Terminal, wireless communication method, and base station

Abstract

A terminal according to one embodiment of the present disclosure is characterized by being provided with: a control means for acquiring tagged data for training an artificial intelligence (AI) model, the tagged data including information relating to the position of the terminal; and a transmission unit that transmits the tagged data. According to one mode of the present disclosure, appropriate overhead reduction / channel estimation / resource utilization can be realized.

Description

Technical Field

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

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

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

[0004] Prior art literature

[0005] Non-patent literature

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

[0007] Problems to be solved by the invention

[0008] For future wireless communication technologies, research is underway to apply artificial intelligence (AI) technologies such as machine learning (ML) to control and manage networks and devices. For example, research is underway to apply AI technologies to determine (estimate) the location (positioning) of user terminals (user equipment (UE)).

[0009] However, the specific contents of such UE position estimation have not been studied yet. If these are not properly regulated, appropriate overhead reduction / high-precision channel estimation / efficient resource utilization cannot be achieved, and there is a concern that the improvement of communication throughput / communication quality will be suppressed.

[0010] Therefore, one of the objects of the present disclosure is to provide a terminal, a wireless communication method, and a base station that can achieve appropriate overhead reduction / channel estimation / resource utilization.

[0011] Means for solving problems

[0012] A terminal according to one aspect of the present disclosure is characterized by comprising: a control unit that obtains labeled data including information related to a location of the terminal and used for training an artificial intelligence (AI) model; and a transmission unit that transmits the labeled data.

[0013] Effects of the Invention

[0014] According to one aspect of the present disclosure, appropriate overhead reduction / channel estimation / resource utilization can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a diagram showing an example of a framework for managing AI models.

[0016] Figure 2 This is a diagram showing an example of specifying an AI model.

[0017] Figure 3 This is a diagram showing an example of a UE positioning method.

[0018] Figure 4 This is a diagram showing an example of a UE positioning method.

[0019] Figure 5 This is a diagram showing an example of a UE positioning method.

[0020] Figure 6 This is a diagram showing an example of a UE positioning method.

[0021] Figure 7 This is a diagram showing an example of AI model inference according to the first embodiment.

[0022] Figure 8 This is a diagram showing an example of AI model inference according to the second embodiment.

[0023] Fig. 9 This is a diagram showing an example of AI model training involved in the third embodiment.

[0024] Fig.10 This is a diagram showing an example of a schematic configuration of a wireless communication system according to an embodiment.

[0025] Fig.11 This is a diagram showing an example of the configuration of a base station according to an embodiment.

[0026] Fig.12 This is a diagram showing an example of a configuration of a user terminal according to an embodiment.

[0027] Fig.13 This is a diagram showing an example of the hardware configuration of a base station and a user terminal according to an embodiment.

[0028] Fig.14 This is a diagram showing an example of a vehicle according to an embodiment. DETAILED DESCRIPTION

[0029] (Application of Artificial Intelligence (AI) technology to wireless communications)

[0030] For future wireless communication technologies, research is underway to apply AI technologies such as machine learning (ML) to control and manage networks and devices.

[0031] For example, for future wireless communication technologies, research is being conducted on the flexible use of AI technology to improve channel state information (Channel State Information Reference Signal (CSI)) feedback (e.g., overhead reduction, accuracy improvement, prediction), improve beam management (e.g., accuracy improvement, prediction in the time / spatial domain), improve position measurement (e.g., improvement in position estimation / prediction), etc.

[0032] Figure 1 This is a diagram showing an example of a framework for managing an AI model. In this example, each stage associated with an AI model is shown by a box. This example also represents the life cycle management of an AI model.

[0033] The data collection phase is equivalent to the phase of collecting data for the generation / update of AI models. The data collection phase may also include data collation (e.g., deciding which data to forward for model training / model inference), data forwarding (e.g., forwarding data to entities (e.g., UE, gNB) that perform model training / model inference), etc.

[0034] In the model training phase, model training is performed based on the data forwarded from the collection phase (training data). This phase may also include data preparation (e.g., implementation of data preprocessing, cleaning, formatting, transformation, etc.), model training / validation, model testing (e.g., confirming whether the trained model meets the performance threshold), model exchange (e.g., forwarding of models for distributed learning), model deployment / update (deployment / update of models to entities that perform model inference), etc.

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

[0036] The participant stage may also include action triggering (e.g., deciding whether to trigger an action on other entities), feedback (e.g., feedback of information necessary for training data / inference data / performance feedback), etc.

[0037] In addition, for example, training of models for mobility optimization can also be performed in conservative operations management (OAM) / gNodeB (gNB) in the network (NW). In the former case, interoperability, large-capacity storage, operator manageability, model flexibility (feature engineering, etc.) are advantageous. In the latter case, latency of model updates, no need for data exchange for model deployment, etc. are advantageous. Reasoning of the above models can also be performed in gNB, for example.

[0038] Furthermore, the entity doing the training / inference can also be different depending on the use case.

[0039] For example, for AI-supported (assisted) beam management based on measurement reports, OAM / gNB can also perform model training and gNB can perform model inference.

[0040] For AI to support UE-assisted positioning, the location management function (LMF) can also perform model training, and the LMF performs model reasoning.

[0041] For CSI feedback / channel estimation using an autoencoder, OAM / gNB / UE can also perform model training, and gNB / UE (jointly) perform model inference.

[0042] For AI-supported beam management based on beam measurement or AI-supported UE optimal positioning, OAM / gNB / UE can also perform model training and UE can perform model inference.

[0043] Figure 2 This is a diagram showing an example of specifying an AI model. In this example, the UE and the NW (e.g., the base station (BS)) can identify models #1 and #2 (the details of the models may not be fully understood). Alternatively, the UE may report the performance of model #1 and the performance of model #2 to the NW, and the NW may instruct the UE on the AI ​​model to be used.

[0044] 《UE positioning using AI technology》

[0045] Fingerprinting localization, which estimates the position of a wireless device using the transmission characteristics of wireless signals, can also be widely used in both line-of-site (LOS) and non-line-of-site (NLOS) scenarios.

[0046] In the present disclosure, LOS may also mean that the UE and the base station are located in an environment where they can see each other (or there are no obstructions), and NLOS may also mean that the UE and the base station are not located in an environment where they can see each other (or there are obstructions).

[0047] In fingerprint positioning, the UE's position is estimated based on the fingerprints of multiple propagation paths (multi-pass) of the UE according to a database / AI model.

[0048] The multipath information may be, for example, information on the angle of arrival (Angle of Arrival (AoA)) / angle of departure (Angle of Departure (AoD)) of a signal in an optimal / candidate propagation path.

[0049] In addition, in the present disclosure, information related to AoA may also include, for example, information related to at least one of azimuthangles of arrival and zenith angles of arrival. In addition, information related to AoD may also include, for example, information related to at least one of radial azimuthangles of departure and radial zenith angles of departure.

[0050] In 3GPP Rel.16 NR, the following positioning technologies are supported.

[0051] ･Positioning based on DL / UL Time Difference Of Arrival (TDOA),

[0052] ･Positioning based on angle (DL AoD / UL AoA),

[0053] ･Positioning based on multiple round trip times (RTT),

[0054] ･Positioning based on Enhanced Cell ID (E-CID).

[0055] Figure 3 This is a diagram showing an example of positioning based on DL / UL TDOA. For example, assume that multiple base stations (TRP#0-#2) are configured around the UE. In this positioning method, the position of the UE is estimated (measured) using the measured value of the received reference signal time difference (RSTD). For example, the RSTD (TRP#i, TRP#j) for two specific base stations (TRP#i, TRP#j (i, j are integers)) is calculated. i -T j ) takes a certain value (k i，j ) can be connected to draw a hyperbola H i，j The intersection of multiple such hyperbolas (in this case H 0，1、 H 1，2、 H2，0 The intersection of the reference signal and the reference signal may also be used as the location of the UE. In addition, the location of the UE may also be estimated by additionally using the RSRP of the reference signal.

[0056] Figure 4 This is a diagram showing an example of positioning based on DL AoD / UL AoA. In this positioning method, the position of the UE is estimated using the measured value of DL AoD (e.g., θ or φ) or the measured value of UL AoA (e.g., θ or φ). In addition, the position of the UE can also be estimated using RSRP.

[0057] Figure 5 This is a diagram showing an example of multi-RTT based positioning. In this positioning method, the position of the UE is estimated using multiple RTTs calculated based on the Tx / Rx time difference of the reference signal (and additionally based on RSRP, RSRQ, etc.). For example, a circle based on the geometry of the RTT centered on each base station can be drawn. The intersection of these multiple circles can also be estimated as the position of the UE.

[0058] Figure 6 FIG. 1 is a diagram showing an example of E-CID-based positioning. In this positioning method, the UE position is estimated based on the geometric positions of the serving cell / neighboring cells and additional measurement results (Tx-Rx time difference, RSRP, RSRQ, etc.).

[0059] The positioning in the above-mentioned DL (DL TDOA, DL AoD) can also be implemented on the UE side or the LMF side. For example, in UE-based positioning, the UE can also calculate the UE position based on various measurement results of the UE and assistance information from the LMF. In addition, in UE assisted positioning, the UE can also report various measurement results to the LMF, and the LMF can calculate the UE's position. The assistance information can also be information used to assist the UE in estimating its position.

[0060] The positioning in the UL (UL TDOA, UL AoA) described above may also be implemented on the LMF side. In this case, the base station may report various measurement results to the LMF, and the LMF may calculate the position of the UE.

[0061] The positioning in the DL and UL (multi-RTT, E-CID) mentioned above can also be implemented on the LMF side. In this case, the UE / base station can also report various measurement results to the LMF, and the LMF calculates the position of the UE.

[0062] In addition, in 3GPP Rel.17, a positioning method using auxiliary information is being proposed for the purpose of further improving positioning accuracy. The auxiliary information can also be used as the measurement information for DL / UL-TDOA, DL-AoD / UL-AoA, multi-RTT, E-CID mentioned above, and propagated between UE, base station, and LMF.

[0063] The auxiliary information may also include information related to at least one of the following:

[0064] ･Timing Error Group (TEG)

[0065] ･RSRPP (path-specific RSRP),

[0066] ･Expected angle

[0067] ･Adjacent beam information,

[0068] ･TRP antenna / beam information,

[0069] ･LOS / NLOS indicator,

[0070] ･Added route report.

[0071] The TEG may also indicate one or more PRS (Positioning Reference Signal) resources whose Rx / Tx timing errors are within a certain tolerance range (margin).

[0072] RSRPP may also show the measurement result of RSRP in the initial path.

[0073] In UL positioning, the auxiliary information related to the expected angle may also show the expected UL-AoA / ZoA. The auxiliary information may be sent from the LMF to the base station. In addition, the auxiliary information may also support at least one of UL TDOA, UL AoA, and multi-RTT positioning.

[0074] In DL positioning, the auxiliary information related to the expected angle may also include information related to the expected DL-AoA / ZoA (expected DL-AoA / ZoA), or DL-AoD / ZoD (expected DL-AoD / ZoD). The auxiliary information may also be sent from the LMF to the UE. In addition, the auxiliary information may also support at least one of DL TDOA, DL AoA, and multi-RTT positioning. As a result, the accuracy of angle-based UE positioning is improved, and optimization of the Rx beamforming of the UE or base station can be performed.

[0075] In addition, the auxiliary information related to the expected angle may include information indicating the range (Uncertainty Range) of uncertainty of these values ​​in addition to the information of the values ​​of AoA / ZoA / AoD / ZoD themselves as described above.

[0076] As additional beam information, the adjacent beam information may also include information about a subset of DL-PRS resources for the purpose of prioritizing DL-AoD reports (option 1) or the boresight direction of each DL-PRS resource (option 2). This enables optimization of UE Rx beam scanning and DL-AoD measurements.

[0077] Furthermore, the auxiliary information may include PRS beam pattern information as additional beam information. The PRS beam pattern information may include information on relative power between DL-PRS resources having different angles for each TRP.

[0078] The LOS / NLOS indicator may also include information related to Line Of Site (LOS) / Non-Line Of Site (NLOS).

[0079] In addition, with the purpose of improving the positioning delay of the UE, pre-set measurement gaps (MG), activation of the MG via the lower layer, MG-less location, PRS Rx / Tx in the RRC_INACTIVE state, or on-demand PRS, etc. can also be set for the UE (and can also be used by the UE).

[0080] 《Beam information for UE positioning》

[0081] As mentioned above, antenna (configuration) settings / beam information is considered useful for AI / Ml models.

[0082] As scenarios where antenna (configuration) settings / beam information is utilized, consider the following scenarios A and B.

[0083] [Scenario A]

[0084] A more appropriate AI model is selected based on antenna settings / frequency / region (erea).

[0085] [Scenario B]

[0086] In order for the AI ​​model to provide better performance, metadata (antenna setting information / beam information) is required as input.

[0087] In existing specifications, auxiliary information supporting beam information of a base station (gNB) from the network (NW) to a UE is only utilized for positioning.

[0088] The following wireless communication methods are being studied for the future:

[0089] ･Use beam information for beam management.

[0090] Interfaces other than positioning protocols (for example, LTE Positioning Protocol (LPP)) also use beam information in the same way.

[0091] ･Beam information of RS other than Positioning Reference Signal (PRS) is used for positioning.

[0092] ･The beam information in the UE is used.

[0093] In Rel.17, as beam information from LMF to UE (beam information for UE-based positioning, information related to the transmission beam of the base station), beam information indicating the direction (line of sight direction) of the beam of each PRS can also be supported. The beam information can also be information indicating the line of sight direction of each PRS.

[0094] The beam information indicating the direction of the beam of each PRS is "DL-PRS-BeamInfoElement" included in "NR-DL-PRS-BeamInfo" of the common NR positioning information element.

[0095] The "DL-PRS-BeamInfoElement" includes information on the azimuth angle (azimuthangle) of the beam transmitted from the base station (TRP) and information on the elevation angle (elevation angle).

[0096] The information related to the azimuth angle is “dl-PRS-Azimuth” and “dl-PRS-Azimuth-fine.” “dl-PRS-Azimuth” is information represented by a value from 0° to 359° in units of 1°, and “dl-PRS-Azimuth-fine” is represented by a value from 0° to 0.9° in units of 0.1°.

[0097] The information related to the elevation angle is “dl-PRS-Elevation” and “dl-PRS-Elevation-fine.” “dl-PRS-Elevation” is information represented by a value from 0° to 180° in a granularity of 1°, and “dl-PRS-Elevation-fine” is information represented by a value from 0° to 0.9° in a granularity of 0.1°.

[0098] In addition, Rel.17 supports beam information representing the relative power of DL PRS at each angle (azimuth / elevation) as beam information from LMF to UE (beam information used for UE positioning, information related to the transmission beam of the base station).

[0099] This beam information indicating relative power is contained in the beam antenna information of the TRP (“NR-TRP-BeamAntennaInfo”) within the common NR positioning information element.

[0100] "NR-TRP-BeamAntennaInfo" contains information "NR-TRP-BeamAntennaInfoAzimuthElevation" related to the beam antenna information of TRP for azimuth and elevation.

[0101] “NR-TRP-BeamAntennaInfoAzimuthElevation” includes “azimuth” indicating the azimuth angle with a granularity of 1° unit, “azimuth-fine” indicating the azimuth angle with a granularity of 0.1° unit, and a list of elevation angles “elevationList”.

[0102] The elevation angle list "elevationList" includes "elevation" indicating the elevation angle with a granularity of 1° unit, "elevation-fine" indicating the elevation angle with a granularity of 0.1° unit, and a beam power list "beamPowerList".

[0103] The beam power list “beamPowerList” includes “nr-dl-prs-ResourceSetID” indicating the resource set ID of DL PRS, “nr-dl-prs-ResourceID” indicating the resource ID of DL PRS, “nr-dl-prs-RelativePower” indicating the relative power of the resource given by “nr-dl-prs-ResourceID” with a granularity of 1dB unit, and “nr-dl-prs-RelativePowerFine” indicating the relative power of the resource given by “nr-dl-prs-ResourceID” with a granularity of 0.1dB unit.

[0104] Furthermore, Rel. 17 supports information indicating an antenna reference point (ARP) as beam (antenna) information (information on a transmission beam of a base station) from an LMF to a UE.

[0105] This information is shown through the location information of TRP in the common NR positioning information element, namely "referencePoint" in "NR-TRP-LocationInfo".

[0106] The location information of TRP "NR-TRP-LocationInfo" is expressed by a reference point and a relative position to the reference point.

[0107] The location of the ARP of the PRS resource is represented by a relative position associated with the ARP location of the PRS resource set.

[0108] The antenna reference point is represented by altitude, latitude and longitude.

[0109] In addition, in Rel.17, as information (information related to the transmission beam of the base station) for LMF from a base station (e.g., gNB, NG-RAN (Next Generation-Radio Access Network) node), information related to the spatial direction of DL PRS is supported.

[0110] This information includes information indicating the boresight direction of the azimuth and elevation of the PRS resource.

[0111] In addition, the information contains the transition information from the local coordinate system (LCS) to the global coordinate system (GCS).

[0112] GCS can also be defined for a system including multiple base stations and multiple UEs. In addition, in LCS, an array antenna for one base station or one UE can also be defined.

[0113] The LCS is used as a reference for defining the vector far-field of each antenna element in the array. The vector far-field is a pattern and a polarization. The configuration of the array within the GCS can also be defined by transforming the GCS and LCS. The GCS / LCS can also be derived based on definitions and transformations that can be recognized by a person skilled in the art (specified in the specification), for example.

[0114] In addition, in Rel.17, as information (information related to the transmission beam of the base station) for LMF from a base station (e.g., gNB), information representing the beam / antenna of TRP is supported.

[0115] This information includes information indicating the relative power of the DL PRS at each angle (azimuth / elevation).

[0116] In addition, in Rel.17, as information (information related to the receiving beam of the base station) for LMF from a base station (e.g., gNB), information related to the receiving beam during UL signal measurement is supported.

[0117] The information includes at least one of a PRS resource ID, a PRS resource set ID, and an SSB index.

[0118] Furthermore, in Rel. 17, information related to spatial relationships is supported as information transmitted from the UE to the NW (information related to the transmission beam of the UE).

[0119] This information indicates an ID / index of a specific RS (eg, SSB / CSI-RS / SRS / DL PRS).

[0120] In addition, Rel. 17 specifies the number of UE receiving beams in beam scanning for positioning. The UE may also report support for UE capabilities to LMF.

[0121] For example, in FR1, the UE uses one receive beam.

[0122] Furthermore, in FR2, when the UE supports a specific UE capability, the number of beams indicated by the value indicated by information "numberOfRxBeamSweepingFactor" indicating the number of Rx beam sweeping factors for FR2 is used. Otherwise, the UE uses 8 receive beams.

[0123] In addition, information about the receive beam used by the UE in the measurement is supported (e.g., "nr-DL-PRS-RxBeamIndex").

[0124] Regarding this information, when different beams are used within the DL PRS resource set, the UE may also report the measurement values ​​received through the same reception beam.

[0125] In other words, the beam information sent by the UE is information indicating whether the same beam is used between resource sets.

[0126] Background 1

[0127] As mentioned above, in 3GPP Rel.17 NR, it is agreed that in order to improve the UE's position estimation accuracy, the UE measures / reports the RSRP of adjacent beams. For example, in the UE-assisted DL-AoD positioning method, the LMF can include at least one of the following options 1~2 in the auxiliary information for representation.

[0128] ･Option 1: Subset of PRS resources for the purpose of prioritization of DL-AoD reporting. The subset may also be set for each PRS resource based on the UE capabilities. In the case where the PRS measurement requested for the associated PRS is reported, the UE may also include the PRS measurement requested for a subset of PRS in the additional measurement of DL-AoD. The requested PRS measurement may also be DL PRS RSRP / path PRS RSRP. The UE may also report PRS measurements only for a subset of PRS resources. In addition, the subset associated with a PRS resource may also exist in the same / different PRS resource set as the PRS resource.

[0129] Option 2: Information on the boresight direction set for each PRS resource according to UE capabilities.

[0130] 《Problem 1》

[0131] However, the auxiliary information shown by the LMF is not necessarily accurate (appropriate) information. For example, the LMF selects information that is appropriate for the LMF side and displays it to the UE. That is, the auxiliary information sent by the LMF does not necessarily include information related to adjacent beams. In addition, the sending of the auxiliary information, beam information, etc. by the LMF to the UE may become a cause of communication overhead.

[0132] Background 2

[0133] In 3GPP Rel.16 NR, it is agreed that the expected RSTD and its uncertainty range are indicated to the UE from the LMF. Further, in Rel.17, it is agreed that in order to reduce the error and complexity in the measurement of AoA / AoD, the expected angle and its uncertainty range are indicated to the UE from the LMF.

[0134] Problem 2

[0135] For example, the expected angle and its uncertainty range are parameters that can affect the measurement of AoA / AoD, so it is not assumed that the expected angle and its uncertainty range are selected as input information for the AI ​​model. On the other hand, if the expected angle and its uncertainty range are used as the output of the AI ​​model, a more accurate expected time / angle can be obtained. As a result, AoA / AoD can be measured with higher accuracy.

[0136] Background 3

[0137] In 3GPP Rel.17 NR, the introduction of positioning reference unit (PRU) is being studied for positioning. PRU is a device discussed as a reference device with a known position in order to mitigate the transmission and reception timing error of UE / gNB. PRU can also be rewritten as UE / gNB / TRP (transmission reception point) / TP (transmission point).

[0138] For example, the PRU may also support at least one of the following:

[0139] Measure DL PRS and report the associated measurement values ​​(e.g. RSTD / transmit / receive time difference / RSRP) to LMF.

[0140] ･Send SRS to enable TRP to measure the measurement value associated with the reference device (e.g., Relative Time of Arrival: RTOA / Transmit / Receive Time Difference, AOA) and report it to LMF,

[0141] ･Operation, measurement, various parameters (transmission and reception timing delay, enhancement of AoD and AOA, and parameters related to correction of measured values),

[0142] ･In the case that the LMF does not have the position coordinate information, report the position coordinate information of the reference device to the LMF,

[0143] ･The reference device whose location is known is UE / gNB,

[0144] ･The accuracy of the position of the reference device can be known.

[0145] Problem 3

[0146] However, when applying AI models to positioning, offline training / online training needs to be studied as a training method for AI models. Online training can also refer to AI model training that uses real-time information for training, and offline training can also refer to AI model training that uses information collected without time constraints. According to offline training, the simplification of training can be listed as an advantage. In addition, according to online training, effects such as increased convenience of model updating through real-time training can be obtained. On the other hand, in online training, there is room for research on other implementation methods such as the problem of communication overhead. In addition, there is also room for research on which of UE / gNB / LMF is used to implement AI model training.

[0147] In view of the above-mentioned problems, the inventors of the present invention have come up with methods of AI model reasoning and AI model training related to positioning.

[0148] Various Rewrites, etc.

[0149] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. The wireless communication methods according to the embodiments may be applied individually or in combination.

[0150] In the following implementations, the angle at which the signal arrives in the UE, the AoA in the UE, and the AoA in the base station may also be overwritten. In the present disclosure, the angle at which the signal is radiated in the UE, the AoD in the UE, and the AoD in the base station may also be overwritten. In the present disclosure, AoA and AoD may also be overwritten. In the present disclosure, the UE and the base station may also be overwritten.

[0151] Furthermore, in the present disclosure, positioning may be interchangeably referred to as position determination, position estimation, position prediction, and the like.

[0152] In one embodiment of the present disclosure, a terminal (terminal, user terminal, User Equipment (UE)) / base station (Base Station (BS)) trains an ML model in a training mode and implements the ML model in an inference mode (also referred to as an inference mode, inference mode, etc.). In the inference mode, the accuracy of the ML model trained in the training mode can also be verified (validated).

[0153] In addition, in the present disclosure, AI may also be rewritten as an object (also referred to as an object, object, data, function, program, etc.) having (implementing) at least one of the following characteristics:

[0154] ･Estimates based on observed or collected information,

[0155] ･Selection based on observed or collected information,

[0156] ･Predictions based on observed or collected information.

[0157] In the present disclosure, an object may also be, for example, a device, such as a terminal or a base station, or a device, etc. In addition, in the present disclosure, an object may also be equivalent to a program / model / entity that operates in the device.

[0158] In addition, in the present disclosure, the ML model may also be rewritten as an object having (implementing) at least one of the following features:

[0159] ･Producing estimates by feeding information,

[0160] ･Predicting estimated values ​​by giving information,

[0161] ･Discovering features by giving information,

[0162] ･Selecting an action by giving information.

[0163] Furthermore, in the present disclosure, an AI model may also refer to a data-driven algorithm that applies AI technology to generate a set of outputs based on a set (set) of inputs.

[0164] In addition, in the present disclosure, AI models, models, ML models, predictive analytics, predictive analytics models, tools, autoencoders, encoders, decoders, neural network models, AI algorithms, schemes, etc. can also be rewritten one from another. In addition, the AI ​​model can also be derived using at least one of regression analysis (e.g., linear regression analysis, multiple regression analysis, logistic regression analysis), support vector machine, random forest, neural network, deep learning, etc.

[0165] In the present disclosure, the autoencoder can also be rewritten with any autoencoder such as stacked autoencoder, convolutional autoencoder, etc. The encoder / decoder of the present disclosure can also adopt models such as Residual Network (ResNet), DenseNet, and RefineNet.

[0166] In addition, in the present disclosure, encoder, encoding, encoding / encoded, correction / change / control based on encoder, compression, compressing / compressed, generating, generating / generated, etc. can also be rewritten to each other.

[0167] In addition, in the present disclosure, decoder, decoding, decoding / decoding, correction / change / control based on the decoder, decompressing, decompressing / decompressed, reconstructing, reconstructing / reconstructed, etc. can also be rewritten to each other.

[0168] In the present disclosure, the layer (for the AI ​​model) can also be rewritten with the layer (input layer, intermediate layer, etc.) used in the AI ​​model. The layer of the present disclosure can also be equivalent to at least one of the input layer, intermediate layer, output layer, batch normalization layer, convolution layer, activation layer, dense layer, normalization layer, pooling layer, attention layer, discard layer, fully connected layer, etc.

[0169] In the present disclosure, the training method of the AI ​​model may also include supervised learning, unsupervised learning, reinforcement learning, federated learning, etc. Supervised learning may also refer to the process of training a model based on input and corresponding labels. Unsupervised learning may also refer to the process of training a model without labeled data. Reinforcement learning may also refer to the process of training a model based on feedback signals (in other words, rewards) generated by input (in other words, state) and output (in other words, action) of the model in an environment where the model interacts.

[0170] In the present disclosure, generation, calculation, and export may be replaced by each other. In the present disclosure, implementation, application, operation, and execution may be replaced by each other. In the present disclosure, training, learning, updating, and retraining may be replaced by each other. In the present disclosure, reasoning, after-training, formal use, and actual use may be replaced by each other. In the present disclosure, signal may be replaced by signal / channel.

[0171] In the present disclosure, "A / B" and "at least one of A and B" may be replaced with each other. In addition, in the present disclosure, "A / B / C" may also mean "at least one of A, B, and C".

[0172] In the present disclosure, activate, deactivate, indicate (or specify), select, configure, update, determine, etc. may also be mutually rewritten. In the present disclosure, support, control, controllable, operate, and can operate, etc. may also be mutually rewritten.

[0173] In the present disclosure, Radio Resource Control (RRC), RRC parameters, RRC messages, high-layer parameters, fields, Information Element (IE), settings, etc. may also be overwritten with each other. In the present disclosure, Medium Access Control (MAC Control Element (CE)), update commands, activation / deactivation commands, etc. may also be overwritten with each other.

[0174] In the present disclosure, high-layer signaling may be, for example, Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information, positioning protocol (e.g., NR Positioning Protocol A (NRPPa)) / LTE Positioning Protocol (LTEPositioning Protocol (LPP))) messages, or any one or a combination thereof.

[0175] In the present disclosure, MAC signaling may also use, for example, MAC Control Element (MACCE), MAC Protocol Data Unit (MAC Protocol Data Unit (PDU)), etc. Broadcast information may also be, for example, Master Information Block (MIB), System Information Block (SIB), minimum system information (Remaining Minimum System Information (RMSI)), Other System Information (Other System Information (OSI)), etc. .

[0176] In the present disclosure, the physical layer signaling may also be, for example, downlink control information (Downlink Control Information (DCI)), uplink control information (Uplink Control Information (UCI)), etc.

[0177] In the present disclosure, an index, an identifier (ID), an indicator, a resource ID, etc. may also be replaced by one another. In the present disclosure, a sequence, a list, a set, a group, a group, a cluster, a subset, etc. may also be replaced by one another.

[0178] In the present disclosure, panel, UE panel, panel group, beam, beam group, precoding, uplink (UL) transmission entity, transmission / reception point (TRP), base station, spatial relation information (SRI), spatial relation, SRS resource indicator (SRI), control resource set (CORESET), physical downlink shared channel (PDSCH), codeword (CW), transport block (TB), reference signal (RS), antenna port (e.g., demodulation reference signal (DMRS)) port), antenna port group (e.g., DMRS port group), group (e.g., spatial relation group, code division multiplexing (CDM)) group, reference signal group, CORESET group, physical uplink control channel (PDSCH)), codeword (CW), transport block (TB), reference signal (RS), antenna port (e.g., demodulation reference signal (DMRS)) port), antenna port group (e.g., DMRS port group), group (e.g., spatial relation group, code division multiplexing (CDM)) group, reference signal group, CORESET group, physical uplink control channel (PDSCH)), Channel (PUCCH)) group, PUCCH resource group), resources (for example, reference signal resources, SRS resources), resource sets (for example, reference signal resource sets), CORESET pool, downlink transmission configuration indication state (Transmission Configuration Indicationstate (TCI state)) (DL TCI state), uplink TCI state (UL TCI state), unified TCI state (unified TCI state), common TCI state (common TCI state), Quasi-Co-Location (Quasi-Co-Location (QCL)), QCL assumptions, etc. can also be rewritten with each other.

[0179] In the present disclosure, timing, moment, time, time slot, sub-slot, code element, sub-frame, etc. can also be rewritten.

[0180] In the present disclosure, direction, axis, dimension, domain, polarization, polarization component, etc. may be replaced with each other.

[0181] In the present disclosure, estimation, prediction, and inference may be replaced by each other. In addition, in the present disclosure, estimation, prediction, and inference may be replaced by each other.

[0182] In the present disclosure, the autoencoder, encoder, decoder, etc. can also be rewritten as at least one of a model, an ML model, a neural network model, an AI model, an AI algorithm, etc. In addition, the autoencoder can also be rewritten with any autoencoder such as a stacked autoencoder, a convolutional autoencoder, etc. The encoder / decoder of the present disclosure can also adopt models such as a residual network (ResidualNetwork (ResNet)), a densely connected network (DenseNet), and an optimized network (RefineNet).

[0183] In the present disclosure, bits, bit sequences, bit series, series, values, information, values ​​obtained from bits, information obtained from bits, etc. may be replaced with each other.

[0184] In the present disclosure, the layers (for the encoder) may also be mutually rewritten with the layers (input layer, intermediate layer, etc.) used in the AI ​​model. The layers of the present disclosure may also be equivalent to at least one of the input layer, intermediate layer, output layer, batch normalization layer, convolution layer, activation layer, dense layer, normalization layer, pooling layer, attention layer, discard layer, fully connected layer, etc.

[0185] In the present disclosure, RSRP may be replaced with any parameter related to received power / reception quality (eg, RSRQ, SINR, CSI), etc.

[0186] (Wireless Communication Method)

[0187] <First Embodiment>

[0188] The first embodiment relates to the inference of RSRP based on an AI model. Figure 7 This is a diagram showing an example of AI model inference according to the first embodiment.

[0189] The AI ​​model can also be used to show a prioritized subset of PRS resources (also referred to as a prioritized subset, a prioritized subset (prioritized subsets)). For example, the AI ​​model can identify adjacent beams and calculate (infer) the RSRP of adjacent beams based on the location of the fingerprint and at least one of the above-mentioned auxiliary information (such as the above-mentioned beam information for UE positioning). In this case, the AI ​​model can also output the RSRP of the prioritized subset of PRS resources.

[0190] That is, the AI ​​model may also determine the RSRP of a preferred subset of PRS resources (e.g., a subset of PRS resources indicating adjacent beams). For example, the AI ​​model may also select a group of subsets of PRS resources with the best RSRP and output the RSRP of the selected subset of PRS resources.

[0191] In the present disclosure, "priority" in the prioritized subset may also mean the priority for selecting a subset of PRS resources with better RSRP. In addition, for a subset of PRS resources that is prioritized, when a report (e.g., DL-AoD report) related to at least one of the PRS resources in the subset is performed, it may also mean that a report related to other PRS resources in the subset is performed. In addition, in the present disclosure, "subset" may also be interchanged with the priority subset.

[0192] The reasoning of RSRP can also be implemented through the AI ​​model on the UE side. Not limited to this, the reasoning of RSRP can also be implemented through the AI ​​model on the gNB side / LMF side.

[0193] like Figure 7 As shown, the information input to the AI ​​model for RSRP inference may also include at least one of the following:

[0194] ･RSRP of DL-PRS,

[0195] ･TRP beam information (such as the TRP beam antenna information mentioned above),

[0196] Receive (Rx) beam information in UE,

[0197] ･Expected angle,

[0198] ･The range of expected angle uncertainty,

[0199] ･The above auxiliary information, etc.

[0200] The information output by the AI ​​model may also include at least one of the following:

[0201] ･RSRP of the prioritized subset of PRS resources (e.g., the subset of PRS resources corresponding to adjacent beams),

[0202] ･The resource index of the PRS (eg, a resource index indicating each of the subsets of PRS resources to be prioritized).

[0203] In addition, the information output by the AI ​​model can also be further used as input information of the next AI model (also called intermediate feature). As a result, the accuracy of RSRP reasoning based on the AI ​​model can be improved, and appropriate UE positioning can be achieved.

[0204] According to the first embodiment, the AI ​​model can learn the relationship / rule between the input information and the RSRP of the subset of PRS resources that are prioritized, and determine which subset of PRS resources can be applied.

[0205] [Implementation Method 1-1]

[0206] In implementation mode 1-1, the application conditions of the AI ​​model are described.

[0207] The UE may also apply the AI ​​model based on any one of the following conditions (specific conditions) from option 1-1-1 to option 1-1-2.

[0208] <Option 1-1-1: Based on instructions from NW>

[0209] For example, the UE may also apply the AI ​​model to measure (infer) the RSRP of a subset of PRS resources after receiving an indication related to the application of a model from the LMF / gNB (e.g., information related to the ON / OFF of the application of a specific model). For example, if the UE receives information indicating the application of a certain model (enabling information), it may also use the model to infer the RSRP of the subset of PRS resources that is prioritized.

[0210] <Option 1-1-2: Based on rules predefined by specifications>

[0211] The UE may also determine whether the application of the AI ​​model is required based on rules predefined by the specification. For example, the UE / gNB may also apply the AI ​​model to measure (infer) the RSRP of the subset of PRS resources after the UE / gNB receives / sends the DL-PRS, regardless of whether the information related to the subset in which the PRS resources are prioritized (e.g., DL-PRS-ResourcePrioritySubset) is indicated from the LMF. In addition, the UE / gNB may also determine whether the application of the AI ​​model is required based on the presence or absence of the information related to the above-mentioned subset (e.g., DL-PRS-ResourcePrioritySubset) indicated through the LMF. For example, more specifically, the UE / gNB may also apply the AI ​​model only when the information related to the above-mentioned subset is not indicated from the LMF.

[0212] [Implementation Method 1-2]

[0213] In implementation mode 1-2, the number of RSRPs (corresponding to the subset in which PRS resources are prioritized) output by the AI ​​model is described.

[0214] The number of RSRPs of a certain subset may also be determined based on any one of the following rules (specific rules) of options 1-2-1 to 1-2-2.

[0215] <Option 1-2-1: Based on rules predefined in the specification>

[0216] The number of RSRPs output for a subset of PRS resources may also be predefined by the specification. In addition, the number of RSRPs output for a subset of PRS resources may also be associated with the AI ​​model (e.g., the number of RSRPs output may be indicated by AI model information or may be determined by the AI ​​model based on input).

[0217] <Option 1-2-2: Based on instructions from NW>

[0218] The NW (gNB / LMF) may also indicate the number of RSRPs to the UE via signaling such as NRPPa / LPP / DCI / MAC CE / RRC. Specifically, the NW may also indicate at least one of the following information to the UE:

[0219] ･The number of subsets of PRS resources and the number of beams shown in each subset,

[0220] ･The number of RSRPs for the subset of PRS resources.

[0221] The above information indicated from the NW can also be used as input information of the AI ​​model. The information output by the AI ​​model (e.g., the indication value of the number of output RSRPs of a subset of PRS resources) can also be further used as input information of the next AI model (also referred to as an intermediate feature). The UE can also determine which AI model to use among multiple AI models based on the indication value of the number of output RSRPs of a subset of PRS resources (the value shown by the above information). That is, the number of output RSRPs of a subset of PRS resources may also be different according to the multiple AI models possessed by the UE.

[0222] [change]

[0223] The information output by the AI ​​model may include at least one of the following in addition to or in place of the above-mentioned output information (for example, the RSRP of the prioritized subset) (the RSRP of the prioritized subset in the first embodiment may also be replaced by the following and applied):

[0224] ･RSTD measurement results,

[0225] RTOA measurement results

[0226] ･ToA measurement results,

[0227] ･AoA / AoD measurement results,

[0228] ･Measurement results of the time difference between sending and receiving,

[0229] ･ DL-PRS RSRP / UL-SRS RSRP,

[0230] ･Features of multipath,

[0231] ･LOS / NLOS indicator,

[0232] ･Frequency-phase measurement results (e.g., measured phase information).

[0233] For example, by inputting different input information into the AI ​​model, different output information (features) can also be output. In addition, these measurement results can also be measurement results related to the prioritized subset.

[0234] In addition, the information input by the AI ​​model may include, in addition to or instead of the above-mentioned input information (for example, RSRP of DL-PRS), at least one of the following:

[0235] ･RSTD measurement results,

[0236] RTOA measurement results

[0237] ･ToA measurement results,

[0238] ･AoA / AoD measurement results,

[0239] ･Measurement results of the time difference between sending and receiving,

[0240] ･ DL-PRS RSRP / UL-SRS RSRP,

[0241] ･Features of multipath,

[0242] ･LOS / NLOS indicator,

[0243] ･Frequency-phase measurement results (e.g., measured phase information).

[0244] In this way, information having a different function than the output information can also be used as input information for the AI ​​model. In addition, these measurements can also be measurements related to the prioritized subset.

[0245] According to the first embodiment described above, the communication overhead of the LMF indicating auxiliary information etc. can be reduced, thereby improving the positioning accuracy.

[0246] <Second Embodiment>

[0247] The second embodiment involves reasoning about the expected angle / expected time / range of uncertainty based on the AI ​​model. Figure 8 is a diagram showing an example of AI model reasoning involved in the second embodiment. In the present disclosure, the range of uncertainty may also mean the range of uncertainty of the expected angle / expected time. In addition, in the present disclosure, "position" may also be a concept including angles (AoA / AoD between UE-gNB, etc.). That is, the above-mentioned expected angle may also be included in the location information of the terminal.

[0248] The AI ​​model can also infer the expected angle / expected time / uncertainty range. As a result, a more accurate expected angle / expected time / uncertainty range can be obtained as the output of the AI ​​model. As a result, the complex measurement and measurement errors of AoA / AoD, etc. are reduced, and higher-precision positioning can be performed.

[0249] The reasoning of the expected angle / expected time / range of uncertainty can also be implemented by the AI ​​model on the UE side. Without limitation, the reasoning of the expected angle / expected time / range of uncertainty can also be implemented by the AI ​​model on the gNB side / LMF side.

[0250] like Figure 8 As shown, the information input to the AI ​​model for reasoning about the expected angle / expected time / range of uncertainty may also include at least one of the following:

[0251] ･DL-PRS RSRP / SSB RSRP,

[0252] ･TRP beam information (such as the TRP beam antenna information mentioned above),

[0253] Information about the spatial direction of the DL-PRS in the TRP provided by the gNB (such as azimuth, elevation, etc.),

[0254] ･TRP's geographical coordinates,

[0255] ･Information related to LOS / NLOS (e.g. LOS / NLOS indicator).

[0256] The information output by the AI ​​model may also include at least one of the following:

[0257] ･Expected angle (e.g. AoA / AoD),

[0258] ･Expected time (such as RSTD),

[0259] ･The range of uncertainty in expected angle / expected time.

[0260] In addition, the input information applied in the second embodiment is not limited to this, and may also include auxiliary information reported from the UE / gNB to the LMF. In addition, information related to RSRP in the auxiliary information can also be reported by the UE.

[0261] The above input information may also be obtained from auxiliary information useful for obtaining the expected angle (AoA / AoD) / expected time (RSTD) / uncertainty range as output. In addition, preferably, the input information is not based on the expected angle / expected time / uncertainty range or other associated measurements. The AI ​​model may also output a more accurate expected angle / expected time / uncertainty range based on these input information. The UE / gNB / LMF may also implement angle (e.g., AoA / AoD) / time (RSTD) / other associated measurements (or derive measurement results) based on the output from the AI ​​model.

[0262] [Implementation Method 2-1]

[0263] In implementation mode 2-1, the application conditions of the AI ​​model are described.

[0264] The UE may also apply the AI ​​model based on any one of the following conditions (specific conditions) of options 2-1-1 to 2-1-2.

[0266] <Option 2-1-1: Based on instructions from NW>

[0267] NW (gNB / LMF) can also indicate to UE whether the application of AI model is needed through signaling such as NRPPa / LPP / DCI / MAC CE / RRC. For example, UE can also apply AI model to measure (infer) expected angle / expected time / uncertainty range after receiving instructions related to model application from LMF / gNB (for example, information related to turning on / off the application of a specific model). For example, if information indicating the application of a certain model (turn-on information) is received, UE can also use the model to infer the expected angle / expected time / uncertainty range.

[0268] <Option 2-1-2: Based on rules predefined in the specification>

[0269] The UE may also determine whether the AI ​​model needs to be applied based on a rule predefined in the specification. For example, the UE / gNB may apply the AI ​​model to measure (infer) the expected angle / expected time / uncertainty range after the UE / gNB receives / transmits the DL-PRS, regardless of whether information related to the expected angle / expected time / uncertainty range is shown from the LMF (e.g., NR-DL-PRS-ExpectedAoD-or-AoA-r17 specified in Rel.17 NR, AngleMeasurement in the UL-AoA auxiliary information (e.g., the expected azimuth / zenith AoA value included in AngleMeasurement)). In addition, the UE / gNB may also determine whether the AI ​​model needs to be applied based on the presence or absence of information related to the above-mentioned expected angle / expected time / uncertainty range shown through the LMF. More specifically, the UE / gNB may apply the AI ​​model only when the information related to the above-mentioned expected angle / expected time / uncertainty range is not shown from the LMF.

[0270] [Implementation Method 2-2]

[0271] In implementation mode 2-2, information transmission between UE / gNB and gNB / UE that apply the AI ​​model is described.

[0272] UE / gNB can also transmit information based on any of the following options 2-2-1 to 2-2-2.

[0273] <Option 2-2-1>

[0274] UE / gNB may also report associated input information to LMF (in addition, it may not be the input information itself, but may be information that can be used to derive the input information. The same applies to the following input information). LMF may also indicate the input information to other gNB / UE (gNB / UE applying the AI ​​model) through NR Positioning Protocol A (NR Positioning Protocol A (NRPPa)) / LTE Positioning Protocol (LTE Positioning Protocol (LPP)) messages.

[0275] <Option 2-2-2>

[0276] The UE / gNB in ​​the neighboring cell (which may also be rewritten as a non-serving cell) may also send input information to the UE / gNB in ​​the serving cell. The UE / gNB in ​​the serving cell may also report / indicate the input information to other gNB / UE (gNB / UE applying the AI ​​model) (in the same serving cell) using at least one of the UCI / DCI / RRC / MAC CEs.

[0277] In option 2-2-2, since inter-UE communication is assumed, the UE preferably has a capability related to a sidelink, that is, the UE can also send input information to other UEs via the sidelink.

[0278] [other]

[0279] In UL-AoA / DL-AoD based positioning (reference Figure 4 ), the output of the AI ​​model can also be used for direct positioning. In positioning based on DL-AoD, UL-AoD / UL-AoA, and positioning based on UL-TDOA (reference Figure 3 ), multi-RTT based positioning (reference Figure 5 ), the output of the AI ​​model may also be reported / indicated to the gNB / UE via at least one of the UCI / DCI / RRC / MAC CEs. The gNB / UE may also report the output information to the LMF via the NRPPa / LPP message. The LMF may also indicate the information to other UE / gNB via the LPP / NRPPa message.

[0280] According to the second embodiment described above, by applying the AI ​​model to infer the expected angle / expected time / range of uncertainty, higher-precision UE positioning can be achieved.

[0281] <Third Embodiment>

[0282] The third embodiment relates to AI model training. In addition, the AI ​​model training in the third embodiment can be equivalent to the training of the AI ​​model used for positioning, and can also be equivalent to the training of other AI models.

[0283] [Implementation Method 3-1]

[0284] In implementation 3-1, the method of collecting information used for AI model training and the model training on the UE / gNB / LMF side are described.

[0285] <Implementation 3-1-1: Labeled Data>

[0286] For online training of AI models, labeled data (labeled data) can also be obtained (collected). In addition, in the present disclosure, labeled data can also mean data to which information (location information) related to the location where the data was obtained (measured to obtain the data) is attached. Labels can also be rewritten with location information, ToA, AoA, AoD, and the probability of LOS / NLOS. As a method for collecting this information, any of the following methods 3-1-1-1 to 3-1-1-4 can also be used.

[0287] <Method 3-1-1-1: Information from PRU>

[0288] The PRU (UE / gNB) may also report associated measurement values ​​(RSTD, transmit / receive time difference, RSRP, etc.) and information related to the location as labeled data to, for example, the LMF. The LMF may also implement model training based on the labeled data. Here, as a method for the LMF to identify the PRU UE (UE equivalent to the PRU), at least one of the following methods may also be used:

[0289] UE reports PRU-related capabilities to NW (identification method 1),

[0290] ･Based on pre-defined rules (recognition method 2).

[0291] In identification method 1, the LMF can identify the PRU UE based on the reported UE capability information. In identification method 2, in order to grasp the location of the PRU UE (for example, the location of all PRU UEs) in advance, the LMF can determine which UE is the PRU UE based on the location information included in the tag data.

[0292] <Method 3-1-1-2: Information related to GNSS position reported by UE>

[0293] At least some of the multiple UEs have a Global Navigation Satellite System (GNSS) function. The UE with GNSS function can also collect its own measured GNSS position as labeled data. In addition, the UE can also report the collected labeled data to the NW (gNB / LMF). At this time, the UE can also report the measurement results measured at the same or similar time as the label information. The UE / NW can also implement model training based on the labeled data.

[0294] <Method 3-1-1-3: UE-assisted positioning / NW optimal positioning>

[0295] The UE can collect its own location information obtained through UE-assisted positioning / NW optimal positioning as labeled data. In addition, the UE can also report the collected labeled data to the NW (gNB / LMF). The UE / NW can also implement model training based on the labeled data.

[0296] <Method 3-1-1-4: Selection of labeled data based on threshold>

[0297] The UE may also collect its own location information (UE location information), ToA, AoA, AoD, and the probability of LOS / NLOS obtained by various methods, and information whose integrity / confidence level (and the sum of uncertainty) is greater than a certain threshold as labeled data. In addition, the UE may also report the collected labeled data to the NW (gNB / LMF). At this time, the UE may also report the measurement results used when calculating the label information. The UE / NW may also implement model training based on the labeled data. In addition, one of the above thresholds may also be notified from the NW (gNB / LMF). In addition, the UE may also request the NW (gNB / LMF) to provide labeled data whose integrity / confidence level (and the sum of uncertainty) is greater than a certain threshold. In this case, the UE may also report the threshold.

[0298] In addition, the tagged data can be constructed in the UE or in other UE / gNB / LMF. That is, the transmission of tagged data in Method 3-1-1-1 to Method 3-1-1-4 can also be simply rewritten as the transmission of location information. For example, when the data (e.g., measurement results) received from the UE / gNB and the location information received from the UE / gNB by other means are received within a certain time difference (e.g., X (X ≥ 0) time slots / subframes / frames / seconds / milliseconds), other UE / gNB / LMF can also generate tagged data by attaching location information to the data.

[0299] The above-mentioned methods for collecting labeled data (method 3-1-1-1 to method 3-1-1-4) can also be appropriately combined and applied. For example, when the UE is a PRU UE (equivalent to a PRU), and the reliability (confidence level) of the information associated with the UE is greater than a certain threshold, the information is collected / reported as labeled data. In addition, when the reliability (confidence level) of the UE's own location information obtained through UE-assisted positioning / NW optimal positioning is greater than a certain threshold, the information is collected / reported as labeled data.

[0300] Below, in implementation modes 3-1-2 to 3-1-4, the AI ​​model training on the UE / gNB / LMF side is described. Fig. 9 This is a diagram showing an example of AI model training involved in the third embodiment.

[0301] <Implementation 3-1-2: AI model training on the UE side>

[0302] The UE performing AI model training can also collect training data sets from other UEs. In this case, communication between UEs needs to be considered. As a communication method between UEs, at least one of the following can also be used:

[0303] ･Communication method between UEs with side link function,

[0304] ･A method in which a data set is sent from a UE to an LMF via a message such as LPP, and the data set is indicated to other UEs (UEs performing AI model training) via a message such as LPP,

[0305] ･Depends on the installation method of UE.

[0306] The UE performing AI model training can also collect training data sets from the gNB. In this case, the communication between gNB / UE needs to be considered. For example, the gNB can also send the training data set to the LMF via messages such as NRPPa. In addition, the LMF can also indicate the data set to the UE (UE performing AI model training) via messages such as LPP.

[0307] UE can also implement AI model training based on the following conditions (specific conditions):

[0308] ･When there is an instruction from NW (LMF / gNB),

[0309] ･The situation where UE capabilities are reported / reported from other UEs.

[0310] In addition, when the UE has capabilities related to AI model training, the UE can also report the above-mentioned labeled data to the UE performing training non-periodically / semi-continuously / periodically.

[0311] <Implementation 3-1-3: AI model training on the gNB side>

[0312] The gNB performing AI model training can also collect training data sets from other gNBs. In this case, communication between UEs needs to be considered. As a communication method between gNBs, at least one of the following can also be used:

[0313] ･Communication methods using inter-gNB interfaces (e.g., Xn interfaces for Xn signaling, etc.),

[0314] ･A method in which a data set is sent from the gNB to the LMF via a message such as NRPPa, and the data set is indicated to other gNBs (gNBs performing AI model training) via a message such as NRPPa.

[0315] The gNB can also implement AI model training based on the following conditions (specific conditions):

[0316] ･When there is an instruction from NW (LMF),

[0317] ･When gNB and NW (LMF) obtain training data sets (that meet the necessary conditions for training),

[0318] ･Depending on the installation of gNE.

[0319] <Implementation 3-1-4: AI model training on the LMF side>

[0320] When AI model training is performed on the LMF side, various measurement information of UE / TRP / gNB can also be sent to LMF through LPP / NRPPa messages.

[0321] LMF can also perform AI model training when (conditionally) receiving a training dataset (that meets the necessary / conditions for implementing training).

[0322] [Implementation Method 3-2]

[0323] In implementation 3-2, the collection and sending of information used for AI model training is described.

[0324] In the case of studying the online training of the AI ​​model as described above, the reduction of the overhead in order to achieve real-time model updates is a subject. On the other hand, in a UE with a static motion trajectory and a static speed, it is assumed that the sudden position change (movement amount) is small. Therefore, during the period when the UE's movement amount is assumed to be relatively small, it can be said that there is no need to frequently locate the UE and report the positioning results. Therefore, the inventors of the present invention conceived that in a UE with such a static motion trajectory and a static speed, the UE reports information related to its own position and movement (activity (motion) auxiliary information described later) to reduce overhead.

[0325] The UE may also report activity assistance information to the NW (gNB / LMF). Here, the activity assistance information is information related to the UE's actions, and may also include information related to the UE's initial position, moving speed, moving trajectory, etc. By using activity assistance information, the UE can be positioned infrequently and the future position of the UE can be appropriately predicted. As a result, the overhead of sending training data sets can be reduced. In the present disclosure, the activity assistance information may also be rewritten as information related to the UE's behavior.

[0326] The reporting of the initial position of the UE (eg, GNSS position) can adopt (reuse) the same method as in the above-mentioned embodiment 3-1.

[0327] The reported activity assistance information may also be a value obtained / measured over a duration of time, and the value may also include at least one of the following:

[0328] ･UE speed,

[0329] ･Speed ​​drift rate,

[0330] UE speed drift rate variation

[0331] ･Motion trail (also called movement trail).

[0332] The duration of time during which the UE obtains / measures the above value may also be at least one of the following:

[0333] ･The period from the past to the present,

[0334] ･The period from the past to the future,

[0335] ･The period from now to the future.

[0336] The type of the above-mentioned value reported by the UE may also be a pre-defined and fixed value. In addition, the type of the above-mentioned value reported by the UE may also be indicated by the NW. For example, the NW may also request the UE for information related to the UE's actions during a certain period (activity assistance information). In addition, the NW may also request the UE for the UE speed and speed deviation rate. When receiving a request from the NW, the UE may also report the activity assistance information indicated by the request to the NW.

[0337] The above-mentioned value (activity assistance information) reported by the UE may also be defined with a validity duration. For example, the start time of the validity duration may also be defined based on any of the following:

[0338] ･ From the time when the UE is explicitly reported (e.g., the system frame number (SFN) / subframe number / time slot number reported together with the auxiliary information),

[0339] ･A time that is defined and fixed in advance.

[0340] In addition, predefined, fixed moments can also be implicitly defined by any of the following:

[0341] ･The time when the above value is reported (the time when the report is sent by the UE),

[0342] ･X time slots / milliseconds / seconds / minutes after the above value is reported (after the report is sent by the UE),

[0343] ･The time when NW receives active assistance information associated with the GNSS position,

[0344] ･It is used to calculate the reception time of the associated PRS.

[0345] The length of the valid period of the above-mentioned value reported by the UE may be explicitly reported by the UE or may be defined and fixed in advance.

[0346] In the AI ​​model training on the gNB side, the UE can also send / report active assistance information to the serving gNB through UCI / MAC CE / RRC. In addition, in the AI ​​model training on the gNB side, the LMF can also send active assistance information through LPP and indicate the active assistance information to the gNB (gNB performing AI model training) through NRPPa.

[0347] During the AI ​​model training on the LMF side, the UE can also send activity assistance information to the LMF through LPP.

[0348] In the AI ​​model training on the UE side, the UE may also send activity auxiliary information through the side link. In addition, in the AI ​​model training on the UE side, the UE may also send activity auxiliary information to the LMF through LPP. The LMF may also indicate the activity auxiliary information to the UE (UE performing AI model training) through NRPPa.

[0349] The UE may also report the activity assistance information at any of the following timings:

[0350] ･After initial access,

[0351] ･Same timing as the initial position report,

[0352] ･Set a regular timer, the timer expires at a certain time,

[0353] ･The timing when the activity support information is changed from the last report (for example, the timing when specific information included in the activity support information is changed to a value above a threshold from the last report),

[0354] ･The timing of the triggering / indication signal is specified / triggered by NW settings,

[0355] ･N symbols / slots / milliseconds / seconds before the validity period expires (N is an integer greater than 0).

[0356] In addition, the new position of the UE can also be predicted in the gNB / LMF based on a combination of the UE's initial position and active assistance information with timestamps measured by the UE / TRP (an AI model (e.g., an AI model used for AI model training) can also be used for this prediction).

[0357] According to the third embodiment described above, AI model training can be appropriately implemented. In addition, the overhead when sending a data set for training can be reduced.

[0358] <Supplement>

[0359] [Notification of information to UE]

[0360] In the above-mentioned embodiment, notification of arbitrary information from the network (Network (NW)) (for example, from a base station (Base Station (BS))) to the UE (in other words, reception of arbitrary information from the BS in the UE) can also be performed using physical layer signaling (for example, DCI), high-layer signaling (for example, RRC signaling, MAC CE), specific signals / channels (for example, PDCCH, PDSCH, reference signals), or a combination thereof.

[0361] When the above notification is performed through MAC CE, the MAC CE may be identified by including a new logical channel ID (Logical Channel ID (LCID)) not specified in the existing specifications in the MAC subheader.

[0362] In the case where the above notification is performed through DCI, the above notification may be performed through a specific field of the DCI, a Radio Network Temporary Identifier (RNTI) used for scrambling of cyclic redundancy check (CRC) bits assigned to the DCI, the format of the DCI, and the like.

[0363] In addition, notification of any information in the above-mentioned embodiments to the UE may be performed periodically, semi-continuously, or aperiodically.

[0364] [Notification of information from UE]

[0365] The notification of arbitrary information from the UE (to the NW) in the above-mentioned embodiments (in other words, the sending / reporting of arbitrary information to the BS in the UE) may also be performed using physical layer signaling (e.g., UCI), high-layer signaling (e.g., RRC signaling, MAC CE), specific signals / channels (e.g., PUCCH, PUSCH, PRACH, reference signals), or a combination thereof.

[0366] In the case where the above notification is performed through MAC CE, the MAC CE can also be identified by including a new LCID that is not specified in the existing specifications in the MAC subheader.

[0367] When the notification is performed through UCI, the notification may be transmitted using PUCCH or PUSCH.

[0368] In addition, the notification of arbitrary information from the UE in the above-mentioned embodiments may be performed periodically, semi-continuously, or aperiodically.

[0369] [AI model information]

[0370] In the present disclosure, AI model information may also mean information including at least one of the following:

[0371] ･AI model input / output information,

[0372] ･Information for pre-processing / post-processing of AI model input / output,

[0373] ･Information on AI model parameters,

[0374] ･Training information for AI models (training information),

[0375] ･Inference information for AI models,

[0376] ･Performance information related to AI models.

[0377] Here, the input / output information of the above AI model may also include information related to at least one of the following:

[0378] Contents of input / output data (e.g., RSRP, SINR, amplitude / phase information in the channel matrix (or precoding matrix), information related to the angle of arrival (AoA), information related to the angle of departure (AoD), location information),

[0379] ･Auxiliary information of the data (also called meta information),

[0380] ･The type of input / output data (e.g., immutable value, floating point number),

[0381] ･The bit width of input / output data (for example, 64 bits for each input value),

[0382] ･Quantization interval (quantization step size) of input / output data (for example, 1dBm for L1-RSRP),

[0383] ･The range that input / output data can take (for example, [0, 1]).

[0384] In addition, in the present disclosure, information related to AoA may also include information related to at least one of an azimuth angle of arrival and a zenith angle of arrival (ZoA). In addition, information related to AoD may also include information related to at least one of an azimuth angle of departure and a zenith angle of depature (ZoD), for example.

[0385] In the present disclosure, location information may also be location information related to UE / NW. Location information may also include information (e.g., latitude, accuracy, altitude) obtained using a positioning system (e.g., satellite positioning system (Global Navigation Satellite System (GNSS)), Global Positioning System (GPS), etc.)), information of a BS adjacent to (or in service of) the UE (e.g., BS / cell identifier (ID), BS-UE example, direction / angle of the BS (UE) seen from the UE (BS), coordinates of the BS (UE) seen from the UE (BS) (e.g., X / Y / Z axis coordinates), etc.), a specific address of the UE (e.g., Internet Protocol (IP) address), etc. At least one of the above. The location information of the UE is not limited to information based on the location of the BS, but may also be information based on a specific point.

[0386] The location information may also include information related to the installation itself (for example, the location / position / orientation of the antenna, the location / orientation of the antenna panel, the number of antennas, the number of antenna panels, etc.).

[0387] The location information may also include mobility information. The mobility information may also include at least one of information indicating the mobility type, the moving speed of the UE, the acceleration of the UE, the moving direction of the UE, and the like.

[0388] Here, the mobility type may also be equivalent to at least one of a fixed location UE, a movable / moving UE, a no mobility UE, a low mobility UE, a middle mobility UE, a high mobility UE, a cell-edge UE, a non-cell-edge UE, etc.

[0389] In the present disclosure, environmental information (used for data) may also be information related to the environment in which the data is obtained / used, for example, it may also be equivalent to information representing frequency information (frequency band ID, etc.), environment type information (at least one of indoor, outdoor, Urban Macro (UMa), Urban Micro (Umi), etc.), line of sight (LOS) / non-line of sight (NLOS) information, etc.

[0390] Here, LOS may also mean that the UE and the BS are in an environment where they can see each other (or there are no obstructions), and NLOS may also mean that the UE and the BS are not in an environment where they can see each other (or there are obstructions). The information indicating LOS / NLOS may show either a soft value (e.g., the probability of LOS / NLOS) or a hard value (e.g., either of LOS / NLOS).

[0391] In the present disclosure, meta information may also mean, for example, information related to input / output information suitable for an AI model, information related to data obtained / available, etc. Meta information may specifically include information related to beams of RS (e.g., CSI-RS / SRS / SSB, etc.) (e.g., the angle at which each beam points, 3dB beam width, the shape of the pointed beam, the number of beams), antenna layout information of gNB / UE, frequency information, environmental information, meta information ID, etc. In addition, meta information may also be used as input / output of an AI model.

[0392] [About application of each embodiment]

[0393] At least one of the above-mentioned embodiments may also be applied when a specific condition is met. The specific condition may be specified in the specification or notified to the UE / BS using high-layer signaling / physical layer signaling.

[0394] At least one of the above-mentioned embodiments may also be applied only to a UE that reports a specific UE capability or supports the specific UE capability.

[0395] The specific UE capability may also represent at least one of the following:

[0396] ･Supporting specific processing / actions / controls / information for at least one of the above embodiments,

[0397] ･Supports AI models for outputting a subset of adjacent beams (or RSRP related to adjacent beams),

[0398] ･Supports AI models for outputting expected angle / expected time / uncertainty range,

[0399] ･UE supports AI model training (in UE / gNB / LMF),

[0400] ･UE supports side link,

[0401] ･UE supports the collection of location information based on positioning,

[0402] ･UE supports prompts from NW's AI models,

[0403] ･In RRC_INACTIVE state (this UE capability) can be applied.

[0404] In addition, the above-mentioned specific UE capabilities can be capabilities that are applied across all frequencies (commonly regardless of frequency), or capabilities for each frequency (for example, one or a combination of cells, frequency bands, frequency band combinations, BWPs, component carriers, etc.), or capabilities for each frequency range (for example, frequency range 1 (Frequency Range 1 (FR1)), FR2, FR3, FR4, FR5, FR2-1, FR2-2), or capabilities for each subcarrier spacing (SubCarrier Spacing (SCS)), or capabilities for each feature set (Feature Set (FS)) or feature set per component carrier (Feature Set Per Component-carrier (FSPC)).

[0405] Furthermore, the specific UE capability may be a capability applied across all full-duplex modes (commonly regardless of the duplex mode) or a capability for each duplex mode (eg, time division duplex (TDD) and frequency division duplex (FDD)).

[0406] In addition, at least one of the above-mentioned embodiments may also be applied when the UE is set / activated / triggered with specific information associated with the above-mentioned embodiments (or the action of implementing the above-mentioned embodiments) through high-layer signaling / physical layer signaling. For example, the specific information may also be information indicating the application of the activated AI model, any RRC parameter for a specific version (e.g., Rel.18 / 19), etc.

[0407] When the UE does not support at least one of the above-mentioned specific UE capabilities or the above-mentioned specific information is not configured, for example, the actions of Rel.15 / 16 may be applied.

[0408] (Note)

[0409] The following inventions are added to one embodiment of the present disclosure.

[0410] <First Embodiment / Second Embodiment>

[0411] [Note 1]

[0412] A terminal having:

[0413] a control unit that inputs information related to the position determination reference signal into an artificial intelligence (AI) model to obtain an output related to the position determination of the terminal; and

[0414] The sending unit sends information related to the location of the terminal based on the output.

[0415] [Note 2]

[0416] A terminal as described in Appendix 1, wherein:

[0417] The control unit inputs information related to the received power of the position determination reference signal into the AI ​​model, and obtains information related to the received power of a priority subset of position determination reference signal resources as the output.

[0418] [Note 3]

[0419] A terminal as described in Supplement 1 or Supplement 2, wherein:

[0420] The control unit inputs information related to the received power of the position determination reference signal into the AI ​​model, and obtains at least one of the expected angle, expected time, and uncertainty range of the signal in the terminal as the output.

[0421] [Note 4]

[0422] A terminal as described in any one of Notes 1 to 3, wherein:

[0423] The control unit determines whether the application of the AI ​​model is required based on specific conditions.

[0424] (Note)

[0425] The following inventions are added to one embodiment of the present disclosure.

[0426] <Third Embodiment>

[0427] [Note 1]

[0428] A terminal having:

[0429] A control unit, which obtains labeled data including information related to the location of the terminal for training an artificial intelligence (AI) model; and

[0430] A sending unit sends the labeled data.

[0431] [Note 2]

[0432] A terminal as described in Appendix 1, wherein:

[0433] The sending unit sends, as the labeled data, information of a plurality of information related to the location of the terminal, for which a value associated with at least one of integrity, reliability, and uncertainty of the information is greater than a certain threshold.

[0434] [Note 3]

[0435] A terminal as described in Supplement 1 or Supplement 2, wherein:

[0436] The transmitting unit transmits information related to the behavior of the terminal in a certain period.

[0437] [Note 4]

[0438] A terminal as described in any one of Notes 1 to 3, wherein:

[0439] The information on the behavior of the terminal includes information on a validity period of the information.

[0440] (Wireless Communication System)

[0441] Hereinafter, a configuration of a wireless communication system according to an embodiment of the present disclosure will be described. In the wireless communication system, communication is performed using any one of the wireless communication methods according to the above-mentioned embodiments of the present disclosure or a combination thereof.

[0442] Fig.10 1 is a diagram showing an example of a schematic structure of a wireless communication system according to an embodiment. The wireless communication system 1 (may also be referred to as simply the system 1) may be a system that implements communication using Long Term Evolution (LTE) standardized by the Third Generation Partnership Project (3GPP), the fifth generation mobile communication system New Radio (5GNR), or the like.

[0443] In addition, the wireless communication system 1 may also support dual connectivity (Multi-RAT Dual Connectivity (MR-DC)) between multiple radio access technologies (Radio Access Technology (RAT)). MR-DC may also include dual connectivity between LTE (Evolved Universal Terrestrial Radio Access (E-UTRA)) and NR (E-UTRA-NR Dual Connectivity (E-UTRA-NR Dual Connectivity (EN-DC))), dual connectivity between NR and LTE (NR-E-UTRA Dual Connectivity (NR-E-UTRA Dual Connectivity (NE-DC))), etc.

[0444] In EN-DC, the base station (eNB) of LTE (E-UTRA) is the master node (Master Node (MN)), and the base station (gNB) of NR is the secondary node (Secondary Node (SN)). In NE-DC, the base station (gNB) of NR is the MN, and the base station (eNB) of LTE (E-UTRA) is the SN.

[0445] The wireless communication system 1 may also support dual connectivity between multiple base stations within the same RAT (for example, dual connectivity (NR-NR Dual Connectivity (NN-DC)) where both MN and SN are NR base stations (gNB)).

[0446] The wireless communication system 1 may also include a base station 11 that forms a macro cell C1 with a relatively wide coverage, and a base station 12 (12a-12c) that is configured in the macro cell C1 and forms a small cell C2 that is narrower than the macro cell C1. The user terminal 20 may also be located in at least one cell. The configuration and number of each cell and user terminal 20 are not limited to the method shown in the figure. Hereinafter, when the base stations 11 and 12 are not distinguished, they are collectively referred to as base stations 10.

[0447] 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).

[0448] Each CC may also be included in at least one of the first frequency band (Frequency Range 1 (FR1)) and the second frequency band (Frequency Range 2 (FR2)). The macro cell C1 may also be included in FR1, and the small cell C2 may also be included in FR2. For example, FR1 may be a frequency band below 6 GHz (below 6 GHz (sub-6 GHz)), and FR2 may be a frequency band higher than 24 GHz (above-24 GHz). In addition, the frequency bands and definitions of FR1 and FR2 are not limited to these. For example, FR1 may also be equivalent to a frequency band higher than FR2.

[0449] Furthermore, the user terminal 20 may perform communication using at least one of time division duplex (TDD) and frequency division duplex (FDD) in each CC.

[0450] Multiple base stations 10 may also be connected by wire (e.g., optical fiber based on Common Public Radio Interface (CPRI), X2 interface, etc.) or wireless (e.g., NR communication). For example, when NR communication between base stations 11 and 12 is used as a backhaul, the base station 11 equivalent to the upper station may also be referred to as an Integrated Access Backhaul (IAB) donor, and the base station 12 equivalent to a relay station (relay) may also be referred to as an IAB node.

[0451] The base station 10 may be connected to the core network 30 via other base stations 10 or directly. The core network 30 may include, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), and a Next Generation Core (NGC).

[0452] The core network 30 may also include network functions (NF), such as User Plane Function (UPF), Access and Mobility Management Function (AMF), Session Management Function (SMF), Unified Data Management (UDM), Application Function (AF), Data Network (DN), Location Management Function (LMF), Operation, Administration and Maintenance (Management) (OAM), etc. In addition, multiple functions may be provided through one network node. In addition, communication with an external network (e.g., network) may also be performed via DN.

[0453] The user terminal 20 may also be a terminal that supports at least one of communication modes such as LTE, LTE-A, and 5G.

[0454] In the wireless communication system 1, a wireless access method based on orthogonal frequency division multiplexing (OFDM) may be used. For example, in at least one of the downlink (DL) and the uplink (UL), cyclic prefix OFDM (CP-OFDM), discrete Fourier transform spread OFDM (DFT-s-OFDM), orthogonal frequency division multiple access (OFDMA), single carrier frequency division multiple access (SC-FDMA), etc. may be used.

[0455] The radio access scheme may also be referred to as a waveform. In addition, in the wireless communication system 1, other radio access schemes (for example, other single-carrier transmission schemes, other multi-carrier transmission schemes) may be used as the radio access schemes for UL and DL.

[0456] As downlink channels, the wireless communication system 1 may use a downlink shared channel (Physical Downlink Shared Channel (PDSCH)) shared by each user terminal 20 , a broadcast channel (Physical Broadcast Channel (PBCH))), a downlink control channel (Physical Downlink Control Channel (PDCCH)), etc.

[0457] In addition, as uplink channels, an uplink shared channel (Physical Uplink Shared Channel (PUSCH)) shared by each user terminal 20, an uplink control channel (Physical Uplink Control Channel (PUCCH)), a random access channel (Physical Random Access Channel (PRACH)), etc. can also be used in the wireless communication system 1.

[0458] User data, high-layer control information, System Information Block (SIB), etc. are transmitted through PDSCH. User data, high-layer control information, etc. can also be transmitted through PUSCH. In addition, Master Information Block (MIB) can also be transmitted through PBCH.

[0459] The lower layer control information may also be transmitted via the PDCCH. The lower layer control information may include, for example, downlink control information (Downlink Control Information (DCI)) including scheduling information of at least one of the PDSCH and the PUSCH.

[0460] In addition, the DCI for scheduling the PDSCH may also be referred to as DL allocation, DL DCI, etc., and the DCI for scheduling the PUSCH may also be referred to as UL grant, UL DCI, etc. In addition, the PDSCH may also be rewritten as DL data, and the PUSCH may also be rewritten as UL data.

[0461] In the detection of PDCCH, the control resource set (CORESET) and the search space can also be used. CORESET corresponds to the resources for searching DCI. The search space corresponds to the search area and search method of PDCCH candidates. A CORESET can also be associated with one or more search spaces. The UE can also monitor the CORESET associated with a search space based on the search space setting.

[0462] A search space may also correspond to a PDCCH candidate corresponding to one or more aggregation levels. One or more search spaces may also be referred to as a search space set. In addition, the "search space", "search space set", "search space setting", "search space set setting", "CORESET", "CORESET setting" and the like in the present disclosure may also be rewritten mutually.

[0463] Uplink control information (uplink control information (UCI)) including at least one of channel state information (CSI), delivery confirmation information (e.g., also known as hybrid automatic repeat request confirmation (HARQ-ACK), ACK / NACK, etc.), and scheduling request (SR) can also be transmitted through PUCCH. The random access preamble used to establish a connection with a cell can also be transmitted through PRACH.

[0464] In the present disclosure, downlink, uplink, etc. may be expressed without “link.” In addition, various channels may be expressed without “Physical” at the beginning.

[0465] In the wireless communication system 1, a synchronization signal (Synchronization Signal (SS)), a downlink reference signal (Downlink Reference Signal (DL-RS)), etc. can also be transmitted. As DL-RS, in the wireless communication system 1, a cell-specific reference signal (Cell-specific Reference Signal (CRS)), a channel state information reference signal (Channel State Information Reference Signal (CSI-RS)), a demodulation reference signal (DeModulation Reference Signal (DMRS)), a positioning reference signal (Positioning Reference Signal (PRS)), a phase tracking reference signal (Phase Tracking Reference Signal (PTRS)), etc. can also be transmitted.

[0466] The synchronization signal may be, for example, at least one of a primary synchronization signal (PSS) and a secondary synchronization signal (SSS). A signal block including SS (PSS, SSS) and PBCH (and DMRS for PBCH) may also be referred to as SS / PBCH block, SS block (SS Block (SSB)), etc. In addition, SS, SSB, etc. may also be referred to as reference signals.

[0467] In addition, in the wireless communication system 1, as an uplink reference signal (Uplink Reference Signal (UL-RS)), a measurement reference signal (Sounding Reference Signal (SRS)), a demodulation reference signal (DMRS), etc. may also be transmitted. In addition, DMRS may also be called a user terminal specific reference signal (UE-specific Reference Signal).

[0468] (Base Station)

[0469] Fig.111 is a diagram showing an example of a structure of a base station involved in one embodiment. The base station 10 includes a control unit 110, a transmitting and receiving unit 120, a transmitting and receiving antenna 130, and a transmission path interface (transmission line interface) 140. In addition, the control unit 110, the transmitting and receiving unit 120, the transmitting and receiving antenna 130, and the transmission path interface 140 may each be provided with more than one.

[0470] In addition, in this example, the functional blocks of the characteristic parts in this embodiment are mainly shown, and it is also assumed that the base station 10 also has other functional blocks required for wireless communication. Part of the processing of each unit described below may also be omitted.

[0471] The control unit 110 controls the entire base station 10. The control unit 110 can be composed of a controller, a control circuit, and the like that are described based on common knowledge in the technical field to which the present disclosure relates.

[0472] The control unit 110 may also control signal generation, scheduling (e.g., resource allocation, mapping), etc. The control unit 110 may also control transmission and reception, measurement, etc. using the transmission and reception unit 120, the transmission and reception antenna 130, and the transmission path interface 140. The control unit 110 may also generate data, control information, sequences, etc. to be sent as signals, and forward them to the transmission and reception unit 120. The control unit 110 may also perform call processing (setting, release, etc.) of communication channels, state management of the base station 10, management of wireless resources, etc.

[0473] The transmitting and receiving unit 120 may also include a baseband unit 121, a radio frequency (RF) unit 122, and a measuring unit 123. The baseband unit 121 may also include a transmitting processing unit 1211 and a receiving processing unit 1212. The transmitting and receiving unit 120 may be composed of a transmitter / receiver, an RF circuit, a baseband circuit, a filter, a phase shifter (phase shifter), a measuring circuit, a transmitting and receiving circuit, etc., which are described based on the common knowledge in the technical field involved in the present disclosure.

[0474] The transmitting and receiving unit 120 may be configured as an integrated transmitting and receiving unit, or may be configured as a transmitting unit and a receiving unit. The transmitting unit may also be configured as a transmitting processing unit 1211 and an RF unit 122. The receiving unit may also be configured as a receiving processing unit 1212, an RF unit 122, and a measuring unit 123.

[0475] The transmitting / receiving antenna 130 can be constituted by an antenna described based on common knowledge in the technical field involved in the present disclosure, such as an array antenna.

[0476] The transmitting and receiving unit 120 may also transmit the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc. The transmitting and receiving unit 120 may also receive the above-mentioned uplink channel, uplink reference signal, etc.

[0477] The transmitting and receiving unit 120 may also use digital beamforming (eg, precoding), analog beamforming (eg, phase rotation), etc. to form at least one of a transmitting beam and a receiving beam.

[0478] The sending and receiving unit 120 (sending processing unit 1211), for example, may also perform Packet Data Convergence Protocol (PDCP) layer processing, Radio Link Control (RLC) layer processing (e.g., RLC retransmission control), Medium Access Control (MAC) layer processing (e.g., HARQ retransmission control), etc. on the data, control information, etc. obtained from the control unit 110, to generate a bit string to be sent.

[0479] The transmitting and receiving unit 120 (transmitting processing unit 1211) may also perform channel coding (which may also include error correction coding), modulation, mapping, filter processing (filtering processing), discrete Fourier transform (DFT) processing (as needed), inverse fast Fourier transform (IFFT) processing), precoding, digital-to-analog conversion and other transmission processing on the bit string to be transmitted, and output a baseband signal.

[0480] The transmitting and receiving unit 120 (RF unit 122 ) may also perform modulation, filter processing, amplification, etc. on the baseband signal to a radio frequency band, and transmit the signal in the radio frequency band via the transmitting and receiving antenna 130 .

[0481] On the other hand, the transmission and reception unit 120 (RF unit 122 ) may also perform amplification, filter processing, demodulation into a baseband signal, etc. on the signal in the radio frequency band received by the transmission and reception antenna 130 .

[0482] The transmitting and receiving unit 120 (receiving processing unit 1212) may also apply receiving processing such as analog-to-digital conversion, Fast Fourier Transform (FFT) processing, Inverse Discrete Fourier Transform (IDFT) processing (as needed), filter processing, demapping, demodulation, decoding (which may also include error correction decoding), MAC layer processing, RLC layer processing, and PDCP layer processing to the acquired baseband signal to obtain user data, etc.

[0483] The transmitting and receiving unit 120 (the measuring unit 123) may also implement measurements related to the received signal. For example, the measuring unit 123 may also perform radio resource management (RRM) measurements, channel state information (CSI) measurements, etc. based on the received signal. The measuring unit 123 may also measure received power (e.g., reference signal received power (RSRP)), received quality (e.g., reference signal received quality (RSRQ), signal to interference plus noise ratio (SINR), signal to noise ratio (SNR)), signal strength (e.g., received signal strength indicator (RSSI)), propagation path information (e.g., CSI), etc. The measurement results may also be output to the control unit 110.

[0484] The transmission path interface 140 can also send and receive signals (return signaling) between devices included in the core network 30, other base stations 10, etc., and can also obtain and transmit user data (user plane data) and control plane data for the user terminal 20.

[0485] In addition, the transmission unit and the reception unit of the base station 10 in the present disclosure may also be constituted by at least one of the transmission and reception unit 120 , the transmission and reception antenna 130 , and the transmission path interface 140 .

[0486] In addition, the sending and receiving unit 120 may also send information related to the location of the terminal based on the output of the AI ​​model.

[0487] In addition, the transmitting and receiving unit 120 may also receive labeled data including information related to the location of the terminal. The transmitting and receiving unit 120 may also transmit the labeled data collected for training. The transmitting and receiving unit 120 may also receive, as the labeled data, information of a value associated with at least one of integrity, reliability, and uncertainty of the information among a plurality of information related to the location of the terminal greater than a certain threshold. The transmitting and receiving unit 120 may also receive information related to the action of the terminal during a certain period.

[0488] The control unit 110 may also input information related to the location determination reference signal into an artificial intelligence (AI) model to obtain an output related to the location determination of the terminal. The control unit 110 may also input information related to the received power of the location determination reference signal into the AI ​​model, and obtain as the output information related to the received power of a priority subset of location determination reference signal resources. The control unit 110 may also input information related to the received power of the location determination reference signal into the AI ​​model, and obtain as the output at least one of the expected angle, expected time, and uncertainty range of the signal in the terminal. The control unit 110 may also determine whether the application of the AI ​​model is needed based on specific conditions.

[0489] The control unit 110 may also obtain labeled data including information related to the location of the terminal for training an artificial intelligence (AI) model.

[0490] (User terminal)

[0491] Fig.12 2 is a diagram showing an example of a configuration of a user terminal according to an embodiment. The user terminal 20 includes a control unit 210, a transmitting / receiving unit 220, and a transmitting / receiving antenna 230. In addition, the control unit 210, the transmitting / receiving unit 220, and the transmitting / receiving antenna 230 may each be provided with one or more.

[0492] In addition, in this example, the functional blocks of the characteristic parts in this embodiment are mainly shown, and it is also conceivable that the user terminal 20 also has other functional blocks required for wireless communication. Part of the processing of each unit described below may be omitted.

[0493] The control unit 210 controls the entire user terminal 20. The control unit 210 can be composed of a controller, a control circuit, and the like that are described based on common knowledge in the technical field to which the present disclosure relates.

[0494] The control unit 210 may also control signal generation, mapping, etc. The control unit 210 may also control transmission and reception, measurement, etc. using the transmission and reception unit 220 and the transmission and reception antenna 230. The control unit 210 may also generate data, control information, sequences, etc. to be transmitted as signals, and forward them to the transmission and reception unit 220.

[0495] The transmitting and receiving unit 220 may also include a baseband unit 221, an RF unit 222, and a measuring unit 223. The baseband unit 221 may also include a transmitting processing unit 2211 and a receiving processing unit 2212. The transmitting and receiving unit 220 may be composed of a transmitter / receiver, an RF circuit, a baseband circuit, a filter, a phase shifter, a measuring circuit, a transmitting and receiving circuit, etc., which are described based on the common knowledge in the technical field involved in the present disclosure.

[0496] The transmitting and receiving unit 220 may be configured as an integrated transmitting and receiving unit, or may be configured as a transmitting unit and a receiving unit. The transmitting unit may also be configured as a transmitting processing unit 2211 and an RF unit 222. The receiving unit may also be configured as a receiving processing unit 2212, an RF unit 222, and a measuring unit 223.

[0497] The transmitting / receiving antenna 230 can be constituted by an antenna described based on common knowledge in the technical field involved in the present disclosure, such as an array antenna.

[0498] The transmitting and receiving unit 220 may also receive the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc. The transmitting and receiving unit 220 may also transmit the above-mentioned uplink channel, uplink reference signal, etc.

[0499] The transmitting and receiving unit 220 may also use digital beamforming (eg, precoding), analog beamforming (eg, phase rotation), etc. to form at least one of a transmitting beam and a receiving beam.

[0500] The sending and receiving unit 220 (sending processing unit 2211) may also perform PDCP layer processing, RLC layer processing (e.g., RLC retransmission control), MAC layer processing (e.g., HARQ retransmission control), etc. on the data, control information, etc. obtained from the control unit 210 to generate a bit string to be sent.

[0501] The transmitting and receiving unit 220 (transmitting processing unit 2211) may also perform channel coding (which may also include error correction coding), modulation, mapping, filter processing, DFT processing (as needed), IFFT processing, precoding, digital-to-analog conversion and other transmission processing on the bit string to be transmitted, and output a baseband signal.

[0502] In addition, whether to apply DFT processing may also be based on the setting of transform precoding. For a certain channel (e.g., PUSCH), when transform precoding is valid (enabled), the transmitting and receiving unit 220 (transmitting processing unit 2211) may also perform DFT processing as the above-mentioned transmission processing in order to transmit the channel using a DFT-s-OFDM waveform. Otherwise, the transmitting and receiving unit 220 (transmitting processing unit 2211) may not perform DFT processing as the above-mentioned transmission processing.

[0503] The transmitting and receiving unit 220 (RF unit 222 ) may also perform modulation, filter processing, amplification, etc. on the baseband signal to a radio frequency band, and transmit the signal in the radio frequency band via the transmitting and receiving antenna 230 .

[0504] On the other hand, the transmission and reception unit 220 (RF unit 222 ) may perform amplification, filter processing, demodulation into a baseband signal, etc. on the signal in the radio frequency band received by the transmission and reception antenna 230 .

[0505] The sending and receiving unit 220 (receiving processing unit 2212) can also apply receiving processing such as analog-to-digital conversion, FFT processing, IDFT processing (as needed), filter processing, demapping, demodulation, decoding (which may also include error correction decoding), MAC layer processing, RLC layer processing, and PDCP layer processing to the obtained baseband signal to obtain user data, etc.

[0506] The transmitting and receiving unit 220 (measuring unit 223) may also perform measurements related to the received signal. For example, the measuring unit 223 may also perform RRM measurement, CSI measurement, etc. based on the received signal. The measuring unit 223 may also measure the received power (e.g., RSRP), the received quality (e.g., RSRQ, SINR, SNR), the signal strength (e.g., RSSI), the propagation path information (e.g., CSI), etc. The measurement results may also be output to the control unit 210.

[0507] In addition, the transmitting unit and the receiving unit of the user terminal 20 in the present disclosure may also be composed of at least one of the transmitting and receiving unit 220 and the transmitting and receiving antenna 230 .

[0508] In addition, the sending and receiving unit 220 may also send information related to the location of the terminal based on the output of the AI ​​model.

[0509] In addition, the transmitting and receiving unit 220 may also receive labeled data containing information related to the location of the terminal. The transmitting and receiving unit 220 may also transmit the labeled data collected for training. The transmitting and receiving unit 220 may also receive, as the labeled data, information of a plurality of information related to the location of the terminal, in which a value associated with at least one of integrity, reliability, and uncertainty of the information is greater than a certain threshold. The transmitting and receiving unit 220 may also receive information related to the action of the terminal during a certain period.

[0510] The control unit 210 may also input information related to the location determination reference signal into an artificial intelligence (AI) model to obtain an output related to the location determination of the terminal. The control unit 210 may also input information related to the received power of the location determination reference signal into the AI ​​model, and obtain information related to the received power of a priority subset of location determination reference signal resources as the output. The control unit 210 may also input information related to the received power of the location determination reference signal into the AI ​​model, and obtain at least one of the expected angle, expected time, and uncertainty range of the signal in the terminal as the output. The control unit 210 may also determine whether the application of the AI ​​model is required based on specific conditions.

[0511] The control unit 210 may also obtain labeled data including information related to the location of the terminal for training an artificial intelligence (AI) model.

[0512] (Hardware structure)

[0513] In addition, the block diagram used in the description of the above-mentioned embodiment shows a block of a functional unit. These functional blocks (structural units) are implemented by any combination of at least one of hardware and software. In addition, the implementation method of each functional block is not particularly limited. That is, each functional block can be implemented by a device that is physically or logically combined, or two or more physically or logically separated devices can be directly or indirectly connected (for example, by wire, wireless, etc.) and implemented by these multiple devices. The functional block can also be implemented by combining the above-mentioned one device or the above-mentioned multiple devices with software.

[0514] Here, the functions include judging, deciding, determining, calculating, calculating, processing, deriving, investigating, searching, confirming, receiving, sending, outputting, accessing, solving, selecting, selecting, establishing, comparing, assuming, expecting, regarding, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, assigning, etc., but are not limited to these. For example, a functional block (structural unit) that implements a sending function may also be referred to as a transmitting unit, a transmitter, etc. Any one of them is as described above, and the implementation method is not particularly limited.

[0515] For example, a base station, a user terminal, etc. in one embodiment of the present disclosure may also function as a computer that performs processing of the wireless communication method of the present disclosure. Fig.13 1001, memory 1002, storage 1003, communication device 1004, input device 1005, output device 1006, bus 1007, etc.

[0516] In addition, in the present disclosure, the terms such as device, circuit, equipment, section, and unit can be interchanged. The hardware configuration of the base station 10 and the user terminal 20 may include one or more of the devices shown in the figure, or may exclude some of the devices.

[0517] For example, only one processor 1001 is shown, but there may be multiple processors. In addition, the processing may be performed by one processor, or may be performed by two or more processors simultaneously, sequentially, or in other ways. In addition, the processor 1001 may also be implemented by one or more chips.

[0518] The functions of the base station 10 and the user terminal 20 are realized, for example, by reading specific software (program) into hardware such as the processor 1001 and the memory 1002, so that the processor 1001 performs calculations and controls communication via the communication device 1004, or controls at least one of the reading and writing of data in the memory 1002 and the storage 1003.

[0519] The processor 1001 controls the entire computer by, for example, operating an operating system. The processor 1001 may also be composed of a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic device, a register, etc. For example, at least a part of the control unit 110 (210), the transmission and reception unit 120 (220), etc. described above may also be implemented by the processor 1001.

[0520] In addition, the processor 1001 reads a program (program code), a software module, data, etc. from at least one of the storage 1003 and the communication device 1004 to the memory 1002, and performs various processes based on them. As a program, a program that causes a computer to perform at least a part of the operations described in the above-mentioned embodiments can be used. For example, the control unit 110 (210) can also be implemented by a control program stored in the memory 1002 and operated in the processor 1001, and the other functional blocks can also be implemented in the same way.

[0521] The memory 1002 may also be a computer-readable recording medium, for example, composed of at least one of a read-only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a random access memory (RAM), or other appropriate storage media. The memory 1002 may also be referred to as a register, a cache, a main memory (main storage device), etc. The memory 1002 can store executable programs (program codes), software modules, etc. for implementing the wireless communication method involved in one embodiment of the present disclosure.

[0522] The storage 1003 may also be a computer-readable recording medium, such as a flexible disk, a floppy disk, an optical disk (such as a compact disk (Compact Disc ROM (CD-ROM)), a digital versatile disk, a Blu-ray (Blu-ray) (registered trademark) disk), a removable disk, a hard disk drive, a smart card, a flash memory device (such as a card, a stick, a key drive), a magnetic stripe, a database, a server, or at least one of other suitable storage media. The storage 1003 may also be referred to as an auxiliary storage device.

[0523] The communication device 1004 is hardware (transmitting and receiving device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as a network device, a network controller, a network card, a communication module, etc. For example, in order to realize at least one of frequency division duplex (Frequency Division Duplex (FDD)) and time division duplex (Time Division Duplex (TDD)), the communication device 1004 may also be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc. For example, the above-mentioned transmitting and receiving unit 120 (220), the transmitting and receiving antenna 130 (230), etc. may also be realized by the communication device 1004. The transmitting and receiving unit 120 (220) may also be realized by physically or logically separating the transmitting unit 120a (220a) and the receiving unit 120b (220b).

[0524] The input device 1005 is an input device that receives input from the outside (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.). The output device 1006 is an output device that performs output to the outside (e.g., a display, a speaker, a light emitting diode (LED) lamp, etc.). In addition, the input device 1005 and the output device 1006 may also be an integrated structure (e.g., a touch panel).

[0525] In addition, the processor 1001, the memory 1002 and other devices are connected via a bus 1007 for communicating information. The bus 1007 may be configured as a single bus or may be configured as different buses between the devices.

[0526] In addition, the base station 10 and the user terminal 20 may also be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), and a field programmable gate array (FPGA), and the hardware may be used to implement a part or all of each functional block. For example, the processor 1001 may also be implemented using at least one of these hardware.

[0527] (Variation)

[0528] In addition, the terms described in the present disclosure and the terms required for understanding the present disclosure may also be replaced with terms having the same or similar meanings. For example, channels, code elements, and signals (signals or signaling) may be rewritten one another. In addition, a signal may also be a message. A reference signal may also be referred to as RS, and may also be referred to as a pilot, a pilot signal, etc. depending on the applied standard. In addition, a component carrier (CC) may also be referred to as a cell, a frequency carrier, a carrier frequency, etc.

[0529] A radio frame may also be composed of one or more periods (frames) in the time domain. Each period (frame) of the one or more periods (frames) constituting a radio frame may also be referred to as a subframe. Further, a subframe may also be composed of one or more time slots in the time domain. A subframe may also be a fixed time length (e.g., 1 ms) that is not dependent on a parameter set (numerology).

[0530] Here, the parameter set may also be a communication parameter applied in at least one of the transmission and reception of a certain signal or channel. For example, the parameter set may also represent at least one of the subcarrier spacing (SCS), bandwidth, symbol length, cyclic prefix length, transmission time interval (TTI), the number of symbols per TTI, wireless frame structure, specific filter processing performed by the transmitter and receiver in the frequency domain, specific windowing processing performed by the transmitter and receiver in the time domain, etc.

[0531] A time slot may also be composed of one or more symbols (Orthogonal Frequency Division Multiplexing (OFDM) symbols, Single Carrier Frequency Division Multiple Access (SC-FDMA) symbols, etc.) in the time domain. In addition, a time slot may also be a time unit based on a parameter set.

[0532] A time slot may also contain multiple mini-slots. Each mini-slot may also be composed of one or more symbols in the time domain. In addition, a mini-slot may also be referred to as a sub-slot. A mini-slot may also be composed of a smaller number of symbols than a time slot. A PDSCH (or PUSCH) transmitted in a time unit larger than a mini-slot may also be referred to as a PDSCH (PUSCH) mapping type A. A PDSCH (or PUSCH) transmitted using a mini-slot may also be referred to as a PDSCH (PUSCH) mapping type B.

[0533] Radio frames, subframes, time slots, mini-time slots, and symbols all represent time units for transmitting signals. Radio frames, subframes, time slots, mini-time slots, and symbols may also be referred to by their respective names. In addition, time units such as frames, subframes, time slots, mini-time slots, and symbols in the present disclosure may also be replaced with each other.

[0534] For example, a subframe may be referred to as a TTI, a plurality of consecutive subframes may be referred to as a TTI, and a time slot or a mini time slot may be referred to as a TTI. That is, at least one of a subframe and a TTI may be a subframe (1 ms) in existing LTE, a period shorter than 1 ms (for example, 1-13 symbols), or a period longer than 1 ms. In addition, the unit representing a TTI may be referred to as a time slot, a mini time slot, etc. instead of a subframe.

[0535] Here, TTI refers to, for example, the minimum time unit for scheduling in wireless communication. For example, in the LTE system, the base station schedules each user terminal to allocate wireless resources (frequency bandwidth, transmission power, etc. that can be used in each user terminal) in TTI units. In addition, the definition of TTI is not limited to this.

[0536] TTI can also be a transmission time unit for a data packet (transport block), code block, code word, etc. that has been channel-coded, and can also be a processing unit for scheduling, link adaptation, etc. In addition, when TTI is given, the time interval (for example, the number of symbols) to which a transport block, code block, code word, etc. is actually mapped can also be shorter than the TTI.

[0537] In addition, when a time slot or a mini time slot is called a TTI, one or more TTIs (i.e., one or more time slots or one or more mini time slots) may also be the minimum time unit of scheduling. In addition, the number of time slots (mini time slots) constituting the minimum time unit of scheduling may also be controlled.

[0538] A TTI having a time length of 1 ms may also be referred to as a normal TTI (TTI in 3GPP Rel.8-12), a standard TTI, a long TTI, a normal subframe, a standard subframe, a long subframe, a time slot, etc. A TTI shorter than a normal TTI may also be referred to as a shortened TTI, a short TTI, a partial TTI (partial or fractional TTI), a shortened subframe, a short subframe, a mini time slot, a sub time slot, a time slot, etc.

[0539] In addition, a long TTI (eg, normal TTI, subframe, etc.) may be rewritten as a TTI having a time length exceeding 1 ms, and a short TTI (eg, shortened TTI, etc.) may be rewritten as a TTI having a TTI length shorter than that of a long TTI and longer than 1 ms.

[0540] Resource Block (RB) is a resource allocation unit in the time domain and frequency domain, and may also include one or more consecutive subcarriers (subcarriers) in the frequency domain. The number of subcarriers included in an RB may also be the same regardless of the parameter set, for example, it may be 12. The number of subcarriers included in an RB may also be determined based on the parameter set.

[0541] In addition, an RB may also include one or more symbols in the time domain, and may also be the length of a slot, a mini-slot, a subframe, or a TTI. A TTI, a subframe, etc. may also be composed of one or more resource blocks, respectively.

[0542] In addition, one or more RBs may also be referred to as a physical resource block (Physical RB (PRB)), a sub-carrier group (Sub-Carrier Group (SCG)), a resource element group (Resource Element Group (REG)), a PRB pair, an RB pair, etc.

[0543] In addition, a resource block may be composed of one or more resource elements (RE). For example, one RE may be a radio resource region of one subcarrier and one symbol.

[0544] Bandwidth Part (BWP) (also referred to as partial bandwidth, etc.) can also represent a subset of contiguous common RBs (common resource blocks) for a parameter set in a carrier. Here, common RBs can also be identified by the index of the RB based on the common reference point of the carrier. PRBs can also be defined in a BWP and numbered within the BWP.

[0545] The BWP may include a UL BWP (BWP for UL) and a DL BWP (BWP for DL). For a UE, one or more BWPs may be configured in one carrier.

[0546] At least one of the set BWPs may be activated, and the UE may not assume that it will transmit or receive a specific signal / channel other than the activated BWP. In addition, "cell", "carrier", etc. in the present disclosure may also be rewritten as "BWP".

[0547] In addition, the above-mentioned structures such as radio frames, subframes, time slots, mini-time slots and symbols are only examples. For example, the number of subframes included in a radio frame, the number of time slots per subframe or radio frame, the number of mini-time slots included in a time slot, the number of symbols and RBs included in a time slot or mini-time slot, the number of subcarriers included in an RB, the number of symbols in a TTI, the symbol length, and the cyclic prefix (CP) length can be changed in various ways.

[0548] In addition, the information, parameters, etc. described in the present disclosure may be represented by absolute values, relative values ​​relative to a specific value, or other corresponding information. For example, a wireless resource may also be indicated by a specific index.

[0549] In the present disclosure, the names used for parameters, etc. are not limiting in all respects. Furthermore, the mathematical formulas, etc. using these parameters may be different from those explicitly disclosed in the present disclosure. The various channels (PUCCH, PDCCH, etc.) and information elements can be identified by any suitable names, and therefore, the various names assigned to these various channels and information elements are not limiting in all respects.

[0550] Information, signals, etc. described in the present disclosure may also be represented using any of a variety of different techniques. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be mentioned throughout the above description may also be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, light fields or photons, or any combination thereof.

[0551] In addition, information, signals, etc. can be output in at least one of the following directions: from a higher layer (upper layer) to a lower layer (lower layer), and from a lower layer to a higher layer. Information, signals, etc. can also be input and output via multiple network nodes.

[0552] The input and output information, signals, etc. may be stored in a specific location (e.g., a memory), or may be managed using a management table. The input and output information, signals, etc. may be overwritten, updated, or added. The output information, signals, etc. may also be deleted. The input information, signals, etc. may also be sent to other devices.

[0553] The notification of information is not limited to the aspects / implementations described in the present disclosure, and may also be performed in other ways. For example, the notification of information in the present disclosure may also be implemented through physical layer signaling (e.g., downlink control information (Downlink Control Information (DCI)), uplink control information (Uplink Control Information (UCI))), high-layer signaling (e.g., Radio Resource Control (RRC) signaling, broadcast information (Master Information Block (MIB)), System Information Block (SIB), etc.), Medium Access Control (MAC) signaling), other signals, or a combination thereof.

[0554] In addition, physical layer signaling may also be referred to as layer 1 / layer 2 (Layer 1 / Layer 2 (L1 / L2)) control information (L1 / L2 control signal), L1 control information (L1 control signal), etc. In addition, RRC signaling may also be referred to as an RRC message, such as an RRC connection establishment (RRC Connection Setup) message, an RRC connection reconstruction (RRC Connection Reconfiguration) message, etc. In addition, MAC signaling may also be notified using, for example, a MAC control element (MACControl Element (CE)).

[0555] Furthermore, notification of specific information (eg, notification of “it is X”) is not limited to explicit notification, but may be performed implicitly (eg, by not notifying the specific information or by notifying other information).

[0556] The determination may be made using a value represented by one bit (0 or 1), a true or false value (Boolean value) represented by true or false, or by comparison of numerical values ​​(eg, comparison with a specific value).

[0557] Whether software is called software, firmware, middle-ware, microcode, hardware description language, or other names, it should be broadly interpreted as meaning instructions, instruction sets, code, code segments, program code, program, sub-program, software module, application, software application, software package, routine, sub-routine, object, executable files, execution thread, procedure, function, etc.

[0558] In addition, software, instructions, information, etc. may also be sent and received via a transmission medium. For example, when the software is sent from a website, server, or other remote source using at least one of wired technology (coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), etc.) and wireless technology (infrared, microwave, etc.), at least one of these wired technology and wireless technology is included in the definition of transmission medium.

[0559] The terms "system" and "network" used in the present disclosure can be used interchangeably. "Network" may also refer to a device (eg, a base station) included in the network.

[0560] In the present disclosure, terms such as "precoding", "precoder", "weight (precoding weight)", "Quasi-Co-Location (QCL)", "Transmission Configuration Indication state (TCI state)", "spatial relation", "spatial domain filter", "transmit power", "phase rotation", "antenna port", "antenna port group", "layer", "number of layers", "rank", "resource", "resource set", "resource group", "beam", "beam width", "beam angle", "antenna", "antenna element", and "panel" can be used interchangeably.

[0561] In the present disclosure, terms such as "base station (BS)", "wireless base station", "fixed station", "NodeB", "eNB (eNodeB)", "gNB (gNodeB)", "access point", "transmission point (TP)", "reception point (RP)", "transmission / reception point (TRP)")", "panel", "cell", "sector", "cell group", "carrier", and "component carrier" can be used interchangeably. There are also cases where base stations are referred to by terms such as macro cell, small cell, micro cell, and pico cell.

[0562] A base station can accommodate one or more (for example, three) cells. When a base station accommodates multiple cells, the overall coverage area of ​​the base station can be divided into multiple smaller areas, and each smaller area can also provide communication services through a base station subsystem (for example, a small base station for indoor use (Remote Radio Head (RRH))). Terms such as "cell" or "sector" refer to a part or the entirety of the coverage area of ​​at least one of a base station and a base station subsystem that provides communication services within the coverage area.

[0563] In the present disclosure, the base station sending information to the terminal may also rewrite the base station instructing the terminal to perform control / operation based on the information.

[0564] In the present disclosure, terms such as “mobile station (MS)”, “user terminal”, “user device (User Equipment (UE))”, and “terminal” can be used interchangeably.

[0565] A mobile station may also be referred to as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, hand set, user agent, mobile client, client or several other appropriate terms.

[0566] At least one of the base station and the mobile station may also be referred to as a transmitting device, a receiving device, a wireless communication device, etc. In addition, at least one of the base station and the mobile station may also be a device mounted on a moving object, a moving object body, etc.

[0567] The mobile body refers to a movable object, and the moving speed is arbitrary, including the case where the mobile body stops. The mobile body includes, for example, vehicles, transport vehicles, motor vehicles, two-wheeled motor vehicles, bicycles, connected cars, excavators, bulldozers, wheel loaders, dump trucks, forklifts, trains, buses, two-wheel trailers, rickshaws, ships (ships and other watercraft), airplanes, rockets, artificial satellites, drones, multi-rotor aircraft, quadcopters, hot air balloons and objects carried on them, but are not limited to these. In addition, the mobile body can also be a mobile body that drives autonomously based on operating instructions.

[0568] The mobile object may be a means of transportation (e.g., a vehicle, an airplane, etc.), a mobile object that moves unmanned (e.g., a drone, an autonomous vehicle, etc.), or a robot (manned or unmanned). In addition, at least one of the base station and the mobile station also includes a device that does not necessarily move during communication operations. For example, at least one of the base station and the mobile station may also be an Internet of Things (IoT) device such as a sensor.

[0569] Fig.14 1 is a diagram showing an example of a vehicle according to an embodiment. The vehicle 40 includes a drive unit 41, a steering unit 42, an accelerator pedal 43, a brake pedal 44, a shift lever 45, left and right front wheels 46, left and right rear wheels 47, an axle 48, an electronic control unit 49, various sensors (including a current sensor 50, a rotation speed sensor 51, an air pressure sensor 52, a vehicle speed sensor 53, an acceleration sensor 54, an accelerator pedal sensor 55, a brake pedal sensor 56, a shift lever sensor 57, and an object detection sensor 58), an information service unit 59, and a communication module 60.

[0570] The driving unit 41 is composed of at least one of an engine, a motor, or a combination of an engine and a motor. The steering unit 42 is composed of at least a steering wheel (also called a handle), and steers at least one of the front wheels 46 and the rear wheels 47 based on the operation of the steering wheel operated by a user.

[0571] The electronic control unit 49 is composed of a microprocessor 61, a memory (ROM, RAM) 62, and a communication port (for example, an input / output (IO) port) 63. Signals from various sensors 50-58 provided in the vehicle are input to the electronic control unit 49. The electronic control unit 49 may also be referred to as an electronic control unit (ECU).

[0572] As signals from various sensors 50-58, there are current signals from the current sensor 50 for sensing the current of the motor, speed signals of the front wheels 46 / rear wheels 47 obtained by the speed sensor 51, air pressure signals of the front wheels 46 / rear wheels 47 obtained by the air pressure sensor 52, vehicle speed signals obtained by the vehicle speed sensor 53, acceleration signals obtained by the acceleration sensor 54, depression amount signals of the accelerator pedal 43 obtained by the accelerator pedal sensor 55, operation signals of the shift lever 45 obtained by the shift lever sensor 57, detection signals for detecting obstacles, vehicles, pedestrians, etc. obtained by the object detection sensor 58, and the like.

[0573] The information service unit 59 is composed of various devices for providing (outputting) various information such as driving information, traffic information, entertainment information, etc., such as a navigation system, an audio system, a speaker, a display, a television, and a radio, and one or more ECUs for controlling these devices. 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, etc.

[0574] The information service unit 59 may include input devices for accepting input from the outside (e.g., keyboard, mouse, microphone, switch, button, sensor, touchpad, etc.), and may also include output devices for implementing output to the outside (e.g., display, speaker, LED light, touchpad, etc.).

[0575] The driving assistance system unit 64 is composed of various devices that provide functions for preventing accidents or reducing the driving burden of the driver, such as millimeter wave radar, light detection and ranging (LiDAR), camera, positioning detector (for example, Global Navigation Satellite System (GNSS)), map information (for example, High Definition (HD) map, Autonomous Vehicle (AV) map, etc.), gyroscope system (for example, inertial measurement unit (Inertial Measurement Unit (IMU))), inertial navigation unit (Inertial Navigation System (INS)), etc.), artificial intelligence (Artificial Intelligence (AI)) chip, AI processor, and one or more ECUs that control these devices. In addition, the driving assistance system unit 64 sends and receives various information via the communication module 60 to realize the driving assistance function or the automatic driving function.

[0576] The communication module 60 can communicate with the microprocessor 61 and the structural elements of the vehicle 40 via the communication port 63. For example, the communication module 60 transmits and receives data (information) between the drive unit 41, the steering unit 42, the accelerator pedal 43, the brake pedal 44, the shift lever 45, the left and right front wheels 46, the left and right rear wheels 47, the axle 48, the microprocessor 61 and the memory (ROM, RAM) 62 in the electronic control unit 49, and various sensors 50-58 provided in the vehicle 40 via the communication port 63.

[0577] The communication module 60 can be controlled by the microprocessor 61 of the electronic control unit 49, and is a communication device that can communicate with an external device. For example, various information is sent and received with the external device via wireless communication. The communication module 60 can be inside or outside the electronic control unit 49. The external device can also be, for example, the above-mentioned base station 10, user terminal 20, etc. In addition, the communication module 60 can also be, for example, at least one of the above-mentioned base station 10 and user terminal 20 (it can also function as at least one of the base station 10 and user terminal 20).

[0578] The communication module 60 may also transmit at least one of the signals from the various sensors 50-58 input to the electronic control unit 49, information obtained based on the signals, and information based on input from the outside (user) obtained via the information service unit 59 to an external device via wireless communication. The electronic control unit 49, the various sensors 50-58, the information service unit 59, etc. may also be referred to as an input unit that receives input. For example, the PUSCH transmitted through the communication module 60 may also include information based on the above input.

[0579] The communication module 60 receives various information (traffic information, traffic light information, vehicle information, etc.) sent from an external device, and displays it on the information service unit 59 provided in the vehicle. The information service unit 59 may also be referred to as an output unit that outputs information (for example, information output to a display, a speaker, etc. based on the PDSCH received by the communication module 60 (or data / information decoded from the PDSCH)).

[0580] In addition, the communication module 60 stores various information received from the external device in the memory 62 that can be used by the microprocessor 61. Based on the information stored in the memory 62, the microprocessor 61 can also control the drive unit 41, the steering unit 42, the accelerator pedal 43, the brake pedal 44, the shift lever 45, the left and right front wheels 46, the left and right rear wheels 47, the axle 48, the various sensors 50-58, etc., which are provided in the vehicle 40.

[0581] In addition, the base station in the present disclosure may also be rewritten as a user terminal. For example, various aspects / implementations of the present disclosure may also be applied to a structure in which the communication between a base station and a user terminal is replaced by the communication between multiple user terminals (for example, it may also be referred to as device-to-device (D2D)), vehicle-to-everything (V2X), etc.). In this case, it may also be set as a structure in which the user terminal 20 has the functions possessed by the above-mentioned base station 10. In addition, terms such as "uplink" and "downlink" may also be rewritten as terms corresponding to inter-terminal communication (for example, "sidelink"). For example, uplink channels, downlink channels, etc. may also be rewritten as sidelink channels.

[0582] Likewise, the user terminal in the present disclosure may be rewritten as a base station. In this case, the base station 10 may have the functions of the user terminal 20 described above.

[0583] In the present disclosure, the actions are assumed to be performed by the base station, and sometimes by its upper node depending on the situation. Obviously, in a network including one or more network nodes having a base station, various operations performed for communication with a terminal can be performed by the base station, one or more network nodes other than the base station (for example, Mobility Management Entity (MME)), Serving-Gateway (S-GW), etc., but not limited to these), or a combination thereof.

[0584] The various aspects / implementations described in this disclosure may be used individually or in combination, and may be used in a switched manner as the execution progresses. In addition, the processing procedures, sequences, flow charts, etc. of the various aspects / implementations described in this disclosure may be swapped in order as long as they are not contradictory. For example, for the methods described in this disclosure, the elements of various steps are presented in an illustrative order, but are not limited to the specific order presented.

[0585] The various aspects / embodiments described in the present disclosure may also be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 6th generation mobile communication system (6G), xth generation mobile communication system (xG (x is, for example, an integer or a decimal)), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New radio access (NX), Future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), systems using other appropriate wireless communication methods, and next-generation systems that are expanded, modified, created or specified based on them. In addition, multiple systems can also be combined (for example, LTE or LTE-A, combination with 5G, etc.) for application.

[0586] The phrase “based on” used in the present disclosure does not mean “based only on” unless otherwise specified. In other words, the phrase “based on” means both “based only on” and “based at least on”.

[0587] Any reference to an element using the terms "first", "second", etc. used in this disclosure does not fully define the amount or order of these elements. These terms can be used in this disclosure as a convenient method to distinguish between two or more elements. Therefore, reference to the first and second elements does not mean that only two elements can be used or that the first element must take precedence over the second element in some form.

[0588] The term "determining" used in this disclosure may include a variety of actions. For example, "determining" may also refer to situations where judging, calculating, computing, processing, deriving, investigating, looking up (e.g., searching in a table, database or other data structure), ascertaining, etc. are considered to be "determining".

[0589] In addition, “judgment (decision)” may also refer to situations where receiving (e.g., receiving information), transmitting (e.g., sending information), input (input), output (output), accessing (e.g., accessing data in a memory), etc. are regarded as making a “judgment (decision)”.

[0590] In addition, "judgment (decision)" can also be regarded as a situation where resolving, selecting, choosing, establishing, comparing, etc. are regarded as "judgment (decision)". That is, "judgment (decision)" can also be regarded as a situation where some actions are regarded as "judgment (decision)".

[0591] In addition, "judge (decide)" can also be rewritten as "assuming (assuming)", "expecting (expecting)", "considering (considering)" and so on.

[0592] The “maximum transmit power” described in the present disclosure may refer to the maximum value of the transmit power, the nominal maximum transmit power (the nominal UE maximum transmit power), or the rated maximum transmit power (the rated UE maximum transmit power).

[0593] The terms "connected", "coupled", or all their variations used in this disclosure refer to all direct or indirect connections or combinations between two or more elements, and may include the situation where one or more intermediate elements exist between two elements that are "connected" or "coupled" to each other. The combination or connection between elements may be physical, logical, or a combination thereof. For example, "connection" may also be rewritten as "access".

[0594] In the present disclosure, when two elements are connected, it is possible to consider being "connected" or "combined" to each other using one or more wires, cables, printed electrical connections, etc., as well as using electromagnetic energy with wavelengths in the wireless frequency domain, microwave region, light (both visible and invisible) region, etc. as several non-limiting and non-inclusive examples.

[0595] In the present disclosure, the term "A is different from B" may also mean "A and B are different from each other". In addition, the term may also mean "A and B are different from C, respectively". The terms "separate" and "combined" may also be interpreted in the same manner as "different".

[0596] When the terms “include,” “including,” and variations thereof are used in the present disclosure, these terms have an inclusive meaning, similar to the term “comprising.” Furthermore, the term “or” used in the present disclosure does not have an exclusive OR meaning.

[0597] In the present disclosure, when an article is added by translation like a, an, and the in English, for example, the present disclosure may also include a case where the noun following the article is in plural form.

[0598] In the present disclosure, “below”, “less than”, “above”, “more than”, “equal to”, etc. may be rephrased with each other. Furthermore, in the present disclosure, the terms meaning “good”, “bad”, “big”, “small”, “high”, “low”, “early”, “late”, “wide”, “narrow”, etc. are not limited to the original degree, comparative degree and superlative degree, but may be rephrased with each other. Furthermore, in the present disclosure, the expressions with “ith” (i is an arbitrary integer) attached to the terms meaning “good”, “bad”, “big”, “small”, “high”, “low”, “early”, “late”, “wide”, “narrow”, etc. are not limited to the original degree, comparative degree and superlative degree, but may be rephrased with each other (for example, “highest” and “i-th highest” may be rephrased with each other).

[0599] In the present disclosure, “of,” “for,” “regarding,” “related to,” “associated with,” etc. may also be replaced by each other.

[0600] The invention involved in the present disclosure has been described in detail above, but it is obvious to those skilled in the art that the invention involved in the present disclosure is not limited to the embodiments described in the present disclosure. The invention involved in the present disclosure can be implemented as a modified and altered mode without departing from the gist and scope of the invention determined based on the description of the claims. Therefore, the description of the present disclosure is for the purpose of illustrative description and does not have any limiting meaning on the invention involved in the present disclosure.

Claims

1. A terminal having: A control unit, which obtains labeled data including information related to the location of the terminal for training an artificial intelligence (AI) model; and A sending unit sends the labeled data.

2. The terminal according to claim 1, wherein: The sending unit sends, as the labeled data, information of a plurality of information related to the location of the terminal, for which a value associated with at least one of integrity, reliability, and uncertainty of the information is greater than a certain threshold.

3. The terminal according to claim 1, wherein: The transmitting unit transmits information related to the behavior of the terminal in a certain period.

4. The terminal according to claim 1, wherein: The information on the behavior of the terminal includes information on a validity period of the information.

5. A wireless communication method, which is a wireless communication method of a terminal, comprising: The step of obtaining labeled data containing information related to the location of the terminal for training an artificial intelligence (AI) model; and The step of sending the labeled data.

6. A base station, comprising: A control unit, which obtains labeled data including information related to the location of the terminal for training an artificial intelligence (AI) model; and A sending unit sends the labeled data.

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