WO2023228382A1 - Terminal, procédé de communication radio et station de base - Google Patents

Terminal, procédé de communication radio et station de base Download PDF

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Publication number
WO2023228382A1
WO2023228382A1 PCT/JP2022/021638 JP2022021638W WO2023228382A1 WO 2023228382 A1 WO2023228382 A1 WO 2023228382A1 JP 2022021638 W JP2022021638 W JP 2022021638W WO 2023228382 A1 WO2023228382 A1 WO 2023228382A1
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information
model
base station
terminal
positioning
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PCT/JP2022/021638
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English (en)
Japanese (ja)
Inventor
春陽 越後
浩樹 原田
チーピン ピ
リュー リュー
ジン ワン
ラン チン
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株式会社Ｎｔｔドコモ
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Priority to PCT/JP2022/021638 priority Critical patent/WO2023228382A1/fr
Publication of WO2023228382A1 publication Critical patent/WO2023228382A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Definitions

  • the present disclosure relates to a terminal, a wireless communication method, and a base station in a next-generation mobile communication system.
  • LTE Long Term Evolution
  • 3GPP Rel. 10-14 LTE-Advanced (3GPP Rel. 10-14) has been specified for the purpose of further increasing capacity and sophistication of LTE (Third Generation Partnership Project (3GPP) Releases (Rel.) 8 and 9).
  • LTE Long Term Evolution
  • 5G 5th generation mobile communication system
  • 5G+ plus
  • NR New Radio
  • E-UTRA Evolved Universal Terrestrial Radio Access
  • E-UTRAN Evolved Universal Terrestrial Radio Access Network
  • AI artificial intelligence
  • ML machine learning
  • UE User Equipment
  • one of the purposes of the present disclosure is to provide a terminal, a wireless communication method, and a base station that can realize suitable overhead reduction/channel estimation/resource utilization.
  • a terminal includes a control unit that inputs information into an Artificial Intelligence (AI) model and obtains an output, and a transmitting unit that transmits information regarding the location of the terminal based on the output.
  • AI Artificial Intelligence
  • suitable overhead reduction/channel estimation/resource utilization can be achieved.
  • FIG. 1 is a diagram illustrating an example of an AI model management framework.
  • FIG. 2 is a diagram illustrating an example of specifying an AI model.
  • FIG. 3 is a diagram illustrating an example of a UE positioning method.
  • FIG. 4 is a diagram illustrating an example of a UE positioning method.
  • FIG. 5 is a diagram illustrating an example of a UE positioning method.
  • FIG. 6 is a diagram illustrating an example of a UE positioning method.
  • FIG. 7 is a diagram illustrating an example of a UE positioning method in option 1-1 of the first embodiment.
  • FIG. 8 is a diagram illustrating an example of a UE positioning method in option 1-2 of the first embodiment.
  • FIG. 9 is a diagram illustrating an example of a UE positioning method in option 1-3 of the first embodiment.
  • FIG. 10 is a diagram illustrating an example of the UE positioning method in option 1-4 of the first embodiment.
  • FIG. 11 is a diagram illustrating an example of a UE positioning method in option 1-5 of the first embodiment.
  • FIG. 12 is a diagram illustrating an example of a schematic configuration of a wireless communication system according to an embodiment.
  • FIG. 13 is a diagram illustrating an example of the configuration of a base station according to an embodiment.
  • FIG. 14 is a diagram illustrating an example of the configuration of a user terminal according to an embodiment.
  • FIG. 15 is a diagram illustrating an example of the hardware configuration of a base station and a user terminal according to an embodiment.
  • FIG. 16 is a diagram illustrating an example of a vehicle according to an embodiment.
  • AI Artificial Intelligence
  • ML machine learning
  • improved Channel State Information Reference Signal e.g., reduced overhead, improved accuracy, prediction
  • improved beam management e.g., improved accuracy, time
  • positioning e.g., position estimation/prediction in the spatial domain
  • position measurement e.g., position estimation/prediction
  • FIG. 1 is a diagram illustrating an example of an AI model management framework.
  • each stage related to the AI model is shown as a block.
  • This example is also expressed as AI model life cycle management.
  • the data collection stage corresponds to the stage of collecting data for generating/updating an AI model.
  • the data collection stage includes data reduction (e.g., deciding which data to transfer for model training/model inference), data transfer (e.g., to entities performing model training/model inference (e.g., UE, gNB)), and transfer data).
  • model training is performed based on the data (training data) transferred from the collection stage.
  • This stage includes data preparation (e.g., performing data preprocessing, cleaning, formatting, transformation, etc.), model training/validation, and model testing (e.g., ensuring that the trained model meets performance thresholds).
  • model exchange e.g., transferring a model for distributed learning
  • model deployment/updating deploying/updating a model to entities performing model inference
  • model inference is performed based on the data (inference data) transferred from the collection stage.
  • This stage includes data preparation (e.g., performing data preprocessing, cleaning, formatting, transformation, etc.), model inference, model monitoring (e.g., monitoring the performance of model inference), and model performance feedback (the entity performing model training). (feedback of model performance to actors), output (provide model output to actors), etc.
  • the actor stage includes action triggers (e.g., deciding whether to trigger an action on other entities), feedback (e.g., feeding back information necessary for training data/inference data/performance feedback), etc. May include.
  • action triggers e.g., deciding whether to trigger an action on other entities
  • feedback e.g., feeding back information necessary for training data/inference data/performance feedback
  • training of a model for mobility optimization may be performed, for example, in Operation, Administration and Maintenance (Management) (OAM) in a network (Network (NW)) / gNodeB (gNB).
  • OAM Operation, Administration and Maintenance
  • NW Network
  • gNodeB gNodeB
  • the former has advantages in interoperability, large storage capacity, operator manageability, and model flexibility (e.g., feature engineering). In the latter case, the advantage is that there is no need for model update latency or data exchange for model development.
  • Inference of the above model may be performed in the gNB, for example.
  • the entity that performs training/inference may be different.
  • the OAM/gNB may perform model training and the gNB may perform model inference.
  • a Location Management Function may perform model training, and the LMF may perform model inference.
  • the OAM/gNB/UE may perform model training and the gNB/UE (jointly) may perform model inference.
  • the OAM/gNB/UE may perform model training and the UE may perform model inference.
  • Identifier (ID)-based model approaches can be one way to manage AI models in such scenarios.
  • the NW/gNB does not know the details of the AI model, but may only know some information about the AI model (for example, which ML model is used for what purpose in the UE) for AI model management. I can do it.
  • FIG. 2 is a diagram showing an example of specifying an AI model.
  • the UE and NW eg, base station (BS)
  • NW eg, base station (BS)
  • the UE may report, for example, the performance of model #1 and the performance of model #2 to the NW, and the NW may instruct the UE about the AI model to use.
  • Fingerprinting localization which uses the propagation characteristics of wireless signals to estimate the location of wireless devices, is widely used in both Line Of Site (LOS) and Non-Line Of Site (NLOS) scenarios. .
  • LOS may mean that the UE and base station are in line-of-sight (or unobstructed) to each other
  • NLOS may mean that the UE and base station are not in line-of-sight (or unobstructed) to each other. It can also mean something.
  • the location of the UE is estimated from the fingerprints of multiple transmission paths (multipaths) of the UE based on a database/AI model.
  • the multipath information may be, for example, information regarding the angle of arrival (AoA)/angle of departure (AoD) of the signal on the optimal/candidate transmission path.
  • AoA angle of arrival
  • AoD angle of departure
  • the information regarding AoA may include, for example, information regarding at least one of azimuth angles of arrival and zenith angles of arrival. Further, the information regarding the AoD may include, for example, information regarding at least one of radial azimuth angles of departure and radial zenith angles of depth.
  • 3GPP Rel. 16 NR supports the following positioning technologies. ⁇ Positioning based on DL/UL Time Difference Of Arrival (TDOA) ⁇ Positioning based on angle (DL AoD/UL AoA) ⁇ Positioning based on multi-Round Trip Time (RTT) ⁇ Positioning based on Enhanced Cell ID (E-CID)
  • TDOA Time Difference Of Arrival
  • DL AoD/UL AoA angle
  • RTT multi-Round Trip Time
  • E-CID Enhanced Cell ID
  • FIG. 3 is a diagram illustrating an example of positioning based on DL/UL TDOA.
  • TRP #0-#2 base stations
  • the position of the UE is estimated (measured) using a measured value of reference signal time difference (RSTD).
  • RSTD reference signal time difference
  • T i - T j the points where RSTD (T i - T j ) takes a certain value (k i, j ) for two specific base stations (TRP #i, #j (i, j are integers)
  • TRP #i, #j i, j are integers
  • a hyperbola H is drawn. I can draw i and j .
  • the intersection of multiple such hyperbolas in this example, the intersection of H 0,1, H 1,2, H 2,0
  • the position of the UE may be estimated using the RSRP of the reference signal.
  • FIG. 4 is a diagram showing an example of positioning based on DL AoD/UL AoA.
  • the position of the UE is estimated using a measured value of DL AoD (for example, ⁇ or ⁇ ) or a measured value of UL AoA (for example, ⁇ or ⁇ ).
  • the location of the UE may be estimated using RSRP.
  • FIG. 5 is a diagram illustrating an example of positioning based on multi-RTT.
  • the position of the UE is estimated using a plurality of RTTs calculated from the Tx/Rx time difference of reference signals (and additionally RSRP, RSRQ, etc.). For example, a geometric circle based on RTT can be drawn around each base station. The intersection of these multiple circles may be estimated as the location of the UE.
  • FIG. 6 is a diagram showing an example of positioning based on E-CID.
  • the location of the UE is estimated based on the geometric location of the serving cell and additional measurements (Tx-Rx time difference, RSRP, RSRQ, etc.).
  • the above-described positioning in DL may be performed on the UE side or the LMF side.
  • the UE may calculate the UE position based on various measurement results of the UE and assistance information from the LMF.
  • the UE may report various measurement results to the LMF, and the LMF may calculate the position of the UE.
  • the assist information may be information for assisting in position estimation of the UE.
  • the above-described positioning in UL may be performed on the LMF side.
  • the base station may report various measurement results to the LMF, and the LMF may calculate the location of the UE.
  • the above-described positioning in DL and UL may be performed on the LMF side.
  • the UE/base station may report various measurement results to the LMF, and the LMF may calculate the location of the UE.
  • 3GPP Rel. No. 17 proposes a positioning method using assist information with the aim of further improving positioning accuracy.
  • the assist information may be transmitted between the UE, the base station, and the LMF as measurement information for the above-mentioned DL/UL-TDOA, DL-AoD/UL-AoA, multi-RTT, and E-CID.
  • the assist information may include information regarding at least one of the following: ⁇ Timing Error Group (TEG), ⁇ RSRPP (path specific RSRP), ⁇ Expected angle, ⁇ Adjacent beam information ⁇ TRP antenna/beam information, ⁇ LOS/NLOS indicator, -Additional path reporting.
  • the TEG may indicate one or more PRS (Positioning Reference Signal) resources whose transmission/reception timing errors (Rx/Tx timing errors) are within a certain margin.
  • PRS Positioning Reference Signal
  • RSRPP may indicate the measurement result of RSRP in the first path.
  • the assist information regarding the expected angle may indicate expected UL-AoA/ZoA.
  • the assist information may be transmitted from the LMF to the base station. Further, the assist information may support positioning of at least one of UL TDOA, UL AoA, and multi-RTT.
  • the assist information regarding the expected angle may include information regarding expected DL-AoA/ZoA (expected DL-AoA/ZoA) or DL-AoD/ZoD (expected DL-AoD/ZoD).
  • the assist information may be transmitted from the LMF to the UE. Further, the assist information may support positioning of at least one of DL TDOA, DL AoA, and multi-RTT. This improves the accuracy of angle-based UE positioning and allows optimization of Rx beamforming of the UE or base station.
  • the assist information regarding the predicted angle may include information indicating the range of uncertainty of these values.
  • adjacent beam information can either be a subset of DL-PRS resources (option 1) for prioritization of DL-AoD reports, or the boresight direction of each DL-PRS resource (option 2). ) may also include information regarding. This allows optimization of the UE's Rx beam sweeping and DL-AoD measurements.
  • the assist information may include PRS beam pattern information as additional beam information.
  • This PRS beam pattern information may include information regarding the relative power between DL-PRS resources for each angle for each TRP.
  • the LOS/NLOS indicator may indicate information regarding Line Of Site (LOS)/Non-Line Of Site (NLOS).
  • measurement gaps that are set in advance, MG activation via lower layers, MG-less position, PRS Rx/Tx in RRC_INACTIVE state, or on-demand PRS, etc. may be configured for (and may be utilized by) the UE.
  • the present inventors conceived of a suitable control method for UE positioning using AI technology. Note that each embodiment of the present disclosure may be applied when AI/prediction is not used.
  • the angle at which a signal arrives at the UE, the AoA at the UE, the AoA, and the AoA at the base station may be read interchangeably.
  • the angle at which a signal is radiated at the UE, the AoD at the UE, and the AoD at the base station may be read interchangeably.
  • AoA and AoD may be read interchangeably.
  • UE and base station may be interchanged.
  • positioning may be interchanged with position determination, position estimation, etc.
  • a terminal (user terminal, User Equipment (UE))/Base Station (BS) trains an ML model in a training mode. , implements the ML model in an inference mode (also called inference mode, inference mode, etc.). In the inference mode, the accuracy of the trained ML model trained in the training mode may be verified.
  • UE User Equipment
  • BS Base Station
  • AI may be read as an object (also referred to as a target, object, data, function, program, etc.) that has (implements) at least one of the following characteristics: ⁇ Estimation based on observed or collected information; - Selection based on observed or collected information; - Predictions based on observed or collected information.
  • an object may be, for example, an apparatus, a device, such as a terminal or a base station. Furthermore, in the present disclosure, an object may correspond to a program/model/entity that operates on the device.
  • the ML model may be replaced by an object that has (implements) at least one of the following characteristics: ⁇ Produce estimates by feeding information, ⁇ Predict the estimated value by giving information, ⁇ Discover characteristics by providing information, ⁇ Select an action by providing information.
  • ML model, model, AI model, predictive analytics, predictive analysis model, etc. may be read interchangeably.
  • the ML model may be derived using at least one of regression analysis (eg, linear regression analysis, multiple regression analysis, logistic regression analysis), support vector machine, random forest, neural network, deep learning, and the like.
  • regression analysis eg, linear regression analysis, multiple regression analysis, logistic regression analysis
  • support vector machine random forest, neural network, deep learning, and the like.
  • a model may be interpreted as at least one of an encoder, a decoder, a tool, etc.
  • the ML model Based on the input information, the ML model outputs at least one information such as an estimated value, a predicted value, a selected action, a classification, etc.
  • the ML model may include supervised learning, unsupervised learning, reinforcement learning, and the like.
  • Supervised learning may be used to learn general rules that map inputs to outputs.
  • Unsupervised learning may be used to learn features of the data.
  • Reinforcement learning may be used to learn actions to maximize a goal.
  • generation, calculation, derivation, etc. may be read interchangeably.
  • implementation, operation, operation, execution, etc. may be read interchangeably.
  • training, learning, updating, retraining, etc. may be used interchangeably.
  • inference, after-training, production use, actual use, etc. may be read interchangeably.
  • Signal may be interchanged with signal/channel.
  • the training mode may correspond to a mode in which the UE/BS transmits/receives signals for the ML model (in other words, an operation mode during the training period).
  • the inference mode may correspond to a mode in which the UE/BS implements an ML model (e.g., implements a trained ML model to predict an output) (in other words, an operating mode during the inference period). good.
  • the training mode may mean a mode in which a specific signal transmitted in the inference mode has a large overhead (for example, a large amount of resources).
  • training mode may mean a mode that refers to a first configuration (for example, a first DMRS configuration, a first CSI-RS configuration, a first CSI reporting configuration).
  • inference mode refers to a mode that refers to a second configuration different from the first configuration (e.g., second DMRS configuration, second CSI-RS configuration, second CSI reporting configuration). You may.
  • the first setting at least one of measurement-related time resources, frequency resources, code resources, and ports (antenna ports) may be set more than in the second setting.
  • the CSI report settings may include settings related to an autoencoder.
  • A/B and “at least one of A and B” may be read interchangeably. Furthermore, in the present disclosure, “A/B/C” may mean “at least one of A, B, and C.”
  • Radio Resource Control RRC
  • RRC parameters RRC parameters
  • RRC messages upper layer parameters, fields, Information Elements (IEs), settings, etc.
  • IEs Information Elements
  • CE Medium Access Control Element
  • update command activation/deactivation command, etc.
  • the upper layer signaling may be, for example, Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information, etc., or a combination thereof.
  • RRC Radio Resource Control
  • MAC Medium Access Control
  • MAC signaling may use, for example, a MAC Control Element (MAC CE), a MAC Protocol Data Unit (PDU), or the like.
  • Broadcast information includes, for example, a master information block (MIB), a system information block (SIB), a minimum system information (RMSI), and other system information ( Other System Information (OSI)) may also be used.
  • MIB master information block
  • SIB system information block
  • RMSI minimum system information
  • OSI Other System Information
  • the physical layer signaling may be, for example, downlink control information (DCI), uplink control information (UCI), etc.
  • DCI downlink control information
  • UCI uplink control information
  • an index an identifier (ID), an indicator, a resource ID, etc.
  • ID an identifier
  • indicator an indicator
  • resource ID a resource ID
  • sequences, lists, sets, groups, groups, clusters, subsets, etc. may be used interchangeably.
  • a panel, a UE panel, a panel group, a beam, a beam group, a precoder, an uplink (UL) transmitting entity, a transmission/reception point (TRP), a base station, and a spatial relation information (SRI) are described.
  • SRS resource indicator SRI
  • control resource set CONtrol REsource SET (CORESET)
  • Physical Downlink Shared Channel PDSCH
  • codeword CW
  • Transport Block Transport Block
  • TB transport Block
  • RS reference signal
  • antenna port e.g. demodulation reference signal (DMRS) port
  • antenna port group e.g.
  • DMRS port group groups (e.g., spatial relationship groups, Code Division Multiplexing (CDM) groups, reference signal groups, CORESET groups, Physical Uplink Control Channel (PUCCH) groups, PUCCH resource groups), resources (e.g., reference signal resources, SRS resource), resource set (for example, reference signal resource set), CORESET pool, downlink Transmission Configuration Indication state (TCI state) (DL TCI state), uplink TCI state (UL TCI state), unified TCI Unified TCI state, common TCI state, quasi-co-location (QCL), QCL assumption, etc. may be read interchangeably.
  • groups e.g., spatial relationship groups, Code Division Multiplexing (CDM) groups, reference signal groups, CORESET groups, Physical Uplink Control Channel (PUCCH) groups, PUCCH resource groups
  • resources e.g., reference signal resources, SRS resource
  • resource set for example, reference signal resource set
  • CORESET pool downlink Transmission Configuration Indication state (TCI state) (DL TCI state), up
  • timing, time, time, slot, subslot, symbol, subframe, etc. may be read interchangeably.
  • direction, axis, dimension, domain, polarization, polarization component, etc. may be read interchangeably.
  • estimation, prediction, and inference may be used interchangeably.
  • estimate the terms “estimate,” “predict,” and “infer” may be used interchangeably.
  • autoencoder, encoder, decoder, etc. may be replaced with at least one of a model, ML model, neural network model, AI model, AI algorithm, etc. Further, the autoencoder may be interchanged with any autoencoder such as a stacked autoencoder or a convolutional autoencoder.
  • the encoder/decoder of the present disclosure may adopt models such as Residual Network (ResNet), DenseNet, RefineNet, etc.
  • bits, bit strings, bit sequences, series, values, information, values obtained from bits, information obtained from bits, etc. may be read interchangeably.
  • layers for encoders may be interchanged with layers (input layer, intermediate layer, etc.) used in the AI model.
  • the layers of the present disclosure include an input layer, an intermediate layer, an output layer, a batch normalization layer, a convolution layer, an activation layer, a dense layer, a normalization layer, a pooling layer, an attention layer, a dropout layer, It may correspond to at least one of the fully connected layers.
  • Option 1-1 describes a case where the UE performs positioning using an AI model on the UE side.
  • Option 1-1 is applicable to UE-based DL positioning.
  • FIG. 7 is a diagram illustrating an example of the UE positioning method in option 1-1 of the first embodiment.
  • the UE may use the AI model to infer UE positioning (UE positioning, location).
  • the UE may report the inferred positioning results to the LMF.
  • the inference of the AI model in the UE may be performed based on the data set that the UE has.
  • Information input for the AI model may include raw data (information regarding characteristics)/assist information measured by the UE.
  • the UE may receive assist information from the base station or the LMF.
  • the raw measurement results (which may be referred to as measured characteristics) measured by the UE may include data (information) regarding at least one of the following (note that information listed with a comma indicates that all are included): (The same applies for the remainder of this disclosure): - UE DL-PRS-RSRP (or DL-PRS-RSSI/RSRQ) measurement results of multiple reference TRPs, ⁇ DL-RSTD measurement results of multiple reference TRPs, ⁇ Reference TRP time of arrival (ToA) measurement results, ⁇ Reference TRP AoD measurement results, ⁇ Quality of various measurements, ⁇ Time stamp (time) of measurement results, ⁇ Physical Cell ID (PCI), Global Cell ID (GCI), Absolute Radio Frequency Channel Number (ARFCN), PRS resource ID, PRS resource set ID, and PRS ID in each measurement, ⁇ UE Rx TEG ID, TRP Tx TEG ID, UE Tx TEG ID, UE RxTx TEG ID, TRP Rx TEG
  • GCI may be interchanged with NR Cell Global Identity (NCGI).
  • NCGI NR Cell Global Identity
  • the assistance information notified from the base station/LMF may include information regarding at least one of the following: ⁇ Noise in the ground truth dataset (expected variance of noise, etc.), ⁇ Physical cell ID (PCI), global cell ID (GCI), ARFCN, and PRS ID of the NR TRP candidate to be measured, ⁇ Relative timing of the NR TRP candidate with the serving (reference) TRP; ⁇ TRP SSB information (SSB time/frequency occupancy rate), ⁇ DL-PRS setting of candidate NR TRP, ⁇ Spatial direction information (azimuth angle, elevation angle, etc.) of TRP DL-PRS resources provided by the base station; - Geographical coordinates of the TRP provided by the base station (including the transmission reference position for each DL-PRS resource ID, the reference position of the transmission antenna of the reference TRP, and the reference position of the transmission antennas of other TRPs), - Fine Timing relative of candidate NR TRP to serving (reference) TRP; ⁇ Transmission point indication
  • ground truth data set may be read as a data set representing location information used for AI training/validation/testing.
  • the UE may output the UE location from the AI model.
  • the above AI model on the UE side which has the UE position as an output, is also applicable to UL-based positioning, DL/UL-based positioning, etc.
  • These inputs may include, for example, the measurement results of the UE/gNB in option 1-4 described below, extracted features, etc., or may include the extracted features on the gNB side in option 1-3 described later. However, it may also include features extracted on the LMF side in option 1-5, which will be described later.
  • Option 1-2 describes a case where the UE extracts features using an AI model on the UE side.
  • Option 1-2 is applicable to UE-based and UE-assisted DL positioning, DL+UL positioning.
  • FIG. 8 is a diagram illustrating an example of the UE positioning method in option 1-2 of the first embodiment.
  • the UE may extract features using an AI model.
  • the UE may then derive the UE positioning based on the extracted features (which may be referred to as extracted features).
  • the UE may report the derived positioning results to the LMF.
  • the UE may input the extracted features into an AI model different from the AI model and output information regarding the UE position, or input the extracted features into a Information regarding the UE location may be output by providing it as an argument of the function (eg, information regarding the UE location may be output based on the positioning technique described above for Rel. 16).
  • the UE may report the extraction results to the LMF.
  • the LMF may perform UE positioning based on the notified extraction result.
  • the inference of the AI model in the UE may be performed based on the data set that the UE has.
  • Information input for the AI model may include raw data (information regarding characteristics)/assist information measured by the UE.
  • the UE may receive assist information from the base station or the LMF. That is, the input information in option 1-1 is also applicable to option 1-2.
  • the raw measurement results (measured characteristics) measured by the UE may further include data regarding at least one of the following: ⁇ Measurement results regarding UE Rx-Tx time difference, ⁇ Timing Advance (TA) offset used by the UE, - UE Rx TEG ID, UE Tx TEG ID, UE RxTx TEG ID, TRP Tx TEG ID, TRP Rx TEG ID, TRP RxTx TEG ID, related to UE Rx-Tx time difference measurement, - Information regarding LOS/NLOS for each UE Rx-Tx time difference measurement.
  • TA Timing Advance
  • the UE may provide the extracted features to the LMF as an output of the AI model.
  • Information regarding extracted features may include information regarding at least one of the following: - UE DL-PRS-RSRP (or DL-PRS-RSSI/RSRQ) measurement results of multiple reference TRPs, ⁇ UE DL-RSTD measurement results of multiple reference TRPs, ⁇ Reference TRP ToA measurement results, ⁇ Reference TRP AoD measurement results, ⁇ 1st path DL-PRS-RSRP ⁇ UE channel impulse response/frequency response, coded channel impulse response/frequency response of reference TRP, - the phase difference measured by the UE at different antennas in the antenna array; - UE received multipath characteristics of reference TRP (e.g.
  • each path DL-PRS-RSRP measurement result amplitude/average/CDF of estimated multipath amplitude, path loss, root mean square (rms) delay spread, kurtosis of channel impulse response, ⁇ Quality of various UE measurements, - LOS/NLOS indicators for each UE measurement/each reference TRP.
  • rms root mean square
  • the extracted features may or may not include a time stamp (time when extraction was performed, time when input information was obtained (for example, measurement was performed), etc.).
  • Information regarding the input of the AI model and information regarding the extracted features may include duplicate information. If the extracted features are also included in the input of the AI model, it is possible for the AI model to make the features more reliable and stable (for example, by processing them using algorithms such as filtering). By).
  • Option 1-3 describes a case where the base station extracts features for UE positioning using an AI model on the base station side, and the LMF performs UE positioning.
  • Option 1-3 is applicable to UL positioning and DL+UL positioning.
  • FIG. 9 is a diagram illustrating an example of the UE positioning method in option 1-3 of the first embodiment.
  • the base station may extract features using an AI model.
  • the base station may then report the extracted features to the LMF as an extraction result.
  • the LMF may derive the UE positioning based on the notified characteristics.
  • the AI model inference at the base station may be performed based on the data set possessed by the base station.
  • the information input for the BS AI model is the raw data (information regarding characteristics) measured by the base station (raw measurement results described below, characteristics recognized by the base station). )/assist information may be included.
  • the base station may receive assist information from the UE or LMF.
  • the UE may extract features using an AI model.
  • the UE may report the extracted features to the base station as an extraction result.
  • the feature extraction may be further performed at the base station.
  • the base station may transmit the extraction results (recognized features) to the LMF, and the LMF may derive the UE positioning based on the extraction results.
  • the inference of the AI model in the UE may be performed similarly to option 1-1, and duplicate explanations will not be repeated.
  • the raw measurements measured by the base station (measured features) or the features recognized by the base station (gNB-aware features) may include data regarding at least one of the following: ⁇ UL-RTOA, ⁇ UL-SRS-RSRP, ⁇ UL-AoA, ⁇ Measurement results regarding the Rx-Tx time difference of the base station, ⁇ Measurement timestamp (time), ⁇ Beam information for each measurement value, ⁇ Measurement NCGI and TRP ID, ⁇ Quality of various measurements, ⁇ PCI, GCI, TRP ID of the TRP provided by the base station, ⁇ UE Rx TEG ID, UE Tx TEG ID, UE RxTx TEG ID, TRP Tx TEG ID, TRP RxTx TEG ID, ⁇ TRP SSB information (SSB time occupancy rate/frequency occupancy rate), ⁇ UE SRS settings, - TRP timing information that configured the UE's SRS transmission, ⁇ Channel impulse
  • the assistance information notified from the UE or LMF may include information regarding at least one of the following: ⁇ Noise in the ground truth dataset (e.g. expected variance of noise), ⁇ UE SRS settings, - TRP timing information that configured the UE's SRS transmission, ⁇ Number of transmissions/time for which UL-SRS is required; ⁇ Bandwidth, ⁇ Resource type (periodic, semi-permanent, aperiodic), - Number of requested SRS resource sets, number of SRS resources per set, ⁇ Spatial related information, ⁇ SSB information, ⁇ SRS period of each SRS resource set, ⁇ Carrier frequency of SRS transmission band, ⁇ Measurement values of LOS/NLOS, Rx-Tx time difference or phase difference for each UL-RTOA/RSRP/AoA/gNB, - UL timing information including timing uncertainty in reception of the SRS by the TRP candidate; - Features extracted by the UE (described above in option 1-2).
  • Information regarding features extracted as output of the AI model may include information regarding at least one of the following: ⁇ UL-RTOA, ⁇ UL-SRS-RSRP, ⁇ UL-AoA, ⁇ Measurement results regarding the Rx-Tx time difference of the base station, ⁇ First path UL-SRS RSRP, ⁇ Channel impulse/frequency response, coded channel impulse/frequency response, ⁇ Phase difference between different antennas in the antenna array, ⁇ Receive multipath characteristics (e.g., UL-SRS-RSRP measurement results for each path, amplitude/average/CDF of estimated multipath amplitude, path loss, root mean square (rms) delay spread, kurtosis of channel impulse response, etc.) ⁇ Quality of various measurements, - LOS/NLOS indicator for each measurement.
  • ⁇ Receive multipath characteristics e.g., UL-SRS-RSRP measurement results for each path, amplitude/average/CDF of estimated multipath amplitude, path loss,
  • Information regarding the input of the AI model and information regarding the extracted features may include duplicate information. If the extracted features are also included in the input of the AI model, the AI model can make the features more reliable and stable.
  • the output of the AI model may be information on the UE location, as described in FIG. 9B.
  • Option 1-4 describes a case where the LMF performs UE positioning using an AI model on the LMF side.
  • Options 1-4 are applicable to UE-assisted DL positioning, UL positioning, or DL+UL positioning.
  • FIG. 10 is a diagram illustrating an example of the UE positioning method in option 1-4 of the first embodiment.
  • the base station/UE may notify (report) information regarding the above characteristics to the LMF.
  • the LMF may use its features as input to an AI model to infer the UE location.
  • the inference of the AI model in the LMF may be performed based on the data set that the LMF has.
  • the information input for the AI model is raw data measured by the UE/base station (information regarding features)/assist information/information regarding extracted features (option 1-2/1-3). ) may be included.
  • the LMF may receive assist information from the UE or base station.
  • the UE may extract features using an AI model based on the dataset it has, as described in option 1-2 above.
  • Information input for the AI model may include raw data (information regarding characteristics)/assist information measured by the UE.
  • the UE may receive assist information from the base station or the LMF.
  • the UE may report the extraction results to the LMF.
  • the LMF may use the notified extraction results (features) as input to an AI model to infer UE positioning.
  • the information input (received) from the UE for the AI model may include information regarding at least one of the following: ⁇ Reference TRP UE DL-PRS-RSRP (or DL-PRS-RSSI/RSRQ) measurement results, ⁇ DL-RSTD measurement results of reference TRP, ⁇ Reference TRP ToA measurement results, ⁇ Reference TRP AoD measurement results, ⁇ Quality of various measurements, ⁇ Measurement results regarding the UE Rx-Tx time difference, - TA offset used by the UE, ⁇ UE Rx TEG ID, UE Tx TEG ID, UE RxTx TEG ID, ⁇ Time stamp of measurement results, ⁇ PCI, GCI, ARFCN, PRS resource ID, PRS resource set ID, PRS ID of each measurement value, ⁇ UE Rx TEG ID for DL RSTD measurement, ⁇ DL-PRS reception beam index, ⁇ Measurement results of the first path DL-PRS-RSRP
  • each path DL-PRS-RSRP measurement result amplitude/average/CDF of estimated multipath amplitude, path loss, root mean square (rms) delay spread, kurtosis of channel impulse response), ⁇ Information on LOS/NLOS in various measurements, ⁇ Noise in the ground truth dataset (expected variance of noise, etc.), - Information about the features extracted at the UE side (described above in option 1-2).
  • the information input (received) from the base station for the AI model may include information regarding at least one of the following: ⁇ UL-RTOA, ⁇ UL-SRS-RSRP, ⁇ UL-AoA, ⁇ Measurement value of base station Rx-Tx time difference, ⁇ Measurement timestamp, ⁇ Beam information for each measurement, ⁇ Measurement NCGI and TRP ID, ⁇ Quality of various measurements, ⁇ PCI, GCI, TRP ID of the TRP provided by the base station, ⁇ TRP Tx TEG ID, TRP Rx TEG ID, TRP RxTx TEG ID, ⁇ TRP SSB information (SSB time/frequency occupancy rate), ⁇ UE SRS settings, - TRP timing information that configured the UE's SRS transmission, ⁇ Channel impulse/frequency response, coded channel impulse/frequency response, ⁇ Phase difference between different antennas in the antenna array, ⁇ Receive multipath characteristics (e.g.
  • the LMF may provide the UE position to the UE or base station.
  • Option 1-5 describes a case where LMF extracts features using an AI model on the LMF side.
  • Options 1-5 are applicable to UE-assisted DL positioning, UL positioning, DL+UL positioning.
  • FIG. 11 is a diagram illustrating an example of the UE positioning method in option 1-5 of the first embodiment.
  • the UE or base station may report measurement results or assist information to the LMF.
  • the LMF may receive information (information regarding features) notified from the UE or the base station as input and extract features using an AI model. The LMF may then derive the UE positioning based on the extracted features.
  • the LMF may receive the input information described above in options 1-4 (measurement results/assist information/information regarding extracted features) from the UE/base station as input information for the AI model.
  • the LMF may provide information about the extracted features to the UE or base station.
  • Information regarding extracted features may include information regarding at least one of the following: ⁇ Reference TRP UE DL-PRS-RSRP (or DL-PRS-RSSI/RSRQ) measurement results, ⁇ DL-RSTD measurement results of reference TRP, ⁇ Reference TRP ToA measurement results, ⁇ Reference TRP AoD measurement results, - UE channel impulse/frequency response of reference TRP, coded channel impulse/frequency response, - UE measured phase difference at different antennas in the antenna array; ⁇ Received multipath characteristics of the reference TRP (e.g., UL-SRS-RSRP measurement results of the first or each path, amplitude/average/CDF of estimated multipath amplitude, path loss, root mean square (rms) delay spread, channel impulse response kurtosis), ⁇ Measurement results regarding UE Rx-Tx time difference, ⁇ UL-RTOA, ⁇ UL
  • Option 2-1 describes AI model learning on the UE side.
  • Option 2-1 may be used, for example, in conjunction with option 1-1/1-2 described above.
  • the base station or LMF may learn the AI model (Option 2-1-A).
  • the base station may set the learned AI model to the UE.
  • the LMF may notify the UE of the AI model through an LTE Positioning Protocol (LPP) message.
  • LPF LTE Positioning Protocol
  • the UE may also learn an AI model (option 2-1-B).
  • the UE determines at least one of the following parameters: whether or not to apply an AI model, which AI model to use (if multiple AI models are stored), parameters of the AI model, etc. (also referred to as information for determining an AI model) may be received from the base station or LMF (option 2-1-a).
  • the base station or LMF may send a request to the UE to update the AI model parameters or start/stop the AI model application. This request may be notified by DCI/MAC CE from the base station to the UE, or may be notified by an LPP message from the LMF to the UE.
  • the UE may determine information for determining the AI model based on certain rules (option 2-1-b).
  • Option 2-2 describes AI model learning on the base station side.
  • Option 2-2 may be used, for example, in conjunction with option 1-3 above.
  • the UE or LMF may learn the AI model (option 2-2-A).
  • the UE may report the learned AI model to the base station.
  • the LMF may notify the base station of the AI model using an NR Positioning Protocol A (NRPPa) message.
  • the base station may also learn an AI model (option 2-2-B).
  • NRPPa NR Positioning Protocol A
  • the UE or LMF may notify the base station of information for determining the AI model (option 2-2-a).
  • the UE or LMF may send a request to the base station to update the AI model parameters or start/stop the AI model application. This request may be notified by the UCI, PUSCH, or MAC CE from the UE to the base station, or by the NRPPa message from the LMF to the base station.
  • the base station may determine information for determining the AI model based on certain rules (option 2-2-b).
  • Option 2-3 describes AI model learning on the LMF side.
  • Option 2-3 may be used, for example, in conjunction with option 1-4/1-5 above.
  • the UE or base station may learn the AI model (option 2-3-A).
  • the UE may report the learned AI model to the LMF in an LPP message.
  • the base station may notify the LMF of the AI model through the NRPPa message.
  • the LMF may also learn an AI model (option 2-3-B).
  • the UE or base station may notify the LMF of information for determining the AI model (option 2-3-a).
  • the UE or base station can send a request to the LMF to update AI model parameters or start/stop the AI model application.
  • This request may be notified by an LPP message from the UE to the LMF or a NRPPa message from the base station to the LMF.
  • the LMF may determine information for determining the AI model based on certain rules (Option 2-3-b).
  • ⁇ Supplement> at least one of the following can be applied as an ML algorithm for positioning: ⁇ Random forest (RF), ⁇ Support vector machine regressor (SVM), ⁇ Relevance Vector Machine (RVM), ⁇ least square SVM classifier (LS-SVMC), ⁇ Deep learning, ⁇ convolution neural networks (CNN), ⁇ Principal component analysis (PCA), ⁇ K-nearest neighbor (KNN), ⁇ Recurrent neural network, ⁇ Linear discriminant analysis (LDA), ⁇ Deep auto encoder, ⁇ multi-layer perceptron (MLP), ⁇ Gaussian process manifold kernel dimension reduction (GPMKDR), ⁇ Kernel principal component analysis, ⁇ K-means, ⁇ deep belief network (DBN), ⁇ online independent SVM (OLSVM), ⁇ Extreme learning machine (ELM), ⁇ Transfer learning.
  • RF Random forest
  • SVM ⁇ Support vector machine regressor
  • RVM Relevance
  • information regarding the AI model used by the UE/base station may be notified in advance from another device (UE/base station/LMF).
  • the AI model information may specify input/output of the AI model.
  • the information input for the AI model/raw data (information regarding features)/assist information measured by the UE may be referred to as information for position estimation (information regarding position). good.
  • This location-related information may include UE location information output as a result of location estimation.
  • Any notification from the NW to the UE in the embodiments described above may include physical layer signaling (e.g. DCI), higher layer signaling (e.g. RRC signaling, MAC CE), specific signals/channels (e.g. PDCCH, PDSCH, ref. signals), or a combination thereof.
  • physical layer signaling e.g. DCI
  • higher layer signaling e.g. RRC signaling, MAC CE
  • specific signals/channels e.g. PDCCH, PDSCH, ref. signals
  • the MAC CE may be identified by including a new logical channel ID (LCID) in the MAC subheader.
  • LCID logical channel ID
  • the above notification When the above notification is performed by a DCI, the above notification includes a specific field of the DCI, a radio network temporary identifier (Radio Network Temporary Identifier (RNTI)), the format of the DCI, etc.
  • RNTI Radio Network Temporary Identifier
  • any notification from the NW to the UE in the above embodiments may be performed periodically, semi-persistently, or aperiodically.
  • the encoder/decoder may be interchanged with the AI model deployed at the UE/base station. That is, the present disclosure is not limited to the case of using an autoencoder, but may be applied to the case of inference using any model. Furthermore, the object that the UE/base station compresses using the encoder in the present disclosure is not limited to CSI (or channel/precoding matrix), but may be any information.
  • At least one of the embodiments described above may be applied only to UEs that have reported or support a particular UE capability.
  • the particular UE capability may indicate at least one of the following: - supporting specific processing/operation/control/information for at least one of the above embodiments; Supporting acquisition/reporting of channel characteristic information (e.g. LOS, NLOS, location information); ⁇ Maximum FLOPs of AI model that UE can deploy, ⁇ Maximum number of parameters of AI model that UE can deploy, ⁇ Reference model supported by UE, ⁇ Layers/algorithms/functions supported by the UE, ⁇ Calculating ability, ⁇ Data collection ability, ⁇ Whether it supports AI model for acquiring UE location, ⁇ Whether the UE supports AI models for extracting features for positioning; ⁇ Whether the UE supports learning the AI model for positioning; - Whether the UE supports notification of AI model information from the base station/LMF.
  • channel characteristic information e.g. LOS, NLOS, location information
  • ⁇ Maximum FLOPs of AI model that UE can deploy e.g. LOS, NLOS, location information
  • the above-mentioned specific UE capability may be a capability that is applied across all frequencies (commonly regardless of frequency), or may be a capability for each frequency (for example, cell, band, BWP). , capability for each frequency range (for example, Frequency Range 1 (FR1), FR2, FR3, FR4, FR5, FR2-1, FR2-2), or for each subcarrier spacing (SCS). It may be the ability of
  • the above-mentioned specific UE capability may be a capability that is applied across all duplex schemes (commonly regardless of the duplex scheme), or may be a capability that is applied across all duplex schemes (for example, Time Division Duplex).
  • the capability may be for each frequency division duplex (TDD)) or frequency division duplex (FDD)).
  • the UE is configured with specific information related to the embodiment described above by upper layer signaling.
  • the specific information may be information indicating that the use of the AI model is enabled, arbitrary RRC parameters for a specific release (for example, Rel. 18), or the like.
  • the UE does not support at least one of the specific UE capabilities or is not configured with the specific information, for example, Rel. 15/16 operations may be applied.
  • the UE may be used for (for compression of) transmission of information between the UE and the base station other than CSI feedback.
  • the UE generates information related to location (or positioning)/information related to location estimation in a location management function (LMF) according to at least one of the above-described embodiments (e.g., using an encoder). You may report it to the network.
  • the information may be channel impulse response (CIR) information for each subband/antenna port. By reporting this, the base station can estimate the location of the UE without reporting the angle/time difference of received signals.
  • CIR channel impulse response
  • a control unit that inputs information to an Artificial Intelligence (AI) model and obtains an output;
  • a terminal comprising: a transmitter that transmits information regarding the location of the terminal based on the output.
  • the information regarding the position includes at least one of information regarding a feature for position estimation and position information of the terminal output as a result of position estimation.
  • the control unit estimates the position of the terminal based on the information regarding the characteristics when obtaining information regarding the characteristics for estimating the position of the terminal as the output.
  • the information input to the AI model includes at least one of information regarding the arrival angle of the signal at the terminal, information regarding the radiation angle of the signal at the terminal, expected angle of the signal at the terminal, and adjacent beam information at the terminal.
  • the terminal according to any one of Supplementary Notes 1 to 3, including:
  • wireless communication system The configuration of a wireless communication system according to an embodiment of the present disclosure will be described below.
  • communication is performed using any one of the wireless communication methods according to the above-described embodiments of the present disclosure or a combination thereof.
  • FIG. 12 is a diagram illustrating an example of a schematic configuration of a wireless communication system according to an embodiment.
  • 5G NR 5th generation mobile communication system New Radio
  • 3GPP Third Generation Partnership Project
  • the wireless communication system 1 may support dual connectivity between multiple Radio Access Technologies (RATs) (Multi-RAT Dual Connectivity (MR-DC)).
  • MR-DC has dual connectivity between LTE (Evolved Universal Terrestrial Radio Access (E-UTRA)) and NR (E-UTRA-NR Dual Connectivity (EN-DC)), and dual connectivity between NR and LTE (NR-E -UTRA Dual Connectivity (NE-DC)).
  • RATs Radio Access Technologies
  • MR-DC has dual connectivity between LTE (Evolved Universal Terrestrial Radio Access (E-UTRA)) and NR (E-UTRA-NR Dual Connectivity (EN-DC)), and dual connectivity between NR and LTE (NR-E -UTRA Dual Connectivity (NE-DC)).
  • E-UTRA Evolved Universal Terrestrial Radio Access
  • EN-DC E-UTRA-NR Dual Connectivity
  • NE-DC NR-E -UTRA Dual Connectivity
  • the LTE (E-UTRA) base station (eNB) is the master node (Master Node (MN)), and the NR base station (gNB) is the secondary node (Secondary Node (SN)).
  • the NR base station (gNB) is the MN
  • the LTE (E-UTRA) base station (eNB) is the SN.
  • the wireless communication system 1 has dual connectivity between multiple base stations within the same RAT (for example, dual connectivity (NR-NR Dual Connectivity (NN-DC) where both the MN and SN are NR base stations (gNB)). )) may be supported.
  • dual connectivity NR-NR Dual Connectivity (NN-DC) where both the MN and SN are NR base stations (gNB)).
  • the wireless communication system 1 includes a base station 11 that forms a macro cell C1 with relatively wide coverage, and base stations 12 (12a-12c) that are located within the macro cell C1 and form a small cell C2 that is narrower than the macro cell C1. You may prepare.
  • User terminal 20 may be located within at least one cell. The arrangement, number, etc. of each cell and user terminal 20 are not limited to the embodiment shown in the figure. Hereinafter, when base stations 11 and 12 are not distinguished, they will be collectively referred to as base station 10.
  • 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).
  • CA carrier aggregation
  • CC component carriers
  • DC dual connectivity
  • Each CC may be included in at least one of a first frequency band (Frequency Range 1 (FR1)) and a second frequency band (Frequency Range 2 (FR2)).
  • Macro cell C1 may be included in FR1
  • small cell C2 may be included in FR2.
  • FR1 may be a frequency band below 6 GHz (sub-6 GHz)
  • FR2 may be a frequency band above 24 GHz (above-24 GHz). Note that the frequency bands and definitions of FR1 and FR2 are not limited to these, and FR1 may correspond to a higher frequency band than FR2, for example.
  • the user terminal 20 may communicate using at least one of time division duplex (TDD) and frequency division duplex (FDD) in each CC.
  • TDD time division duplex
  • FDD frequency division duplex
  • the plurality of base stations 10 may be connected by wire (for example, optical fiber, X2 interface, etc. compliant with Common Public Radio Interface (CPRI)) or wirelessly (for example, NR communication).
  • wire for example, optical fiber, X2 interface, etc. compliant with Common Public Radio Interface (CPRI)
  • NR communication for example, when NR communication is used as a backhaul between base stations 11 and 12, base station 11, which is an upper station, is an Integrated Access Backhaul (IAB) donor, and base station 12, which is a relay station, is an IAB donor. May also be called a node.
  • IAB Integrated Access Backhaul
  • the base station 10 may be connected to the core network 30 via another base station 10 or directly.
  • the core network 30 may include, for example, at least one of Evolved Packet Core (EPC), 5G Core Network (5GCN), Next Generation Core (NGC), and the like.
  • EPC Evolved Packet Core
  • 5GCN 5G Core Network
  • NGC Next Generation Core
  • the core network 30 includes, for example, User Plane Function (UPF), Access and Mobility Management Function (AMF), Session Management Function (SMF), Unified Data Management (UDM), Application Function (AF), Data Network (DN), and Location Manager.
  • UPF User Plane Function
  • AMF Access and Mobility Management Function
  • SMF Session Management Function
  • UDM Unified Data Management
  • AF Application Function
  • DN Data Network
  • NF agement Network Functions
  • NF such as Function (LMF) and Operation, Administration and Maintenance (Management) (OAM)
  • multiple functions may be provided by one network node.
  • communication with an external network eg, the Internet
  • the user terminal 20 may be a terminal compatible with at least one of communication systems such as LTE, LTE-A, and 5G.
  • an orthogonal frequency division multiplexing (OFDM)-based wireless access method may be used.
  • OFDM orthogonal frequency division multiplexing
  • CP-OFDM Cyclic Prefix OFDM
  • DFT-s-OFDM Discrete Fourier Transform Spread OFDM
  • OFDMA Orthogonal Frequency Division Multiple Access
  • SC-FDMA Single Carrier Frequency Division Multiple Access
  • a wireless access method may also be called a waveform.
  • other wireless access methods for example, other single carrier transmission methods, other multicarrier transmission methods
  • the UL and DL radio access methods may be used as the UL and DL radio access methods.
  • the downlink channels include a physical downlink shared channel (PDSCH) shared by each user terminal 20, a broadcast channel (physical broadcast channel (PBCH)), and a downlink control channel (physical downlink control). Channel (PDCCH)) or the like may be used.
  • PDSCH physical downlink shared channel
  • PBCH physical broadcast channel
  • PDCCH downlink control channel
  • uplink channels include a physical uplink shared channel (PUSCH) shared by each user terminal 20, an uplink control channel (PUCCH), and a random access channel. (Physical Random Access Channel (PRACH)) or the like may be used.
  • PUSCH physical uplink shared channel
  • PUCCH uplink control channel
  • PRACH Physical Random Access Channel
  • User data, upper layer control information, System Information Block (SIB), etc. are transmitted by the PDSCH.
  • User data, upper layer control information, etc. may be transmitted by PUSCH.
  • a Master Information Block (MIB) may be transmitted via the PBCH.
  • Lower layer control information may be transmitted by PDCCH.
  • the lower layer control information may include, for example, downlink control information (DCI) that includes scheduling information for at least one of PDSCH and PUSCH.
  • DCI downlink control information
  • DCI that schedules PDSCH may be called DL assignment, DL DCI, etc.
  • DCI that schedules PUSCH may be called UL grant, UL DCI, etc.
  • PDSCH may be replaced with DL data
  • PUSCH may be replaced with UL data.
  • a control resource set (CONtrol REsource SET (CORESET)) and a search space may be used to detect the PDCCH.
  • CORESET corresponds to a resource for searching DCI.
  • the search space corresponds to a search area and a search method for PDCCH candidates (PDCCH candidates).
  • PDCCH candidates PDCCH candidates
  • One CORESET may be associated with one or more search spaces. The UE may monitor the CORESET associated with a certain search space based on the search space configuration.
  • One search space may correspond to PDCCH candidates corresponding to one or more aggregation levels.
  • One or more search spaces may be referred to as a search space set. Note that “search space”, “search space set”, “search space setting”, “search space set setting”, “CORESET”, “CORESET setting”, etc. in the present disclosure may be read interchangeably.
  • the PUCCH allows channel state information (CSI), delivery confirmation information (for example, may be called Hybrid Automatic Repeat Request ACKnowledgement (HARQ-ACK), ACK/NACK, etc.), and scheduling request ( Uplink Control Information (UCI) including at least one of SR)) may be transmitted.
  • CSI channel state information
  • delivery confirmation information for example, may be called Hybrid Automatic Repeat Request ACKnowledgement (HARQ-ACK), ACK/NACK, etc.
  • UCI Uplink Control Information including at least one of SR
  • a random access preamble for establishing a connection with a cell may be transmitted by PRACH.
  • downlinks, uplinks, etc. may be expressed without adding "link”.
  • various channels may be expressed without adding "Physical” at the beginning.
  • a synchronization signal (SS), a downlink reference signal (DL-RS), and the like may be transmitted.
  • the DL-RS includes a cell-specific reference signal (CRS), a channel state information reference signal (CSI-RS), and a demodulation reference signal (DeModulation).
  • Reference Signal (DMRS)), Positioning Reference Signal (PRS), Phase Tracking Reference Signal (PTRS), etc. may be transmitted.
  • 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 be called an SS/PBCH block, SS Block (SSB), etc. Note that SS, SSB, etc. may also be called reference signals.
  • DMRS Downlink Reference Signal
  • UL-RS uplink reference signals
  • SRS Sounding Reference Signal
  • DMRS demodulation reference signals
  • UE-specific reference signal user terminal-specific reference signal
  • FIG. 13 is a diagram illustrating an example of the configuration of a base station according to an embodiment.
  • the base station 10 includes a control section 110, a transmitting/receiving section 120, a transmitting/receiving antenna 130, and a transmission line interface 140. Note that one or more of each of the control unit 110, the transmitting/receiving unit 120, the transmitting/receiving antenna 130, and the transmission path interface 140 may be provided.
  • this example mainly shows functional blocks that are characteristic of the present embodiment, and it may be assumed that the base station 10 also has other functional blocks necessary for wireless communication. A part of the processing of each unit described below may be omitted.
  • the control unit 110 controls the entire base station 10.
  • the control unit 110 can be configured from a controller, a control circuit, etc., which will be explained based on common recognition in the technical field related to the present disclosure.
  • the control unit 110 may control signal generation, scheduling (e.g., resource allocation, mapping), and the like.
  • the control unit 110 may control transmission and reception, measurement, etc. using the transmitting/receiving unit 120, the transmitting/receiving antenna 130, and the transmission path interface 140.
  • the control unit 110 may generate data, control information, a sequence, etc. to be transmitted as a signal, and may transfer the generated data to the transmitting/receiving unit 120.
  • the control unit 110 may perform communication channel call processing (setting, release, etc.), status management of the base station 10, radio resource management, and the like.
  • the transmitting/receiving section 120 may include a baseband section 121, a radio frequency (RF) section 122, and a measuring section 123.
  • the baseband section 121 may include a transmission processing section 1211 and a reception processing section 1212.
  • the transmitter/receiver unit 120 includes a transmitter/receiver, an RF circuit, a baseband circuit, a filter, a phase shifter, a measurement circuit, a transmitter/receiver circuit, etc., which are explained based on common understanding in the technical field related to the present disclosure. be able to.
  • the transmitting/receiving section 120 may be configured as an integrated transmitting/receiving section, or may be configured from a transmitting section and a receiving section.
  • the transmitting section may include a transmitting processing section 1211 and an RF section 122.
  • the reception section may include a reception processing section 1212, an RF section 122, and a measurement section 123.
  • the transmitting/receiving antenna 130 can be configured from an antenna described based on common recognition in the technical field related to the present disclosure, such as an array antenna.
  • the transmitter/receiver 120 may transmit the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc.
  • the transmitter/receiver 120 may receive the above-mentioned uplink channel, uplink reference signal, and the like.
  • the transmitting/receiving unit 120 may form at least one of a transmitting beam and a receiving beam using digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), or the like.
  • digital beamforming e.g., precoding
  • analog beamforming e.g., phase rotation
  • the transmitting/receiving unit 120 (transmission processing unit 1211) performs Packet Data Convergence Protocol (PDCP) layer processing, Radio Link Control (RLC) layer processing (for example, RLC retransmission control), Medium Access Control (MAC) layer processing (for example, HARQ retransmission control), etc. may be performed to generate a bit string to be transmitted.
  • PDCP Packet Data Convergence Protocol
  • RLC Radio Link Control
  • MAC Medium Access Control
  • HARQ retransmission control for example, HARQ retransmission control
  • the transmitting/receiving unit 120 performs channel encoding (which may include error correction encoding), modulation, mapping, filter processing, and discrete Fourier transform (DFT) on the bit string to be transmitted.
  • a baseband signal may be output by performing transmission processing such as processing (if necessary), Inverse Fast Fourier Transform (IFFT) processing, precoding, and digital-to-analog conversion.
  • IFFT Inverse Fast Fourier Transform
  • the transmitting/receiving unit 120 may perform modulation, filter processing, amplification, etc. on the baseband signal in a radio frequency band, and may transmit the signal in the radio frequency band via the transmitting/receiving antenna 130. .
  • the transmitting/receiving section 120 may perform amplification, filter processing, demodulation into a baseband signal, etc. on the radio frequency band signal received by the transmitting/receiving antenna 130.
  • the transmitting/receiving unit 120 (reception processing unit 1212) performs analog-to-digital conversion, fast Fourier transform (FFT) processing, and inverse discrete Fourier transform (IDFT) on the acquired baseband signal. )) processing (if necessary), applying reception processing such as filter processing, demapping, demodulation, decoding (which may include error correction decoding), MAC layer processing, RLC layer processing and PDCP layer processing, User data etc. may also be acquired.
  • FFT fast Fourier transform
  • IDFT inverse discrete Fourier transform
  • the transmitting/receiving unit 120 may perform measurements regarding the received signal.
  • the measurement unit 123 may perform Radio Resource Management (RRM) measurement, Channel State Information (CSI) measurement, etc. based on the received signal.
  • the measurement unit 123 measures received power (for example, Reference Signal Received Power (RSRP)), reception quality (for example, Reference Signal Received Quality (RSRQ), Signal to Interference plus Noise Ratio (SINR), Signal to Noise Ratio (SNR) )) , signal strength (for example, Received Signal Strength Indicator (RSSI)), propagation path information (for example, CSI), etc. may be measured.
  • the measurement results may be output to the control unit 110.
  • the transmission path interface 140 transmits and receives signals (backhaul signaling) between devices included in the core network 30 (for example, network nodes providing NF), other base stations 10, etc., and provides information for the user terminal 20.
  • signals backhaul signaling
  • devices included in the core network 30 for example, network nodes providing NF, other base stations 10, etc.
  • User data user plane data
  • control plane data etc. may be acquired and transmitted.
  • the transmitting unit and receiving unit of the base station 10 in the present disclosure may be configured by at least one of the transmitting/receiving unit 120, the transmitting/receiving antenna 130, and the transmission path interface 140.
  • control unit 110 may input information to an Artificial Intelligence (AI) model to obtain an output.
  • AI Artificial Intelligence
  • the control unit 110 may estimate the position of the terminal based on the information regarding the characteristics.
  • the transmitting/receiving unit 120 may transmit information regarding the location of the terminal based on the output of the control unit 110.
  • FIG. 14 is a diagram illustrating an example of the configuration of a user terminal according to an embodiment.
  • the user terminal 20 includes a control section 210, a transmitting/receiving section 220, and a transmitting/receiving antenna 230. Note that one or more of each of the control unit 210, the transmitting/receiving unit 220, and the transmitting/receiving antenna 230 may be provided.
  • this example mainly shows functional blocks that are characteristic of the present embodiment, and it may be assumed that the user terminal 20 also has other functional blocks necessary for wireless communication. A part of the processing of each unit described below may be omitted.
  • the control unit 210 controls the entire user terminal 20.
  • the control unit 210 can be configured from a controller, a control circuit, etc., which will be explained based on common recognition in the technical field related to the present disclosure.
  • the control unit 210 may control signal generation, mapping, etc.
  • the control unit 210 may control transmission and reception using the transmitting/receiving unit 220 and the transmitting/receiving antenna 230, measurement, and the like.
  • the control unit 210 may generate data, control information, sequences, etc. to be transmitted as a signal, and may transfer the generated data to the transmitting/receiving unit 220.
  • the transmitting/receiving section 220 may include a baseband section 221, an RF section 222, and a measuring section 223.
  • the baseband section 221 may include a transmission processing section 2211 and a reception processing section 2212.
  • the transmitting/receiving unit 220 can be configured from a transmitter/receiver, an RF circuit, a baseband circuit, a filter, a phase shifter, a measuring circuit, a transmitting/receiving circuit, etc., which are explained based on common recognition in the technical field related to the present disclosure.
  • the transmitting/receiving section 220 may be configured as an integrated transmitting/receiving section, or may be configured from a transmitting section and a receiving section.
  • the transmitting section may include a transmitting processing section 2211 and an RF section 222.
  • the reception section may include a reception processing section 2212, an RF section 222, and a measurement section 223.
  • the transmitting/receiving antenna 230 can be configured from an antenna, such as an array antenna, as described based on common recognition in the technical field related to the present disclosure.
  • the transmitter/receiver 220 may receive the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc.
  • the transmitter/receiver 220 may transmit the above-mentioned uplink channel, uplink reference signal, and the like.
  • the transmitting/receiving unit 220 may form at least one of a transmitting beam and a receiving beam using digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), or the like.
  • digital beamforming e.g., precoding
  • analog beamforming e.g., phase rotation
  • the transmission/reception unit 220 (transmission processing unit 2211) performs PDCP layer processing, RLC layer processing (e.g. RLC retransmission control), MAC layer processing (e.g. , HARQ retransmission control), etc., to generate a bit string to be transmitted.
  • RLC layer processing e.g. RLC retransmission control
  • MAC layer processing e.g. , HARQ retransmission control
  • the transmitting/receiving unit 220 (transmission processing unit 2211) performs channel encoding (which may include error correction encoding), modulation, mapping, filter processing, DFT processing (as necessary), and IFFT processing on the bit string to be transmitted. , precoding, digital-to-analog conversion, etc., and output a baseband signal.
  • DFT processing may be based on the settings of transform precoding.
  • the transmitting/receiving unit 220 transmits the above processing in order to transmit the channel using the DFT-s-OFDM waveform.
  • DFT processing may be performed as the transmission processing, or if not, DFT processing may not be performed as the transmission processing.
  • the transmitting/receiving unit 220 may perform modulation, filter processing, amplification, etc. on the baseband signal in a radio frequency band, and may transmit the signal in the radio frequency band via the transmitting/receiving antenna 230. .
  • the transmitting/receiving section 220 may perform amplification, filter processing, demodulation into a baseband signal, etc. on the radio frequency band signal received by the transmitting/receiving antenna 230.
  • the transmission/reception unit 220 (reception processing unit 2212) performs analog-to-digital conversion, FFT processing, IDFT processing (if necessary), filter processing, demapping, demodulation, and decoding (error correction) on the acquired baseband signal. (which may include decoding), MAC layer processing, RLC layer processing, and PDCP layer processing may be applied to obtain user data and the like.
  • the transmitting/receiving unit 220 may perform measurements regarding the received signal.
  • the measurement unit 223 may perform RRM measurement, CSI measurement, etc. based on the received signal.
  • the measurement unit 223 may measure received power (for example, RSRP), reception quality (for example, RSRQ, SINR, SNR), signal strength (for example, RSSI), propagation path information (for example, CSI), and the like.
  • the measurement results may be output to the control unit 210.
  • the transmitting unit and receiving unit of the user terminal 20 in the present disclosure may be configured by at least one of the transmitting/receiving unit 220 and the transmitting/receiving antenna 230.
  • control unit 210 may input information to an artificial intelligence (AI) model to obtain an output.
  • AI artificial intelligence
  • the control unit 210 may estimate the position of the terminal based on the information regarding the characteristics.
  • the transmitting/receiving unit 220 may transmit information regarding the location of the terminal based on the output of the control unit 210.
  • each functional block may be realized using one physically or logically coupled device, or may be realized using two or more physically or logically separated devices directly or indirectly (e.g. , wired, wireless, etc.) and may be realized using a plurality of these devices.
  • the functional block may be realized by combining software with the one device or the plurality of devices.
  • functions include judgment, decision, judgement, calculation, calculation, processing, derivation, investigation, exploration, confirmation, reception, transmission, output, access, solution, selection, selection, establishment, comparison, assumption, expectation, and consideration. , broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, assigning, etc.
  • a functional block (configuration unit) that performs transmission may be called a transmitting unit, a transmitter, or the like. In either case, as described above, the implementation method is not particularly limited.
  • a base station, a user terminal, etc. in an embodiment of the present disclosure may function as a computer that performs processing of the wireless communication method of the present disclosure.
  • FIG. 15 is a diagram illustrating an example of the hardware configuration of a base station and a user terminal according to an embodiment.
  • the base station 10 and user terminal 20 described above may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, etc. .
  • the hardware configuration of the base station 10 and the user terminal 20 may be configured to include one or more of each device shown in the figure, or may be configured not to include some of the devices.
  • processor 1001 may be implemented using one or more chips.
  • Each function in the base station 10 and the user terminal 20 is performed by, for example, loading predetermined software (program) onto hardware such as a processor 1001 and a memory 1002, so that the processor 1001 performs calculations and communicates via the communication device 1004. This is achieved by controlling at least one of reading and writing data in the memory 1002 and storage 1003.
  • predetermined software program
  • the processor 1001 operates an operating system to control the entire computer.
  • the processor 1001 may be configured by a central processing unit (CPU) that includes interfaces with peripheral devices, a control device, an arithmetic unit, registers, and the like.
  • CPU central processing unit
  • the above-mentioned control unit 110 (210), transmitting/receiving unit 120 (220), etc. may be realized by the processor 1001.
  • the processor 1001 reads programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 to the memory 1002, and executes various processes in accordance with these.
  • programs program codes
  • software modules software modules
  • data etc.
  • the control unit 110 may be realized by a control program stored in the memory 1002 and operated in the processor 1001, and other functional blocks may also be realized in the same way.
  • the memory 1002 is a computer-readable recording medium, and includes at least one of Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically EPROM (EEPROM), Random Access Memory (RAM), and other suitable storage media. It may be composed of one. Memory 1002 may be called a register, cache, main memory, or the like.
  • the memory 1002 can store executable programs (program codes), software modules, and the like to implement a wireless communication method according to an embodiment of the present disclosure.
  • the storage 1003 is a computer-readable recording medium, such as a flexible disk, a floppy (registered trademark) disk, a magneto-optical disk (for example, a compact disk (CD-ROM), etc.), a digital versatile disk, removable disk, hard disk drive, smart card, flash memory device (e.g., card, stick, key drive), magnetic stripe, database, server, or other suitable storage medium. It may be configured by Storage 1003 may also be called an auxiliary storage device.
  • a computer-readable recording medium such as a flexible disk, a floppy (registered trademark) disk, a magneto-optical disk (for example, a compact disk (CD-ROM), etc.), a digital versatile disk, removable disk, hard disk drive, smart card, flash memory device (e.g., card, stick, key drive), magnetic stripe, database, server, or other suitable storage medium. It may be configured by Storage 1003 may also be called an auxiliary storage device.
  • the communication device 1004 is hardware (transmission/reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as a network device, network controller, network card, communication module, etc., for example.
  • the communication device 1004 includes, for example, a high frequency switch, a duplexer, a filter, a frequency synthesizer, etc. in order to realize at least one of frequency division duplex (FDD) and time division duplex (TDD). It may be configured to include.
  • FDD frequency division duplex
  • TDD time division duplex
  • the transmitter/receiver 120 (220) may be physically or logically separated into a transmitter 120a (220a) and a receiver 120b (220b).
  • the input device 1005 is an input device (eg, keyboard, mouse, microphone, switch, button, sensor, etc.) that accepts input from the outside.
  • the output device 1006 is an output device (for example, a display, a speaker, a light emitting diode (LED) lamp, etc.) that performs output to the outside. Note that the input device 1005 and the output device 1006 may have an integrated configuration (for example, a touch panel).
  • each device such as the processor 1001 and the memory 1002 is connected by a bus 1007 for communicating information.
  • the bus 1007 may be configured using a single bus, or may be configured using different buses for each device.
  • the base station 10 and user terminal 20 also include a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), etc. It may be configured to include hardware, and a part or all of each functional block may be realized using the hardware. For example, processor 1001 may be implemented using at least one of these hardwares.
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • PLD programmable logic device
  • FPGA field programmable gate array
  • channel, symbol and signal may be interchanged.
  • the signal may be a message.
  • the reference signal may also be abbreviated as RS, and may be called a pilot, pilot signal, etc. depending on the applicable standard.
  • a component carrier CC may be called a cell, a frequency carrier, a carrier frequency, or the like.
  • a radio frame may be composed of one or more periods (frames) in the time domain.
  • Each of the one or more periods (frames) constituting a radio frame may be called a subframe.
  • a subframe may be composed of one or more slots in the time domain.
  • a subframe may have a fixed time length (eg, 1 ms) that does not depend on numerology.
  • the numerology may be a communication parameter applied to at least one of transmission and reception of a certain signal or channel.
  • Numerology includes, for example, subcarrier spacing (SCS), bandwidth, symbol length, cyclic prefix length, transmission time interval (TTI), number of symbols per TTI, and radio frame configuration. , a specific filtering process performed by the transceiver in the frequency domain, a specific windowing process performed by the transceiver in the time domain, etc.
  • a slot may 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. Furthermore, a slot may be a time unit based on numerology.
  • OFDM Orthogonal Frequency Division Multiplexing
  • SC-FDMA Single Carrier Frequency Division Multiple Access
  • a slot may include multiple mini-slots. Each minislot may be made up of one or more symbols in the time domain. Furthermore, a mini-slot may also be called a sub-slot. A minislot may be made up of fewer symbols than a slot.
  • PDSCH (or PUSCH) transmitted in time units larger than minislots may be referred to as PDSCH (PUSCH) mapping type A.
  • PDSCH (or PUSCH) transmitted using minislots may be referred to as PDSCH (PUSCH) mapping type B.
  • Radio frames, subframes, slots, minislots, and symbols all represent time units when transmitting signals. Other names may be used for the radio frame, subframe, slot, minislot, and symbol. Note that time units such as frames, subframes, slots, minislots, and symbols in the present disclosure may be read interchangeably.
  • one subframe may be called a TTI
  • a plurality of consecutive subframes may be called a TTI
  • one slot or one minislot may be called a TTI.
  • at least one of the subframe and TTI may be a subframe (1ms) in existing LTE, a period shorter than 1ms (for example, 1-13 symbols), or a period longer than 1ms. It may be.
  • the unit representing the TTI may be called a slot, minislot, etc. instead of a subframe.
  • TTI refers to, for example, the minimum time unit for scheduling in wireless communication.
  • a base station performs scheduling to allocate radio resources (frequency bandwidth, transmission power, etc. that can be used by each user terminal) to each user terminal on a TTI basis.
  • radio resources frequency bandwidth, transmission power, etc. that can be used by each user terminal
  • the TTI may be a transmission time unit of a channel-coded data packet (transport block), a code block, a codeword, etc., or may be a processing unit of scheduling, link adaptation, etc. Note that when a TTI is given, the time interval (for example, the number of symbols) to which transport blocks, code blocks, code words, etc. are actually mapped may be shorter than the TTI.
  • one slot or one minislot is called a TTI
  • one or more TTIs may be the minimum time unit for scheduling.
  • the number of slots (minislot number) that constitutes the minimum time unit of the scheduling may be controlled.
  • a TTI having a time length of 1 ms may be called a normal TTI (TTI in 3GPP Rel. 8-12), normal TTI, long TTI, normal subframe, normal subframe, long subframe, slot, etc.
  • TTI TTI in 3GPP Rel. 8-12
  • normal TTI long TTI
  • normal subframe normal subframe
  • long subframe slot
  • TTI that is shorter than the normal TTI may be referred to as an abbreviated TTI, short TTI, partial or fractional TTI, shortened subframe, short subframe, minislot, subslot, slot, etc.
  • long TTI for example, normal TTI, subframe, etc.
  • short TTI for example, short TTI, etc. It may also be read as a TTI having the above TTI length.
  • a resource block is a resource allocation unit in the time domain and frequency domain, and may include one or more continuous subcarriers (subcarriers) in the frequency domain.
  • the number of subcarriers included in an RB may be the same regardless of the numerology, and may be 12, for example.
  • the number of subcarriers included in an RB may be determined based on numerology.
  • an RB may include one or more symbols in the time domain, and may have a length of one slot, one minislot, one subframe, or one TTI.
  • One TTI, one subframe, etc. may each be composed of one or more resource blocks.
  • one or more RBs include a physical resource block (Physical RB (PRB)), a sub-carrier group (SCG), a resource element group (REG), a PRB pair, and an RB. They may also be called pairs.
  • PRB Physical RB
  • SCG sub-carrier group
  • REG resource element group
  • PRB pair an RB. They may also be called pairs.
  • a resource block may be configured by one or more resource elements (REs).
  • REs resource elements
  • 1 RE may be a radio resource region of 1 subcarrier and 1 symbol.
  • Bandwidth Part (also called partial bandwidth, etc.) refers to a subset of consecutive common resource blocks (RB) for a certain numerology in a certain carrier.
  • the common RB may be specified by an RB index based on a common reference point of the carrier.
  • PRBs may be defined in a BWP and numbered within that BWP.
  • BWP may include UL BWP (BWP for UL) and DL BWP (BWP for DL).
  • BWP UL BWP
  • BWP for DL DL BWP
  • One or more BWPs may be configured within one carrier for a UE.
  • At least one of the configured BWPs may be active and the UE may not expect to transmit or receive a given signal/channel outside of the active BWP.
  • “cell”, “carrier”, etc. in the present disclosure may be replaced with "BWP”.
  • the structures of the radio frame, subframe, slot, minislot, symbol, etc. described above are merely examples.
  • the number of subframes included in a radio frame, the number of slots per subframe or radio frame, the number of minislots included in a slot, the number of symbols and RBs included in a slot or minislot, the number of symbols included in an RB The number of subcarriers, the number of symbols within a TTI, the symbol length, the cyclic prefix (CP) length, and other configurations can be changed in various ways.
  • radio resources may be indicated by a predetermined index.
  • data, instructions, commands, information, signals, bits, symbols, chips, etc. which may be referred to throughout the above description, may refer to voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, light fields or photons, or any of these. It may also be represented by a combination of
  • information, signals, etc. may be output from the upper layer to the lower layer and from the lower layer to at least one of the upper layer.
  • Information, signals, etc. may be input and output via multiple network nodes.
  • Input/output information, signals, etc. may be stored in a specific location (for example, memory) or may be managed using a management table. Information, signals, etc. that are input and output can be overwritten, updated, or added. The output information, signals, etc. may be deleted. The input information, signals, etc. may be transmitted to other devices.
  • Notification of information is not limited to the aspects/embodiments described in this disclosure, and may be performed using other methods.
  • the notification of information in this disclosure may be physical layer signaling (e.g., Downlink Control Information (DCI), Uplink Control Information (UCI)), upper 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 It may be carried out by physical layer signaling (e.g., Downlink Control Information (DCI), Uplink Control Information (UCI)), upper 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 It may be carried out by
  • the physical layer signaling may also be called Layer 1/Layer 2 (L1/L2) control information (L1/L2 control signal), L1 control information (L1 control signal), etc.
  • RRC signaling may be called an RRC message, and may be, for example, an RRC Connection Setup message, an RRC Connection Reconfiguration message, or the like.
  • MAC signaling may be notified using, for example, a MAC Control Element (CE).
  • CE MAC Control Element
  • notification of prescribed information is not limited to explicit notification, but may be made implicitly (for example, by not notifying the prescribed information or by providing other information) (by notification).
  • the determination may be made by a value expressed by 1 bit (0 or 1), or by a boolean value expressed by true or false. , may be performed by numerical comparison (for example, comparison with a predetermined value).
  • Software includes instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, whether referred to as software, firmware, middleware, microcode, hardware description language, or by any other name. , should be broadly construed to mean an application, software application, software package, routine, subroutine, object, executable, thread of execution, procedure, function, etc.
  • software, instructions, information, etc. may be sent and received via a transmission medium.
  • a transmission medium such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), etc.
  • wired technology such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), etc.
  • wireless technology such as infrared, microwave, etc.
  • Network may refer to devices (eg, base stations) included in the network.
  • precoding "precoding weight”
  • QCL quadsi-co-location
  • TCI state "Transmission Configuration Indication state
  • space space
  • spatial relation "spatial domain filter”
  • transmission power "phase rotation”
  • antenna port "antenna port group”
  • layer "number of layers”
  • Terms such as “rank”, “resource”, “resource set”, “resource group”, “beam”, “beam width”, “beam angle”, “antenna”, “antenna element”, and “panel” are interchangeable.
  • Base Station BS
  • Wireless base station 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,” “component carrier,” and the like
  • a base station is sometimes referred to by terms such as macrocell, small cell, femtocell, and picocell.
  • a base station can accommodate one or more (eg, three) cells. If a base station accommodates multiple cells, the overall coverage area of the base station can be partitioned into multiple smaller areas, and each smaller area is connected to a base station subsystem (e.g., an indoor small base station (Remote Radio Communication services can also be provided by the Head (RRH)).
  • a base station subsystem e.g., an indoor small base station (Remote Radio Communication services can also be provided by the Head (RRH)
  • RRH Remote Radio Communication services
  • the term “cell” or “sector” refers to part or all of the coverage area of a base station and/or base station subsystem that provides communication services in this coverage.
  • a base station transmitting information to a terminal may be interchanged with the base station instructing the terminal to control/operate based on the information.
  • MS Mobile Station
  • UE User Equipment
  • a mobile station is a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal. , handset, user agent, mobile client, client, or some other suitable terminology.
  • At least one of a base station and a mobile station may be called a transmitting device, a receiving device, a wireless communication device, etc.
  • a transmitting device may be called a transmitting device, a receiving device, a wireless communication device, etc.
  • the base station and the mobile station may be a device mounted on a moving object, the moving object itself, or the like.
  • the moving body refers to a movable object, and the moving speed is arbitrary, and naturally includes cases where the moving body is stopped.
  • the mobile objects include, for example, vehicles, transport vehicles, automobiles, motorcycles, bicycles, connected cars, excavators, bulldozers, wheel loaders, dump trucks, forklifts, trains, buses, carts, rickshaws, and ships (ships and other watercraft). , including, but not limited to, airplanes, rockets, artificial satellites, drones, multicopters, quadcopters, balloons, and items mounted thereon.
  • the mobile object may be a mobile object that autonomously travels based on a travel command.
  • the moving object may be a vehicle (for example, a car, an airplane, etc.), an unmanned moving object (for example, a drone, a self-driving car, etc.), or a robot (manned or unmanned). ).
  • a vehicle for example, a car, an airplane, etc.
  • an unmanned moving object for example, a drone, a self-driving car, etc.
  • a robot manned or unmanned.
  • at least one of the base station and the mobile station includes devices that do not necessarily move during communication operations.
  • at least one of the base station and the mobile station may be an Internet of Things (IoT) device such as a sensor.
  • IoT Internet of Things
  • FIG. 16 is a diagram illustrating 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 (current sensor 50, (including 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 section 59, and a communication module 60. Be prepared.
  • the drive unit 41 is composed of, for example, at least one of an engine, a motor, and a hybrid of an engine and a motor.
  • the steering unit 42 includes at least a steering wheel (also referred to as a steering wheel), and is configured to steer at least one of the front wheels 46 and the rear wheels 47 based on the operation of the steering wheel operated by the user.
  • the electronic control unit 49 includes 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 section 49 may be called an electronic control unit (ECU).
  • the signals from the various sensors 50 to 58 include a current signal from the current sensor 50 that senses the current of the motor, a rotation speed signal of the front wheel 46/rear wheel 47 obtained by the rotation speed sensor 51, and a signal obtained by the air pressure sensor 52.
  • air pressure signals of the front wheels 46/rear wheels 47 a vehicle speed signal acquired by the vehicle speed sensor 53, an acceleration signal acquired by the acceleration sensor 54, a depression amount signal of the accelerator pedal 43 acquired by the accelerator pedal sensor 55, and a brake pedal sensor.
  • 56 a shift lever 45 operation signal obtained by the shift lever sensor 57, and an object detection sensor 58 for detecting obstacles, vehicles, pedestrians, etc. There are signals etc.
  • the information service department 59 includes various devices such as car navigation systems, audio systems, speakers, displays, televisions, and radios that provide (output) various information such as driving information, traffic information, and entertainment information, and these devices. It consists of one or more ECUs that control the The information service unit 59 provides various information/services (for example, multimedia information/multimedia services) to the occupants of the vehicle 40 using information acquired from an external device via the communication module 60 or the like.
  • various information/services for example, multimedia information/multimedia services
  • the information service unit 59 may include an input device (for example, a keyboard, a mouse, a microphone, a switch, a button, a sensor, a touch panel, etc.) that accepts input from the outside, and an output device that performs output to the outside (for example, display, speaker, LED lamp, touch panel, etc.).
  • an input device for example, a keyboard, a mouse, a microphone, a switch, a button, a sensor, a touch panel, etc.
  • an output device that performs output to the outside (for example, display, speaker, LED lamp, touch panel, etc.).
  • the driving support system unit 64 includes millimeter wave radar, Light Detection and Ranging (LiDAR), a camera, a positioning locator (for example, Global Navigation Satellite System (GNSS), etc.), and map information (for example, High Definition (HD)). maps, autonomous vehicle (AV) maps, etc.), gyro systems (e.g., inertial measurement units (IMUs), inertial navigation systems (INS), etc.), artificial intelligence ( Artificial Intelligence (AI) chips, AI processors, and other devices that provide functions to prevent accidents and reduce the driver's driving burden, as well as one or more devices that control these devices. It consists of an ECU. Further, the driving support system section 64 transmits and receives various information via the communication module 60, and realizes a driving support function or an automatic driving function.
  • LiDAR Light Detection and Ranging
  • GNSS Global Navigation Satellite System
  • HD High Definition
  • maps for example, autonomous vehicle (AV) maps, etc.
  • gyro systems e.g.,
  • the communication module 60 can communicate with the microprocessor 61 and components of the vehicle 40 via the communication port 63.
  • the communication module 60 communicates via the communication port 63 with 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, which are included in the vehicle 40.
  • Data (information) is transmitted and received between the axle 48, the microprocessor 61 and memory (ROM, RAM) 62 in the electronic control unit 49, and various sensors 50-58.
  • 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 external devices. For example, various information is transmitted and received with an external device via wireless communication.
  • the communication module 60 may be located either inside or outside the electronic control unit 49.
  • the external device may be, for example, the base station 10, user terminal 20, etc. described above.
  • the communication module 60 may be, for example, at least one of the base station 10 and the user terminal 20 described above (it may function as at least one of the base station 10 and the user terminal 20).
  • the communication module 60 receives signals from the various sensors 50 to 58 described above that are input to the electronic control unit 49, information obtained based on the signals, and input from the outside (user) obtained via the information service unit 59. At least one of the information based on the information may be transmitted to an external device via wireless communication.
  • the electronic control unit 49, various sensors 50-58, information service unit 59, etc. may be called an input unit that receives input.
  • the PUSCH transmitted by the communication module 60 may include information based on the above input.
  • the communication module 60 receives various information (traffic information, signal information, inter-vehicle information, etc.) transmitted from an external device, and displays it on the information service section 59 provided in the vehicle.
  • the information service unit 59 is an output unit that outputs information (for example, outputs information to devices such as a display and a speaker based on the PDSCH (or data/information decoded from the PDSCH) received by the communication module 60). may be called.
  • the communication module 60 also stores various information received from external devices into a memory 62 that can be used by the microprocessor 61. Based on the information stored in the memory 62, the microprocessor 61 controls the drive unit 41, steering unit 42, accelerator pedal 43, brake pedal 44, shift lever 45, left and right front wheels 46, and left and right rear wheels provided in the vehicle 40. 47, axle 48, various sensors 50-58, etc. may be controlled.
  • the base station in the present disclosure may be replaced by a user terminal.
  • communication between a base station and a user terminal is replaced with communication between multiple user terminals (for example, it may be called Device-to-Device (D2D), Vehicle-to-Everything (V2X), etc.).
  • D2D Device-to-Device
  • V2X Vehicle-to-Everything
  • each aspect/embodiment of the present disclosure may be applied.
  • the user terminal 20 may have the functions that the base station 10 described above has.
  • words such as "uplink” and “downlink” may be replaced with words corresponding to inter-terminal communication (for example, "sidelink”).
  • uplink channels, downlink channels, etc. may be replaced with sidelink channels.
  • the user terminal in the present disclosure may be replaced with a base station.
  • the base station 10 may have the functions that the user terminal 20 described above has.
  • the operations performed by the base station may be performed by its upper node in some cases.
  • various operations performed for communication with a terminal may be performed by the base station, one or more network nodes other than the base station (e.g. It is clear that this can be performed by a Mobility Management Entity (MME), a Serving-Gateway (S-GW), etc. (though not limited thereto), or a combination thereof.
  • MME Mobility Management Entity
  • S-GW Serving-Gateway
  • Each aspect/embodiment described in this disclosure may be used alone, in combination, or may be switched and used in accordance with execution. Further, the order of the processing procedures, sequences, flowcharts, etc. of each aspect/embodiment described in this disclosure may be changed as long as there is no contradiction. For example, the methods described in this disclosure use an example order to present elements of the various steps and are not limited to the particular order presented.
  • LTE Long Term Evolution
  • LTE-A LTE-Advanced
  • LTE-B LTE-Beyond
  • SUPER 3G IMT-Advanced
  • 4G 4th generation mobile communication system
  • 5G 5th generation mobile communication system
  • 6G 6th generation mobile communication system
  • xG x is an integer or decimal number, for example
  • Future Radio Access FAA
  • RAT New-Radio Access Technology
  • NR New Radio
  • NX New Radio Access
  • FX Future Generation Radio Access
  • G Global System for Mobile Communications
  • CDMA2000 Ultra Mobile Broadband
  • UMB Ultra Mobile Broadband
  • IEEE 802 .11 Wi-Fi (registered trademark)
  • IEEE 802.16 WiMAX (registered trademark)
  • IEEE 802.20 Ultra-WideBand (UWB), Bluetooth (registered trademark), and other appropriate wireless communication methods.
  • the present invention may be applied to systems to be used, next-generation systems expanded, modified, created, or defined based on these
  • the phrase “based on” does not mean “based solely on” unless explicitly stated otherwise. In other words, the phrase “based on” means both “based only on” and “based at least on.”
  • any reference to elements using the designations "first,” “second,” etc. does not generally limit the amount or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Thus, reference to a first and second element does not imply that only two elements may be employed or that the first element must precede the second element in any way.
  • determining may encompass a wide variety of actions. For example, “judgment” can mean judging, calculating, computing, processing, deriving, investigating, looking up, search, inquiry ( For example, searching in a table, database, or other data structure), ascertaining, etc. may be considered to be “determining.”
  • judgment (decision) includes receiving (e.g., receiving information), transmitting (e.g., sending information), input (input), output (output), access ( may be considered to be “determining”, such as accessing data in memory (eg, accessing data in memory).
  • judgment is considered to mean “judging” resolving, selecting, choosing, establishing, comparing, etc. Good too.
  • judgment (decision) may be considered to be “judgment (decision)” of some action.
  • connection refers to any connection or coupling, direct or indirect, between two or more elements.
  • the coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connection” may be replaced with "access.”
  • microwave when two elements are connected, they may be connected using one or more electrical wires, cables, printed electrical connections, etc., as well as in the radio frequency domain, microwave can be considered to be “connected” or “coupled” to each other using electromagnetic energy having wavelengths in the light (both visible and invisible) range.
  • a and B are different may mean “A and B are different from each other.” Note that the term may also mean that "A and B are each different from C”. Terms such as “separate” and “coupled” may also be interpreted similarly to “different.”
  • words meaning "good”, “bad”, “large”, “small”, “high”, “low”, “early”, “slow”, etc. may be read interchangeably. (Not limited to original, comparative, and superlative).
  • words meaning "good”, “bad”, “large”, “small”, “high”, “low”, “early”, “slow”, etc. are replaced with “i-th”. They may be interchanged as expressions (not limited to the original, comparative, and superlative) (for example, “the highest” may be interchanged with “the i-th highest”).

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

Un terminal selon un mode de réalisation de la présente divulgation comprend : une unité de commande qui entre des informations dans un modèle d'intelligence artificielle (IA) et obtient une sortie ; et une unité de transmission qui transmet des informations relatives à la position du terminal sur la base de la sortie. Le mode de réalisation de la présente divulgation permet d'obtenir une réduction de surdébit, une estimation de canal et une utilisation de ressources appropriées.
PCT/JP2022/021638 2022-05-26 2022-05-26 Terminal, procédé de communication radio et station de base WO2023228382A1 (fr)

Priority Applications (1)

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PCT/JP2022/021638 WO2023228382A1 (fr) 2022-05-26 2022-05-26 Terminal, procédé de communication radio et station de base

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/JP2022/021638 WO2023228382A1 (fr) 2022-05-26 2022-05-26 Terminal, procédé de communication radio et station de base

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WO2023228382A1 true WO2023228382A1 (fr) 2023-11-30

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Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
CATT: "Discussion on other aspects on AI/ML for positioning", 3GPP TSG RAN WG1 #109-E R1-2203456, 29 April 2022 (2022-04-29), XP052152988 *
CMCC: "Discussion on other aspects on AI/ML for positioning accuracy enhancement", 3GPP TSG RAN WG1 #109-E R1-2204300, 29 April 2022 (2022-04-29), XP052153464 *
QUALCOMM INCORPORATED: "Other aspects on AI-ML for positioning accuracy enhancement", 3GPP TSG RAN WG1 #109-E R1- 2205029, 29 April 2022 (2022-04-29), XP052144138 *
VIVO: "Evaluation on AI/ML for positioning accuracy enhancement", 3GPP TSG RAN WG1 #109-E R1-2203554, 29 April 2022 (2022-04-29), XP052153029 *

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