control information obtained from the control unit 110, to generate a bit string to be sent.

[0638] The transmitting and receiving unit 120 (transmitting processing unit 1211) may also perform channel coding (which may also include error correction coding), modulation, mapping, filter processing (filtering processing), discrete Fourier transform (DFT) processing (as needed), inverse fast Fourier transform (IFFT) processing), precoding, digital-to-analog conversion and other transmission processing on the bit string to be transmitted, and output a baseband signal.

[0639] The transmitting and receiving unit 120 (RF unit 122 ) may also perform modulation, filter processing, amplification, etc. on the baseband signal to a radio frequency band, and transmit the signal in the radio frequency band via the transmitting and receiving antenna 130 .

[0640] On the other hand, the transmission and reception unit 120 (RF unit 122 ) may also perform amplification, filter processing, demodulation into a baseband signal, etc. on the signal in the radio frequency band received by the transmission and reception antenna 130 .

[0641] The transmitting and receiving unit 120 (receiving processing unit 1212) may also apply receiving processing such as analog-to-digital conversion, fast Fourier transform (FFT) processing, inverse discrete Fourier transform (IDFT) processing (as needed), filter processing (filtering processing), demapping, demodulation, decoding (which may also include error correction decoding), MAC layer processing, RLC layer processing, and PDCP layer processing to the acquired baseband signal to obtain user data, etc.

[0642] The transmitting and receiving unit 120 (the measuring unit 123) may also implement measurements related to the received signal. For example, the measuring unit 123 may also perform radio resource management (RRM) measurements, channel state information (CSI) measurements, etc. based on the received signal. The measuring unit 123 may also measure received power (e.g., reference signal received power (RSRP)), received quality (e.g., reference signal received quality (RSRQ), signal to interference plus noise ratio (SINR), signal to noise ratio (SNR)), signal strength (e.g., received signal strength indicator (RSSI)), propagation path information (e.g., CSI), etc. The measurement results may also be output to the control unit 110.

[0643] The transmission path interface 140 can also send and receive signals (return signaling) between devices included in the core network 30 (for example, network nodes providing NF), other base stations 10, etc., and can also obtain and transmit user data (user plane data) and control plane data for the user terminal 20.

[0644] In addition, the transmission unit and the reception unit of the base station 10 in the present disclosure may also be constituted by at least one of the transmission and reception unit 120 , the transmission and reception antenna 130 , and the transmission path interface 140 .

[0645] In addition, the sending and receiving unit 120 may also send a performance indicator for performance monitoring regarding positioning based on artificial intelligence (AI). In addition, the performance indicator may also include information related to at least one of the accuracy, delay, and complexity of positioning. The sending and receiving unit 120 may also send and receive monitoring information including information related to the elements of the performance indicator. The sending and receiving unit 120 may also receive output information related to the positioning of the terminal output from the AI ​​model.

[0646] The control unit 110 may also control the performance monitoring. The control unit 110 may also determine whether a specific operation after the performance monitoring can be performed. The control unit 110 may determine the specific operation based on the monitoring information. The specific operation may be at least one of switching, updating, and rolling back the AI ​​model. The control unit 110 may also control the performance monitoring when more than one AI model is deployed based on specific requirements related to the performance indicator.

[0647] (User terminal)

[0648] Fig.10 2 is a diagram showing an example of a configuration of a user terminal according to an embodiment. The user terminal 20 includes a control unit 210, a transmitting / receiving unit 220, and a transmitting / receiving antenna 230. In addition, the control unit 210, the transmitting / receiving unit 220, and the transmitting / receiving antenna 230 may each be provided with one or more.

[0649] In addition, in this example, the functional blocks of the characteristic parts in this embodiment are mainly shown, and it is also conceivable that the user terminal 20 also has other functional blocks required for wireless communication. Part of the processing of each unit described below may be omitted.

[0650] The control unit 210 controls the entire user terminal 20. The control unit 210 can be composed of a controller, a control circuit, and the like that are described based on common knowledge in the technical field to which the present disclosure relates.

[0651] The control unit 210 may also control signal generation, mapping, etc. The control unit 210 may also control transmission and reception, measurement, etc. using the transmission and reception unit 220 and the transmission and reception antenna 230. The control unit 210 may also generate data, control information, sequences, etc. to be transmitted as signals, and forward them to the transmission and reception unit 220.

[0652] The transmitting and receiving unit 220 may also include a baseband unit 221, an RF unit 222, and a measuring unit 223. The baseband unit 221 may also include a transmitting processing unit 2211 and a receiving processing unit 2212. The transmitting and receiving unit 220 may be composed of a transmitter / receiver, an RF circuit, a baseband circuit, a filter, a phase shifter, a measuring circuit, a transmitting and receiving circuit, etc., which are described based on the common knowledge in the technical field involved in the present disclosure.

[0653] The transmitting and receiving unit 220 may be configured as an integrated transmitting and receiving unit, or may be configured as a transmitting unit and a receiving unit. The transmitting unit may also be configured as a transmitting processing unit 2211 and an RF unit 222. The receiving unit may also be configured as a receiving processing unit 2212, an RF unit 222, and a measuring unit 223.

[0654] The transmitting / receiving antenna 230 can be constituted by an antenna described based on common knowledge in the technical field involved in the present disclosure, such as an array antenna.

[0655] The transmitting and receiving unit 220 may also receive the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc. The transmitting and receiving unit 220 may also transmit the above-mentioned uplink channel, uplink reference signal, etc.

[0656] The transmitting and receiving unit 220 may also use digital beamforming (eg, precoding), analog beamforming (eg, phase rotation), etc. to form at least one of a transmitting beam and a receiving beam.

[0657] The sending and receiving unit 220 (sending processing unit 2211) may also perform PDCP layer processing, RLC layer processing (e.g., RLC retransmission control), MAC layer processing (e.g., HARQ retransmission control), etc. on the data, control information, etc. obtained from the control unit 210 to generate a bit string to be sent.

[0658] The transmitting and receiving unit 220 (transmitting processing unit 2211) can also perform channel coding (which may also include error correction coding), modulation, mapping, filter processing (filtering processing), DFT processing (as needed), IFFT processing, precoding, digital-to-analog conversion and other transmission processing on the bit string to be sent, and output a baseband signal.

[0659] In addition, whether to apply DFT processing may also be based on the setting of transform precoding. For a certain channel (e.g., PUSCH), when transform precoding is valid (enabled), the transmitting and receiving unit 220 (transmitting processing unit 2211) may also perform DFT processing as the above-mentioned transmission processing in order to transmit the channel using a DFT-s-OFDM waveform. Otherwise, the transmitting and receiving unit 220 (transmitting processing unit 2211) may also perform DFT processing as the above-mentioned transmission processing without performing DFT processing.

[0660] The transmitting and receiving unit 220 (RF unit 222 ) may perform modulation, filter processing (filtering processing), amplification, etc. on the baseband signal to a radio frequency band, and transmit the signal in the radio frequency band via the transmitting and receiving antenna 230 .

[0661] On the other hand, the transmission and reception unit 220 (RF unit 222 ) may perform amplification, filter processing (filter processing), demodulation into a baseband signal, etc. on the signal in the radio frequency band received by the transmission and reception antenna 230 .

[0662] The sending and receiving unit 220 (receiving processing unit 2212) can also apply receiving processing such as analog-to-digital conversion, FFT processing, IDFT processing (as needed), filter processing (filtering processing), demapping, demodulation, decoding (which may also include error correction decoding), MAC layer processing, RLC layer processing and PDCP layer processing to the obtained baseband signal to obtain user data, etc.

[0663] The transmitting and receiving unit 220 (measuring unit 223) may also perform measurements related to the received signal. For example, the measuring unit 223 may also perform RRM measurement, CSI measurement, etc. based on the received signal. The measuring unit 223 may also measure the received power (e.g., RSRP), the received quality (e.g., RSRQ, SINR, SNR), the signal strength (e.g., RSSI), the propagation path information (e.g., CSI), etc. The measurement results may also be output to the control unit 210.

[0664] In addition, the transmitting unit and the receiving unit of the user terminal 20 in the present disclosure may also be composed of at least one of the transmitting and receiving unit 220 and the transmitting and receiving antenna 230 .

[0665] In addition, the sending and receiving unit 220 may also receive a performance indicator for performance monitoring regarding positioning based on artificial intelligence (AI). In addition, the performance indicator may also include information related to at least one of the accuracy, delay, and complexity of positioning. The sending and receiving unit 220 may also report (send) or receive monitoring information including information related to the elements of the performance indicator. The sending and receiving unit 220 may also report output information related to the positioning of the terminal output from the AI ​​model.

[0666] The control unit 210 may also control the performance monitoring. The control unit 210 may also determine whether a specific operation after the performance monitoring can be performed. The control unit 210 may also determine the specific operation based on the monitoring information. The specific operation may be at least one of switching, updating, and rolling back the AI ​​model. The control unit 210 may also control the performance monitoring when more than one AI model is deployed based on specific requirements related to the performance indicator.

[0667] (Hardware structure)

[0668] In addition, the block diagram used in the description of the above-mentioned embodiment shows a block of a functional unit. These functional blocks (structural units) are implemented by any combination of at least one of hardware and software. In addition, the implementation method of each functional block is not particularly limited. That is, each functional block can be implemented by a device that is physically or logically combined, or two or more physically or logically separated devices can be directly or indirectly connected (for example, by wire, wireless, etc.) and implemented by these multiple devices. The functional block can also be implemented by combining the above-mentioned one device or the above-mentioned multiple devices with software.

[0669] Here, the functions include judging, deciding, determining, calculating, calculating, processing, deriving, investigating, searching, confirming, receiving, sending, outputting, accessing, solving, selecting, selecting, establishing, comparing, assuming, expecting, regarding, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, assigning, etc., but are not limited to these. For example, a functional block (structural unit) that implements a sending function may also be referred to as a transmitting unit, a transmitter, etc. Any one of them is as described above, and the implementation method thereof is not particularly limited.

[0670] For example, a base station, a user terminal, etc. in one embodiment of the present disclosure may also function as a computer that performs processing of the wireless communication method of the present disclosure. Fig.11 1001, memory 1002, storage 1003, communication device 1004, input device 1005, output device 1006, bus 1007, etc.

[0671] In addition, in the present disclosure, the terms such as device, circuit, equipment, section, and unit can be interchanged. The hardware configuration of the base station 10 and the user terminal 20 may include one or more of the devices shown in the figure, or may exclude some of the devices.

[0672] For example, only one processor 1001 is shown, but there may be multiple processors. In addition, the processing may be performed by one processor, or may be performed by two or more processors simultaneously, sequentially, or in other ways. In addition, the processor 1001 may also be implemented by one or more chips.

[0673] The functions of the base station 10 and the user terminal 20 are realized, for example, by reading specific software (program) into hardware such as the processor 1001 and the memory 1002, so that the processor 1001 performs calculations and controls communication via the communication device 1004, or controls at least one of the reading and writing of data in the memory 1002 and the storage 1003.

[0674] The processor 1001 controls the entire computer by, for example, operating an operating system. The processor 1001 may also be composed of a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic device, a register, etc. For example, at least a part of the control unit 110 (210), the transmission and reception unit 120 (220), etc. described above may also be implemented by the processor 1001.

[0675] In addition, the processor 1001 reads a program (program code), a software module, data, etc. from at least one of the storage 1003 and the communication device 1004 to the memory 1002, and performs various processes based on them. As a program, a program that causes a computer to perform at least a part of the operations described in the above-mentioned embodiments can be used. For example, the control unit 110 (210) can also be implemented by a control program stored in the memory 1002 and operated in the processor 1001, and the other functional blocks can also be implemented in the same way.

[0676] The memory 1002 may also be a computer-readable recording medium, for example, composed of at least one of a read-only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a random access memory (RAM), or other appropriate storage media. The memory 1002 may also be referred to as a register, a cache, a main memory (main storage device), etc. The memory 1002 can store executable programs (program codes), software modules, etc. for implementing the wireless communication method involved in one embodiment of the present disclosure.

[0677] The storage 1003 may also be a computer-readable recording medium, such as a flexible disk, a floppy disk, an optical disk (such as a compact disk (Compact Disc ROM (CD-ROM)), a digital versatile disk, a Blu-ray (Blu-ray) (registered trademark) disk), a removable disk, a hard disk drive, a smart card, a flash memory device (such as a card, a stick, a key drive), a magnetic stripe, a database, a server, or at least one of other appropriate storage media. The storage 1003 may also be referred to as an auxiliary storage device.

[0678] The communication device 1004 is hardware (transmitting and receiving device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as a network device, a network controller, a network card, a communication module, etc. In order to realize at least one of frequency division duplex (Frequency Division Duplex (FDD)) and time division duplex (Time Division Duplex (TDD)), the communication device 1004 may also be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc. For example, the above-mentioned transmitting and receiving unit 120 (220), the transmitting and receiving antenna 130 (230), etc. may also be realized by the communication device 1004. The transmitting and receiving unit 120 (220) may also be realized by physically or logically separating the transmitting unit 120a (220a) and the receiving unit 120b (220b).

[0679] The input device 1005 is an input device that receives input from the outside (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.). The output device 1006 is an output device that performs output to the outside (e.g., a display, a speaker, a light emitting diode (LED) lamp, etc.). In addition, the input device 1005 and the output device 1006 may also be an integrated structure (e.g., a touch panel).

[0680] In addition, the processor 1001, the memory 1002 and other devices are connected via a bus 1007 for communicating information. The bus 1007 may be configured as a single bus or may be configured as different buses between the devices.

[0681] In addition, the base station 10 and the user terminal 20 may also be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), and a field programmable gate array (FPGA), and the hardware may be used to implement a part or all of each functional block. For example, the processor 1001 may also be implemented using at least one of these hardware.

[0682] (Variation)

[0683] In addition, the terms described in the present disclosure and the terms required for understanding the present disclosure may also be replaced with terms having the same or similar meanings. For example, channels, code elements, and signals (signals or signaling) may be rewritten one another. In addition, a signal may also be a message. A reference signal may also be referred to as RS, and may also be referred to as a pilot, a pilot signal, etc. depending on the applied standard. In addition, a component carrier (CC) may also be referred to as a cell, a frequency carrier, a carrier frequency, etc.

[0684] A radio frame may also be composed of one or more periods (frames) in the time domain. Each period (frame) of the one or more periods (frames) constituting a radio frame may also be referred to as a subframe. Further, a subframe may also be composed of one or more time slots in the time domain. A subframe may also be a fixed time length (e.g., 1 ms) that is not dependent on a parameter set (numerology).

[0685] Here, the parameter set may also be a communication parameter applied in at least one of the transmission and reception of a certain signal or channel. For example, the parameter set may also represent at least one of the subcarrier spacing (SCS), bandwidth, symbol length, cyclic prefix length, transmission time interval (TTI), the number of symbols per TTI, wireless frame structure, specific filter processing performed by the transmitter and receiver in the frequency domain, specific windowing processing performed by the transmitter and receiver in the time domain, etc.

[0686] A time slot may also be composed of one or more symbols (Orthogonal Frequency Division Multiplexing (OFDM) symbols, Single Carrier Frequency Division Multiple Access (SC-FDMA) symbols, etc.) in the time domain. In addition, a time slot may also be a time unit based on a parameter set.

[0687] A time slot may also contain multiple mini-slots. Each mini-slot may also be composed of one or more symbols in the time domain. In addition, a mini-slot may also be referred to as a sub-slot. A mini-slot may also be composed of a smaller number of symbols than a time slot. A PDSCH (or PUSCH) transmitted in a time unit larger than a mini-slot may also be referred to as a PDSCH (PUSCH) mapping type A. A PDSCH (or PUSCH) transmitted using a mini-slot may also be referred to as a PDSCH (PUSCH) mapping type B.

[0688] Radio frames, subframes, time slots, mini-time slots, and symbols all represent time units for transmitting signals. Radio frames, subframes, time slots, mini-time slots, and symbols may also be referred to by their respective names. In addition, time units such as frames, subframes, time slots, mini-time slots, and symbols in the present disclosure may also be replaced with each other.

[0689] For example, a subframe may be referred to as a TTI, a plurality of consecutive subframes may be referred to as a TTI, and a time slot or a mini time slot may be referred to as a TTI. That is, at least one of a subframe and a 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. In addition, the unit representing a TTI may be referred to as a time slot, a mini time slot, etc. instead of a subframe.

[0690] Here, TTI refers to, for example, the minimum time unit for scheduling in wireless communication. For example, in the LTE system, the base station schedules each user terminal to allocate wireless resources (frequency bandwidth, transmission power, etc. that can be used in each user terminal) in TTI units. In addition, the definition of TTI is not limited to this.

[0691] TTI can also be a transmission time unit for a data packet (transport block), code block, code word, etc. that has been channel-coded, and can also be a processing unit for scheduling, link adaptation, etc. In addition, when TTI is given, the time interval (for example, the number of symbols) to which a transport block, code block, code word, etc. is actually mapped can also be shorter than the TTI.

[0692] In addition, when a time slot or a mini time slot is called a TTI, one or more TTIs (i.e., one or more time slots or one or more mini time slots) may also be the minimum time unit of scheduling. In addition, the number of time slots (mini time slots) constituting the minimum time unit of scheduling may also be controlled.

[0693] A TTI having a time length of 1 ms may also be referred to as a normal TTI (TTI in 3GPP Rel.8-12), a standard TTI, a long TTI, a normal subframe, a standard subframe, a long subframe, a time slot, etc. A TTI shorter than a normal TTI may also be referred to as a shortened TTI, a short TTI, a partial TTI (partial or fractional TTI), a shortened subframe, a short subframe, a mini time slot, a sub time slot, a time slot, etc.

[0694] In addition, a long TTI (eg, normal TTI, subframe, etc.) may be rewritten as a TTI having a time length exceeding 1 ms, and a short TTI (eg, shortened TTI, etc.) may be rewritten as a TTI having a TTI length shorter than that of a long TTI and longer than 1 ms.

[0695] Resource Block (RB) is a resource allocation unit in the time domain and frequency domain, and may also include one or more consecutive subcarriers (subcarriers) in the frequency domain. The number of subcarriers included in an RB may also be the same regardless of the parameter set, for example, it may be 12. The number of subcarriers included in an RB may also be determined based on the parameter set.

[0696] In addition, an RB may also include one or more symbols in the time domain, and may also be the length of a slot, a mini-slot, a subframe, or a TTI. A TTI, a subframe, etc. may also be composed of one or more resource blocks, respectively.

[0697] In addition, one or more RBs may also be referred to as a physical resource block (Physical RB (PRB)), a sub-carrier group (Sub-Carrier Group (SCG)), a resource element group (Resource Element Group (REG)), a PRB pair, an RB pair, etc.

[0698] In addition, a resource block may be composed of one or more resource elements (RE). For example, one RE may be a radio resource region of one subcarrier and one symbol.

[0699] Bandwidth Part (BWP) (also referred to as partial bandwidth, etc.) can also represent a subset of contiguous common RBs (common resource blocks) for a parameter set in a carrier. Here, common RBs can also be identified by the index of the RB based on the common reference point of the carrier. PRBs can also be defined in a BWP and numbered within the BWP.

[0700] The BWP may include a UL BWP (BWP for UL) and a DL BWP (BWP for DL). For a UE, one or more BWPs may be configured in one carrier.

[0701] At least one of the set BWPs may be activated, and the UE may not assume that a specific signal / channel is transmitted or received outside the activated BWP. In addition, "cell", "carrier", etc. in the present disclosure may also be rewritten as "BWP".

[0702] In addition, the above-mentioned structures such as radio frames, subframes, time slots, mini-time slots and symbols are only examples. For example, the number of subframes included in a radio frame, the number of time slots per subframe or radio frame, the number of mini-time slots included in a time slot, the number of symbols and RBs included in a time slot or mini-time slot, the number of subcarriers included in an RB, the number of symbols in a TTI, the symbol length, and the cyclic prefix (CP) length can be changed in various ways.

[0703] In addition, the information, parameters, etc. described in the present disclosure may be represented by absolute values, relative values ​​relative to a specific value, or other corresponding information. For example, wireless resources may also be indicated by a specific index.

[0704] In the present disclosure, the names used for parameters, etc. are not limiting in all respects. Furthermore, the mathematical formulas, etc. using these parameters may be different from those explicitly disclosed in the present disclosure. The various channels (PUCCH, PDCCH, etc.) and information elements can be identified by any suitable names, and therefore, the various names assigned to these various channels and information elements are not limiting in all respects.

[0705] Information, signals, etc. described in the present disclosure may also be represented using any of a variety of different techniques. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be mentioned throughout the above description may also be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, light fields or photons, or any combination thereof.

[0706] In addition, information, signals, etc. can be output in at least one of the following directions: from a higher layer (upper layer) to a lower layer (lower layer), and from a lower layer to a higher layer. Information, signals, etc. can also be input and output via multiple network nodes.

[0707] The input and output information, signals, etc. may be stored in a specific location (e.g., a memory), or may be managed using a management table. The input and output information, signals, etc. may be overwritten, updated, or appended. The output information, signals, etc. may also be deleted. The input information, signals, etc. may also be sent to other devices.

[0708] The notification of information is not limited to the methods / implementations described in the present disclosure, and may also be performed by other methods. For example, the notification of information in the present disclosure may also be implemented through physical layer signaling (e.g., downlink control information (Downlink Control Information (DCI)), uplink control information (Uplink Control Information (UCI))), high-layer signaling (e.g., Radio Resource Control (RRC) signaling, broadcast information (Master Information Block (MIB)), System Information Block (SIB), etc.), Medium Access Control (MAC) signaling), other signals, or a combination thereof.

[0709] In addition, physical layer signaling may also be referred to as layer 1 / layer 2 (Layer 1 / Layer 2 (L1 / L2)) control information (L1 / L2 control signal), L1 control information (L1 control signal), etc. In addition, RRC signaling may also be referred to as an RRC message, such as an RRC connection establishment (RRC Connection Setup) message, an RRC connection reconstruction (RRC Connection Reconfiguration) message, etc. In addition, MAC signaling may also be notified using, for example, a MAC control element (MACControl Element (CE)).

[0710] Furthermore, notification of specific information (eg, notification of “it is X”) is not limited to explicit notification, but may be performed implicitly (eg, by not notifying the specific information or by notifying other information).

[0711] The determination may be made using a value represented by one bit (0 or 1), a true or false value (Boolean value) represented by true or false, or by comparison of numerical values ​​(eg, comparison with a specific value).

[0712] Whether software is called software, firmware, middle-ware, microcode, hardware description language, or other names, it should be broadly interpreted as meaning instructions, instruction sets, code, code segments, program code, program, sub-program, software module, application, software application, software package, routine, sub-routine, object, executable files, execution thread, procedure, function, etc.

[0713] In addition, software, instructions, information, etc. may also be sent and received via a transmission medium. For example, when the software is sent from a website, server, or other remote source using at least one of wired technology (coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), etc.) and wireless technology (infrared, microwave, etc.), at least one of these wired technology and wireless technology is included in the definition of transmission medium.

[0714] The terms "system" and "network" used in the present disclosure can be used interchangeably. "Network" may also refer to a device (eg, a base station) included in the network.

[0715] In the present 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", "antenna port group", "layer", "number of layers", "rank", "resource", "resource set", "resource group", "beam", "beam width", "beam angle", "antenna", "antenna element", and "panel" can be used interchangeably.

[0716] In the present disclosure, terms such as "base station (BS)", "wireless base station", "fixed station", "NodeB", "eNB (eNodeB)", "gNB (gNodeB)", "access point", "transmission point (TP)", "reception point (RP)", "transmission / reception point (TRP)")", "panel", "cell", "sector", "cell group", "carrier", and "component carrier" can be used interchangeably. There are also cases where base stations are referred to by terms such as macro cell, small cell, micro cell, and pico cell.

[0717] A base station can accommodate one or more (for example, three) cells. When a base station accommodates multiple cells, the overall coverage area of ​​the base station can be divided into multiple smaller areas, and each smaller area can also provide communication services through a base station subsystem (for example, a small base station for indoor use (Remote Radio Head (RRH))). Terms such as "cell" or "sector" refer to a part or the entirety of the coverage area of ​​at least one of a base station and a base station subsystem that provides communication services within the coverage area.

[0718] In the present disclosure, the base station sending information to the terminal may also be rewritten with the base station instructing the terminal to control / operate based on the information.

[0719] In the present disclosure, terms such as “mobile station (MS)”, “user terminal”, “user device (User Equipment (UE))”, and “terminal” can be used interchangeably.

[0720] 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, hand set, user agent, mobile client, client or several other appropriate terms.

[0721] At least one of the base station and the mobile station may also be referred to as a transmission device, a reception device, a wireless communication device, etc. In addition, at least one of the base station and the mobile station may be a device mounted on a moving object, a moving object body, etc.

[0722] The mobile body refers to a movable object, and the moving speed is arbitrary, and of course it also includes the situation where the mobile body stops. The mobile body includes, for example, vehicles, transport vehicles, cars, automatic two-wheeled vehicles (motorcycles), bicycles, connected cars, loading shovels, bulldozers, wheel loaders, dump trucks, forklifts, trains, buses, trolleys, rickshaws, ships (ships and other watercraft), airplanes, rockets, artificial satellites, drones, multicopters, quadcopters, balloons, and objects carried on them, but are not limited to these. In addition, the mobile body can also be a mobile body that drives autonomously based on operating instructions.

[0723] The mobile object may be a means of transportation (e.g., a vehicle, an airplane, etc.), a mobile object that moves unmanned (e.g., a drone, an autonomous vehicle, etc.), or a robot (manned or unmanned). In addition, at least one of the base station and the mobile station may include a device that does not necessarily move when performing communication operations. For example, at least one of the base station and the mobile station may also be an Internet of Things (IoT) device such as a sensor.

[0724] Fig.121 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, an axle 48, an electronic control unit 49, various sensors (including a current sensor 50, a rotation speed 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.

[0725] The driving unit 41 is composed of at least one of an engine, an electric motor, and a hybrid of an engine and an electric motor. The steering unit 42 is composed of at least a steering wheel (also called a steering wheel), and steers 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.

[0726] The electronic control unit 49 is composed of a microprocessor 61, a memory (ROM, RAM) 62, and a communication port (for example, an input / output (IO) port) 63. Signals from various sensors 50-58 provided in the vehicle are input to the electronic control unit 49. The electronic control unit 49 may also be referred to as an electronic control unit (ECU).

[0727] The signals from the various sensors 50-58 include a current signal from a current sensor 50 for sensing the current of the motor, a speed signal of the front wheels 46 / rear wheels 47 obtained by a 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 stepping amount signal of the accelerator pedal 43 obtained by an accelerator pedal sensor 55, a stepping 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, a detection signal for detecting obstacles, vehicles, pedestrians, etc. obtained by an object detection sensor 58, and the like.

[0728] The information service unit 59 is composed of various devices such as a vehicle navigation system, an audio system, a speaker, 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 obtained from an external device via the communication module 60, etc. to provide various information / services (for example, multimedia information / multimedia services) to the occupants of the vehicle 40.

[0729] The information service unit 59 may include input devices for accepting input from the outside (e.g., keyboard, mouse, microphone, switch, button, sensor, touch panel, etc.), and may also include output devices for implementing output to the outside (e.g., display, speaker, LED light, touch panel, etc.).

[0730] The driving assistance system unit 64 is composed of various devices for providing functions for preventing accidents or reducing the driving load of the driver, such as millimeter wave radar, light detection and ranging (LiDAR), camera, positioning locator (e.g., Global Navigation Satellite System (GNSS)), map information (e.g., high-precision (High Definition (HD))) map, autonomous vehicle (Autonomous Vehicle (AV)) map, etc.), gyroscope system (e.g., inertial measurement unit (Inertial Measurement Unit (IMU)), inertial navigation unit (Inertial Navigation System (INS)), etc.), artificial intelligence (Artificial Intelligence (AI)) chip, AI processor, and one or more ECUs for controlling these devices. In addition, the driving assistance system unit 64 sends and receives various information via the communication module 60 and realizes the driving assistance function or the autonomous driving function.

[0731] The communication module 60 can communicate with the microprocessor 61 and the structural elements of the vehicle 40 via the communication port 63. For example, the communication module 60 transmits and receives data (information) with the drive unit 41, the steering unit 42, the accelerator pedal 43, the brake pedal 44, the shift lever 45, the left and right front wheels 46, the left and right rear wheels 47, the axle 48, the microprocessor 61 and the memory (ROM, RAM) 62 in the electronic control unit 49, and various sensors 50-58 of the vehicle 40 via the communication port 63.

[0732] 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, various information is sent and received with the external device via wireless communication. The communication module 60 can be located either inside or outside the electronic control unit 49. The external device can also be, for example, the above-mentioned base station 10, user terminal 20, etc. In addition, the communication module 60 can also be, for example, at least one of the above-mentioned base station 10 and user terminal 20 (it can also function as at least one of the base station 10 and user terminal 20).

[0733] The communication module 60 may also transmit at least one of the signals from the 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 also be referred to as an input unit that receives input. For example, the PUSCH transmitted through the communication module 60 may also include information based on the above input.

[0734] The communication module 60 receives various information (traffic information, signal information, vehicle-to-vehicle information, etc.) transmitted from an external device, and displays it to the information service unit 59 provided in the vehicle. The information service unit 59 may also be referred to as an output unit for outputting information (for example, outputting information to a display, a speaker, etc. based on the PDSCH received by the communication module 60 (or data / information decoded from the PDSCH)).

[0735] In addition, the communication module 60 stores various information received from the external device in the memory 62 that can be used by the microprocessor 61. The microprocessor 61 can also control the drive unit 41, the steering unit 42, the accelerator pedal 43, the brake pedal 44, the shift lever 45, the left and right front wheels 46, the left and right rear wheels 47, the axle 48, and various sensors 50-58 of the vehicle 40 based on the information stored in the memory 62.

[0736] In addition, the base station in the present disclosure may also be rewritten as a user terminal. For example, the various methods / implementations of the present disclosure may also be applied to a structure in which the communication between a base station and a user terminal is replaced by the communication between multiple user terminals (for example, it may also be referred to as device-to-device (D2D)), vehicle-to-everything (V2X), etc.). In this case, it may also be set as a structure in which the user terminal 20 has the functions possessed by the above-mentioned base station 10. In addition, terms such as "uplink" and "downlink" may also be rewritten as terms corresponding to inter-terminal communication (for example, "sidelink"). For example, uplink channels, downlink channels, etc. may also be rewritten as sidelink channels.

[0737] Likewise, the user terminal in the present disclosure may be rewritten as a base station. In this case, the base station 10 may have the functions of the user terminal 20 described above.

[0738] In the present disclosure, the actions are assumed to be performed by the base station, and sometimes by its upper node depending on the situation. Obviously, in a network including one or more network nodes having a base station, various operations performed for communication with a terminal can be performed by the base station, one or more network nodes other than the base station (for example, Mobility Management Entity (MME)), Serving-Gateway (S-GW), etc., but not limited to these), or a combination thereof.

[0739] Each method / implementation method described in this disclosure may be used alone or in combination, and may be switched as the method is executed. In addition, the processing procedures, timings, flow charts, etc. of each method / implementation method described in this disclosure may be swapped in order as long as they are not contradictory. For example, for the method described in this disclosure, the elements of various steps are presented in an illustrative order, but are not limited to the specific order presented.

[0740] The various modes / implementations described in the present disclosure may be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 6th generation mobile communication system (6G), xth generation mobile communication system (xG (x is, for example, an integer or a decimal)), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New radio access (NX), 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 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), systems using other appropriate wireless communication methods, and next-generation systems that are enhanced, modified, produced or specified based on them. In addition, multiple systems can also be combined (for example, LTE or LTE-A, combination with 5G, etc.) for application.

[0741] The phrase “based on” used in the present disclosure does not mean “based only on” unless otherwise specified. In other words, the phrase “based on” means both “based only on” and “based at least on”.

[0742] Any reference to an element using the terms "first", "second", etc. used in this disclosure does not fully define the amount or order of these elements. These terms can be used in this disclosure as a convenient method to distinguish between two or more elements. Therefore, reference to the first and second elements does not mean that only two elements can be used or that the first element must take precedence over the second element in some form.

[0743] The term "determining" used in this disclosure may include a variety of actions. For example, "determining" may also refer to situations where judging, calculating, computing, processing, deriving, investigating, looking up (e.g., searching in a table, database or other data structure), ascertaining, etc. are considered to be "determining".

[0744] In addition, “judgment (decision)” may also refer to situations where receiving (e.g., receiving information), transmitting (e.g., sending information), input (input), output (output), accessing (e.g., accessing data in a memory), etc. are regarded as making a “judgment (decision)”.

[0745] In addition, "judgment (decision)" can also be regarded as a situation where resolving, selecting, choosing, establishing, comparing, etc. are regarded as "judgment (decision)". That is, "judgment (decision)" can also be regarded as a situation where some actions are regarded as "judgment (decision)".

[0746] In addition, "judge (decide)" can also be rewritten as "assuming (assuming)", "expecting (expecting)", "considering (considering)" and so on.

[0747] The “maximum transmit power” described in the present disclosure may refer to the maximum value of the transmit power, the nominal maximum transmit power (the nominal UE maximum transmit power), or the rated maximum transmit power (the rated UE maximum transmit power).

[0748] The terms "connected", "coupled", or all their variations used in this disclosure refer to all direct or indirect connections or combinations between two or more elements, and may include the situation where one or more intermediate elements exist between two elements that are "connected" or "coupled" to each other. The combination or connection between elements may be physical, logical, or a combination thereof. For example, "connection" may also be rewritten as "access".

[0749] In the present disclosure, when two elements are connected, it is possible to consider being "connected" or "combined" to each other using one or more wires, cables, printed electrical connections, etc., and as several non-limiting and non-inclusive examples, using electromagnetic energy with a wavelength in the wireless frequency domain, microwave region, light (both visible and invisible) region, etc.

[0750] In the present disclosure, the term "A is different from B" may also mean "A and B are different from each other". In addition, the term may also mean "A and B are different from C, respectively". The terms "separate" and "combined" may also be interpreted in the same manner as "different".

[0751] When the terms “include,” “including,” and variations thereof are used in the present disclosure, these terms, like the term “comprising,” have an inclusive meaning. Furthermore, the term “or” used in the present disclosure does not have an exclusive OR meaning.

[0752] In the present disclosure, when an article is added by translation like a, an, and the in English, for example, the present disclosure may also include a case where the noun following the article is in plural form.

[0753] In the present disclosure, “below”, “less than”, “above”, “more than”, “equal to”, etc. may be rephrased with each other. Furthermore, in the present disclosure, terms meaning “good”, “bad”, “big”, “small”, “high”, “low”, “early”, “late”, “wide”, “narrow”, etc. are not limited to the original level, comparative level and superlative level, but may be rephrased with each other. Furthermore, in the present disclosure, terms meaning “good”, “bad”, “big”, “small”, “high”, “low”, “early”, “late”, “wide”, “narrow”, etc. may be rephrased with each other as expressions with “ith” (i is an arbitrary integer) added, and are not limited to the original level, comparative level and superlative level (for example, “highest” may be rephrased with “i-th highest”).

[0754] In the present disclosure, “of”, “for”, “regarding”, “related to”, “associated with”, etc. may also be replaced by each other.

[0755] The invention involved in the present disclosure has been described in detail above, but it is obvious to those skilled in the art that the invention involved in the present disclosure is not limited to the embodiments described in the present disclosure. The invention involved in the present disclosure can be implemented as a modified and altered mode without departing from the gist and scope of the invention determined based on the description of the claims. Therefore, the description of the present disclosure is for the purpose of illustrative description and does not have any limiting meaning on the invention involved in the present disclosure.

Claims

1. A terminal having: a receiving unit, for receiving a performance indicator for performance monitoring based on artificial intelligence (AI) positioning; and a control unit for controlling the performance monitoring, The control unit determines whether a specific operation after the performance monitoring can be performed.

2. The terminal according to claim 1, wherein: The receiving unit receives monitoring information including information related to the requirements of the performance indicator. The control unit determines the specific operation based on the monitoring information.

3. The terminal according to claim 1, wherein: The specific operation is at least one of switching, updating, and rolling back of the AI ​​model.

4. The terminal according to claim 1, wherein: The control unit controls the performance monitoring when more than one AI model is deployed based on specific requirements related to the performance indicator.

5. A wireless communication method, which is a wireless communication method of a terminal, comprising: Regarding artificial intelligence (AI) based positioning, the step of receiving performance indicators for performance monitoring; controlling said performance monitoring step; and The step of determining whether a specific operation after the performance monitoring can be performed.

6. A base station, comprising: a sending unit, for sending a performance indicator for performance monitoring with respect to artificial intelligence (AI) based positioning; and a control unit for controlling the performance monitoring, The control unit determines whether a specific operation after the performance monitoring can be performed.

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