Network node and wireless communication method

Abstract

A network node according to one aspect of the present disclosure comprises: a control unit that generates, on the basis of input information, an output relating to (RAT-independent) positioning that does not depend on Radio Access Technology (RAT) using an Artificial Intelligence (AI) model; and a transmission unit that transmits the output. According to the aspect of the present disclosure, it is possible to implement highly precise, highly efficient, and less complex positioning utilizing AI / ML technology.

Description

Network node and wireless communication method

[0001] This disclosure relates to network nodes and wireless communication methods in next-generation mobile communication systems.

[0002] In the Universal Mobile Telecommunications System (UMTS) network, Long Term Evolution (LTE) was specified with the aim of achieving even higher data rates and lower latency (Non-Patent Literature 1). Furthermore, LTE-Advanced (3GPP Rel. 10-14) was specified with the aim of further increasing the capacity and sophistication of LTE (Third Generation Partnership Project (3GPP®) Release (Rel.) 8, 9).

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

[0004] 3GPP TS 36.300 V8.12.0 “Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Overall description; Stage 2 (Release 8)”, April 2010

[0005] In future wireless communication systems (Rel. 19 and beyond), the use of Artificial Intelligence / Machine Learning (AI / ML) technology for terminal positioning (AI / ML-based positioning) of terminals (which may also be called user terminals, user equipment (UE), etc.) is being considered.

[0006] Furthermore, the introduction of low-complexity positioning (e.g., Radio Access Technology (RAT) independent positioning and at least one of hybrid NR positioning) applying AI / ML models to future wireless communication systems (Rel. 20 and beyond) is being considered. However, detailed studies of these positioning methods have not been sufficiently conducted.

[0007] If these considerations are insufficient, it may not be possible to achieve high accuracy, high efficiency, and low complexity positioning using AI / ML technology, potentially hindering improvements in communication quality and throughput.

[0008] Therefore, one of the objectives of this disclosure is to provide a network node and wireless communication method that can achieve high-precision and high-efficiency positioning utilizing AI / ML technology.

[0009] A network node according to one aspect of this disclosure includes a control unit that generates an output relating to Radio Access Technology (RAT) independent positioning using an Artificial Intelligence (AI) model based on input information, and a transmission unit that transmits the output.

[0010] According to one aspect of this disclosure, it is possible to achieve high-precision, high-efficiency, and low-complexity positioning using AI / ML technology.

[0011] Figure 1 shows an example of processing using an AI model. Figure 2 shows an example of an AI model (AI / ML model). Figures 3A to 3C show variations of positioning using DL signals. Figures 4A and 4B show variations of positioning using UL signals. Figure 5 shows an example of transfer information from UE to LMF in GNSS / A-GNSS positioning. Figure 6 shows an example of assistance data transferred from LMF to UE in GNSS / A-GNSS positioning. Figure 7 shows an example of transfer information from UE to LMF in motion sensor positioning. Figures 8A and 8B show an example of transfer information from UE to LMF and an example of assistance data transferred from LMF to UE in barometric pressure sensor positioning. Figures 9A and 9B show examples of transfer information from UE to LMF and assistance data transferred from LMF to UE in WLAN positioning. Figures 10A and 10B show examples of transfer information from UE to LMF and assistance data transferred from LMF to UE in Bluetooth® positioning. Figures 11A and 11B show examples of transfer information from UE to LMF and assistance data transferred from LMF to UE in TBS positioning. Figure 12 shows examples of input and output to a model according to Embodiment 1A. Figure 13 shows an example of a schematic configuration of a wireless communication system according to one embodiment. Figure 14 shows an example of a base station configuration according to one embodiment. Figure 15 shows an example of a user terminal configuration according to one embodiment. Figure 16 shows an example of the hardware configuration of a base station and user terminal according to one embodiment. Figure 17 shows an example of a vehicle according to one embodiment.

[0012] (AI Model) Regarding future wireless communication technology, the use of AI technologies such as machine learning (ML) for network / device control and management is being considered.

[0013] For example, AI technology is being considered for future wireless communication technologies to improve Channel State Information Reference Signal (CSI) feedback (e.g., overhead reduction, improved accuracy, prediction), beam management (e.g., improved accuracy, prediction in the spatiotemporal domain), and position measurement (e.g., improved position estimation / prediction).

[0014] In this disclosure, AI model information used in AI technology may mean information including at least one of the following: - Input / output information of the AI ​​model, - Pre-processing / post-processing information for the input / output of the AI ​​model, - Parameter information of the AI ​​model, - Training information for the AI ​​model, - Inference information for the AI ​​model, - Performance information regarding the AI ​​model.

[0015] In this disclosure, the terms "AI model" and "AI / ML model" may be interpreted interchangeably.

[0016] Here, the input / output information of the above AI model may include information about at least one of the following: • Content of the input / output data (e.g., RSRP, SINR, amplitude / phase information in the channel matrix (or precoding matrix), information about the angle of arrival (AoA), information about the angle of departure (AoD), position information), • Type of input / output data (e.g., immutable value, floating-point number), • Quantization interval (quantization step size) of the input / output data (e.g., 1 dBm for L1-RSRP), • Range of possible input / output data (e.g., [0, 1]).

[0017] In this disclosure, AoA information may include information on at least one of the azimuth angle of arrival and the zenith angle of arrival (ZoA). Also, AoD information may include, for example, information on at least one of the azimuth angle of departure and the zenith angle of departure (ZoD).

[0018] In this disclosure, location information may be location information relating to a UE / NW. Location information may include at least one of the following: information obtained using a positioning system (e.g., satellite positioning system (Global Navigation Satellite System (GNSS), Global Positioning System (GPS), etc.)) (e.g., latitude, longitude, altitude); information of a base station adjacent to (or serving) the UE (e.g., base station / cell identifier (ID), distance between BS and UE, direction / angle of BS(UE) as seen from UE(BS), coordinates of BS(UE) as seen from UE(BS) (e.g., X / Y / Z axis coordinates), etc.); and a specific address of the UE (e.g., Internet Protocol (IP) address). Location information of a UE is not limited to information based on the position of a BS, but may also be information based on a specific point.

[0019] Location information may include information about its own implementation (for example, the location / position of the antenna, the location / position of the antenna panel, the number of antennas, the number of antenna panels, etc.).

[0020] Location information may include mobility information. Mobility information may include information indicating the mobility type, information indicating the movement speed of the UE, the acceleration of the UE, and the direction of movement of the UE, or at least one of these.

[0021] Here, the mobility type may be at least one of the following: fixed location UE, movable / moving UE, no mobility UE, low mobility UE, middle mobility UE, high mobility UE, cell-edge UE, not-cell-edge UE, etc.

[0022] The pre-processing / post-processing information for the input / output of the above AI model may include information on at least one of the following: whether or not to apply normalization (e.g., Z-score normalization (standardization), min-max normalization); parameters for normalization (e.g., mean / variance for Z-score normalization, minimum / maximum value for min-max normalization); whether or not to apply a specific numerical transformation method (e.g., one-hot encoding, label encoding, etc.); and selection rules for whether or not to use the data as training data.

[0023] Figure 1 shows an example of processing using an AI model. For example, Z-score normalization (x) is performed as a preprocessing step for input information x (Original input values). ｎｅｗ Normalized input information x = (x - μ) / σ, where μ is the mean of x and σ is the standard deviation. ｎｅｗ (Normalized input values) can also be input to the AI ​​model, and the output y from the AI ​​model is ｏｕｔ The output values ​​may be post-processed to obtain the final output y (post-processed output values).

[0024] The parameter information of the above AI model may include information on at least one of the following: • Weight information in the AI ​​model (e.g., neuron coefficients (connection coefficients)), • Structure of the AI ​​model, • Type of AI model as a model component (e.g., Residual Network (ResNet), DenseNet, RefineNet, Transformer model, CRBlock, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU)), • Function of the AI ​​model as a model component (e.g., decoder, encoder).

[0025] The weight information in the above AI model may include information on at least one of the following: • the bit width (size) of the weight information, • the quantization interval of the weight information, • the range of possible weights, • the weight parameters in the AI ​​model, • the difference from the AI ​​model before the update (if updated), • the weight initialization method (e.g., zero initialization, random initialization (based on normal distribution / uniform distribution / truncated normal distribution), Xavier initialization (for sigmoid function), He initialization (for Rectified Linear Units (ReLU))).

[0026] Furthermore, the structure of the AI ​​model described above may include information on at least one of the following: • Number of layers, • Layer types (e.g., convolutional layer, activation layer, dense layer, normalization layer, pooling layer, attention layer), • Layer information, • Time-series specific parameters (e.g., bidirectionality, time step), • Parameters for training (e.g., Type of function (L2 regularization, dropout function, etc.), where (e.g., after which layer) to place this function).

[0027] The above layer information may include information on at least one of the following: • Number of neurons in each layer, • Kernel size, • Stride for pooling / convolutional layers, • Pooling method (MaxPooling, AveragePooling, etc.), • Residual block information, • Number of heads, • Normalization method (batch normalization, instance normalization, layer normalization, etc.), • Activation function (sigmoid, tanh function, ReLU, leaky ReLU information, Maxout, Softmax).

[0028] Figure 2 shows an example of an AI model (AI / ML model). This example shows an AI model that includes Model Component #1, ResNet, Model Component #2, a Transformer Model, a Dense Layer, and a Normalization Layer. Thus, one AI model may be included as a component of another AI model. Note that Figure 2 may also show an AI model where processing proceeds from left to right.

[0029] The training information for the above AI model may include information on at least one of the following: • Information on the optimization algorithm (e.g., type of optimization (Stochastic Gradient Descent (SGD)), AdaGrad, Adam, etc.), optimization parameters (learning rate, momentum information, etc.), • Information on the loss function (e.g., information on metrics of the loss function (Mean Absolute Error (MAE), Mean Square Error (MSE), cross-entropy loss, NLLLoss, KL divergence, etc.)), • Parameters to be frozen for training (e.g., layers, weights), • Parameters to be updated (e.g., layers, weights), • Parameters that should be initial parameters for training (to be used as initial parameters) (e.g., layers, weights), • How to train / update the AI ​​model (e.g., (recommended) number of epochs, batch size, number of data to use for training).

[0030] The inference information for the above AI model may include information regarding decision tree branch pruning, parameter quantization, and other related matters.

[0031] The performance information relating to the above AI model may include information regarding the expected value of the loss function defined for the AI ​​model.

[0032] AI model information relating to a specific AI model may be predetermined in the standard, or it may be notified to the UE from the Network (NW). An AI model defined in the standard may be called a reference AI model. AI model information relating to a reference AI model may be called reference AI model information.

[0033] Furthermore, the AI ​​model information in this disclosure may include an index for identifying the AI ​​model (for example, which may be called an AI model index). The AI ​​model information in this disclosure may include an AI model index in addition to / instead of the above-mentioned input / output information of the AI ​​model. The association between the AI ​​model index and the AI ​​model information (for example, input / output information of the AI ​​model) may be predetermined in the standard or notified from the network to the user architecture.

[0034] (UE positioning using AI technology) Fingerprinting localization, which estimates the position of wireless devices by utilizing the propagation characteristics of wireless signals, is widely used in both Line of Sight (LOS) and Non-Line of Sight (NLOS) scenarios.

[0035] In this disclosure, LOS may mean that the UE and the base station are in a line of sight to each other (or there are no obstructions), and NLOS may mean that the UE and the base station are not in a line of sight to each other (or there are obstructions).

[0036] In fingerprint localization, the location of a UE is estimated based on a database / AI model using fingerprints from multiple transmission paths (multipath) of the UE.

[0037] The multi-path information may be, for example, information regarding the angle of arrival (AoA) / angle of departure (AoD) of signals in an optimal / candidate transmission path.

[0038] In the present disclosure, the information regarding AoA may include, for example, information regarding at least one of the azimuth angles of arrival and the zenith angles of arrival. Also, the information regarding AoD may include, for example, information regarding at least one of the azimuth angles of departure and the zenith angles of departure.

[0039] In 3GPP Rel. 16 NR, the following positioning technologies are supported. - Positioning based on DL / UL Time Difference Of Arrival (TDOA), - Positioning based on angles (DL AoD / UL AoA), - Positioning based on multi Round Trip Time (RTT), - Positioning based on Enhanced Cell ID (E-CID).

[0040] In the positioning based on DL / UL TDOA, for example, assume a case where a plurality of base stations (TRP#0-#2) are arranged around the UE. In this positioning method, the position of the UE is estimated (measured) using the measured value of the reference signal time difference (Reference Signal Time Difference (RSTD)). For example, for two specific base stations (TRP#i, #j (i, j are integers)), the RSTD (T ｉ - T ｊ ) takes a certain value (k ｉ，ｊ ), and a hyperbola H ｉ，ｊ can be drawn by connecting the points. The intersection points of multiple such hyperbolas (in this example, H ０，１、 H １，２、 H ２，０The intersection point) may be estimated as the position of the UE. Additionally, the position of the UE may be estimated using the RSRP of the reference signal.

[0041] In the positioning / position measurement method based on DL AoD / UL AoA, the position of the UE is estimated using the measurement value of DL AoD (e.g., θ or φ) or the measurement value of UL AoA (e.g., θ or φ). Additionally, the position of the UE may be estimated using RSRP.

[0042] In the positioning / position measurement method based on multi-RTT, the position of the UE is estimated using a plurality of RTTs calculated from the Tx / Rx time difference of the reference signal (and additionally RSRP, RSRQ, etc.). For example, geometric circles based on RTT can be drawn around each base station. The intersection points of these multiple circles may be estimated as the position of the UE.

[0043] In the positioning / this position measurement method based on E-CID, the position of the UE is estimated based on the geometric positions of the serving cell / neighbor cell and additional measurement results (Tx-Rx time difference, RSRP, RSRQ, etc.).

[0044] The positioning in the above-mentioned DL (DL TDOA, DL AoD) may be performed on the UE side or the LMF side. For example, in UE-based positioning, the UE may calculate the UE position based on various measurement results of the UE and assistance information from the LMF. Additionally, in UE-assisted positioning, the UE reports various measurement results to the LMF, and the LMF may calculate the position of the UE. The assistance information may be information for assisting in the estimation of the UE position.

[0045] The positioning in the above-mentioned UL (UL TDOA, UL AoA) may be performed on the LMF side. In this case, the base station reports various measurement results to the LMF, and the LMF may calculate the position of the UE.

[0046] The positioning in DL and UL (Multi-RTT, E-CID) described above may be performed on the LMF side. In this case, the UE / base station may report various measurement results to the LMF, and the LMF may calculate the UE's position.

[0047] Furthermore, 3GPP Rel. 17 proposes a positioning method using assistance information to further improve positioning accuracy. Assistance information may be transmitted between the UE, base station, and LMF as measurement information for DL / UL-TDOA, DL-AoD / UL-AoA, multi-RTT, and E-CID as described above.

[0048] Assistance information may include information on at least one of the following: • Timing Error Group (TEG) • RSRPP (Path-Specific RSRP) • Expected angle • Adjacent beam information • TRP antenna / beam information • LOS / NLOS indicator • Additional path report.

[0049] TEG may indicate one or more PRS (Positioning Reference Signal) resources whose transmission / reception timing errors (Rx / Tx timing errors) are within a certain margin.

[0050] RSRPP may represent the measurement result of RSRP in the first pass.

[0051] In UL positioning, assistance information regarding the expected angle may indicate the expected UL-AoA / ZoA. This assistance information may be transmitted from the LMF to the base station. Furthermore, this assistance information may support at least one positioning from UL TDOA, UL AoA, and multi-RTT.

[0052] In DL positioning, assistance information regarding the expected angle may include information regarding the expected DL-AoA / ZoA or DL-AoD / ZoD. This assistance information may be transmitted from the LMF to the UE. Furthermore, this assistance information may support at least one positioning method from DL TDOA, DL AoA, and multi-RTT. This improves the accuracy of angle-based UE positioning and enables optimization of Rx beamforming of the UE or base station.

[0053] Furthermore, assistance information regarding the predicted angle may include not only the values ​​of AoA / ZoA / AoD / ZoD themselves as described above, but also information indicating the uncertainty range of these values.

[0054] As additional beam information, adjacent beam information may include a subset of DL-PRS resources for prioritizing DL-AoD reports (Option 1), or information regarding the boresight direction of each DL-PRS resource (Option 2). This allows for optimization of UE's Rx beam sweeping and DL-AoD measurements.

[0055] Additionally, the assistance information may include PRS beam pattern information as additional beam information. This PRS beam pattern information may include information on the relative power between DL-PRS resources for each angle for each TRP.

[0056] The LOS / NLOS indicator may display information regarding Line of Sight (LOS) and Non-Line of Sight (NLOS).

[0057] Furthermore, in order to improve the positioning delay of the UE, pre-set measurement gaps (MG), MG activation via lower layers, MG-less position, PRS Rx / Tx in RRC_INACTIVE state, or on-demand PRS may be set for the UE (or used by the UE).

[0058] In 3GPP Rel. 17 NR, it is agreed that UEs should measure and report the RSRP of adjacent beams in order to improve the accuracy of UE position estimation. For example, in the UE-assisted DL-AoD positioning method, the LMF may indicate that at least one of the following options 1-2 is included in the assistance information.

[0059] Option 1: A subset of PRS resources for the purpose of prioritizing DL-AOD reporting. This subset may be set for each PRS resource depending on the UE's capabilities. The UE may include the PRS measurements required for a subset of PRS in the additional measurements for DL-AoD if the PRS measurements required for the relevant PRS are reported. The required PRS measurements may be DL PRS RSRP / path PRS RSRP. The UE may report PRS measurements only for a subset of PRS resources. The subset related to a PRS resource may reside in the same / different PRS resource set as the PRS resource in question. Option 2: Information regarding boresight direction set for each PRS resource depending on the UE's capabilities.

[0060] In 3GPP Rel. 16 NR, it is agreed that the expected RSTD and its uncertainty range should be provided from the LMF to the UE. Furthermore, in Rel. 17, it is agreed that the expected angle and its uncertainty range should be provided from the LMF to the UE in order to reduce errors and complexities in AoA / AoD measurements.

[0061] In 3GPP Rel. 17 NR, the introduction of a Positioning Reference Unit (PRU) is being considered for positioning. The PRU is being discussed as a reference device with a known location to mitigate transmission and reception timing errors of UE / gNB. The PRU may also be interpreted as UE / gNB / TRP (transmission reception point) / TP (transmission point).

[0062] For example, the PRU may support at least one of the following: • Measuring DL PRS and reporting the relevant measurement (e.g., RSTD / Transmit / Receive Time Difference / RSRP) to the LMF. • Transmitting SRS and enabling the TRP to measure and report the relevant measurement (e.g., Relative Time of Arrival: RTOA / Transmit / Receive Time Difference, AOA) to the LMF. • Operation, measurement, and various parameters (parameters related to transmit / receive timing delay, enhancement of AoD and AOA, and calibration of measurement values). • Reporting the position coordinate information of the reference device to the LMF if the LMF does not have the position coordinate information. • The reference device whose position is known is a UE / gNB. • Accuracy that allows the position of the reference device to be known.

[0063] AI-based positioning using AI models has two main use cases: • Direct AI / ML positioning (AI / ML-based positioning) • AI / ML-assisted positioning.

[0064] Direct AI / ML positioning outputs, for example, UE positioning (UE location). AI / ML assisted positioning outputs, for example, intermediate features. These intermediate features may be input back into the AI / ML model.

[0065] As an example of the AI / ML-assisted positioning output described above, at least one of the following may be included: - Identification of LOS / NLOS (probability of LOS / NLOS), - ToA (time of arrival of PRS / SRS), - Rx-Tx (transmit / receive) time difference, - AoA / AoD, - Number of waves, Rx-Tx (transmit / receive) phase difference (phase measurement of Rel. 18), - DL RSTD / UL TDOA, - DL-PRS / UL-SRS, RSRPs / RSRPPPs, - Likelihood of the above values ​​(e.g., probability of ToA).

[0066] Positioning in Rel. 18 introduces sidelink positioning based on the Sidelink Positioning Protocol (SLPP). For example, SL-RTT, SL-AoA, SL-TDOA, and SL-TOA are introduced. For example, the sidelink reference signal used for position calculation is called SL-PRS. At least one of the following may be used as a measurement based on SL-PRS: SL PRS-RSRP, SL PRS-RSRPP, SL RTOA, SL AoA, sidelink receive-transmit (Rx-Tx) time difference, SL RSTD, SL PRS-RSSI, SL PRS-channel occupancy ratio (CR), and SL PRS-channel busy ratio (CBR). Furthermore, as a measurement related to the carrier phase positioning method, at least one of UL / DL reference signal carrier phase (RSCP) and DL reference signal carrier phase difference (RSCPD) may be used.

[0067] (Use cases for AI-based positioning) Typical use cases for AI / ML-based positioning can be classified as follows, depending on which entity's (or the model owned by) is used and whether the measurement results of either DL signals or UL signals are used for position prediction.

[0068] Figures 3A and 3C show variations in positioning using DL signals. Figures 4A and 4B show variations in positioning using UL signals.

[0069] • Case 1: UE-based positioning using the UE model (direct AI / ML positioning, or AI / ML-assisted positioning). • Case 2a: UE-assisted / LMF-based positioning using the UE model (AI / ML-assisted positioning). • Case 2b: UE-assisted / LMF-based positioning using the LMF model (direct AI / ML positioning). • Case 3a: NG-RAN node-assisted positioning using the gNB model (AI / ML-assisted positioning). • Case 3b: NG-RAN node-assisted positioning using the LMF model (direct AI / ML positioning).

[0070] <Case 1> Case 1 is an example of positioning using a UE-side model and DL signals / channels (see Figure 3A). In Case 1, the UE receives (necessary) assistance information related to positioning (position prediction) from the NW (gNB / LMF). The UE-side model calculates (measures / predicts) the UE position or intermediate value based on the assistance information and DL signals / channels from the NW. The UE transmits the UE position or intermediate value to the NW (LMF).

[0071] <Case 2a> Case 2a is an example of positioning using a UE-side model and DL signals / channels (see Figure 3B). In Case 2a, the UE receives (necessary) assistance information related to positioning (location prediction) from the NW (gNB / LMF). The UE-side model calculates (measures / predicts) intermediate values ​​based on the assistance information and DL signals / channels from the NW. The UE transmits these intermediate values ​​to the NW (LMF).

[0072] <Case 2b> Case 2b is an example of positioning using an LMF-side model and DL signals / channels (see Figure 3C). In Case 2b, the UE transmits the measurement results of the DL signal (a specific signal / channel (e.g., RS)) from the NW to the NW (gNB / LMF). The UE also receives instructions from the NW to collect (necessary) data related to positioning (position prediction). The LMF-side model calculates (measures / predicts) the UE position based on the measurement results of the DL signal.

[0073] <Case 3a> Case 3a is an example of positioning using a gNB-side model and UL signals / channels (see Figure 4A). In Case 3a, the gNB receives (necessary) assistance information related to positioning (position prediction) from the LMF. The gNB-side model calculates (measures / predicts) intermediate values ​​based on the assistance information and the UL signals / channels from the UE. The gNB transmits these intermediate values ​​to the LMF.

[0074] <Case 3b> Case 3b is an example of positioning using an LMF-side model and UL signals / channels (see Figure 4B). In Case 3b, the gNB transmits the measurement results of the UL signal (a specific signal / channel (e.g., RS)) from the UE to the LMF. The gNB also receives (necessary) assistance information related to positioning (position prediction) from the LMF. The LMF-side model calculates (measures / predicts) the UE position based on the measurement results of the UL signal.

[0075] (Positioning other than cellular) By Rel. 18 NR, positioning using technologies other than cellular was specified.

[0076] Positioning that uses technologies other than cellular may also be called RAT-independent positioning, hybrid [NR] positioning, etc.

[0077] RAT-independent positioning may include at least one of the following: • GNSS / Assisted-GNSS positioning. • Motion sensor positioning. • Barometric pressure sensor positioning. • Wireless Local Area Network (WLAN) positioning. • Wi-Fi / Bluetooth® positioning. • Terrestrial Beacon Systems (TBS) positioning.

[0078] For example, GNSS positioning is performed by measuring the distance to four or more positioning satellites (three-point positioning and positioning for time error correction). GNSS positioning may also be performed using, for example, the GNSS systems of each country (e.g., GPS (Global Positioning System) of the United States, Galileo of Europe, GLONASS (Global Navigation Satellite System) of Russia, and QZSS (Quasi-Zenith Satellite System) of Japan).

[0079] While GNSS positioning can be expected to provide highly accurate measurements in outdoor positioning, its positioning accuracy can deteriorate significantly indoors where satellite signals are unavailable or difficult to receive.

[0080] Furthermore, GNSS positioning consumes a significant amount of power, and high-precision positioning requires a certain amount of time.

[0081] GNSS positioning requires receiving assistance data such as time signals, almanac data, and ephemeris data from satellites, and therefore takes minutes to complete. Almanac data is general orbital information for all satellites, while ephemeris data is detailed orbital information for the satellite itself.

[0082] A-GNSS positioning is a positioning method that receives this assistance data from a cellular network without going through satellites.

[0083] In A-GNSS positioning, a UE (SUPL Enabled Terminal (SET)) that can utilize SUPL (Secure User Plane Location) receives assist data from the user plane / SUPL and assist data from the control plane / LPP from a server (SUPL Location Platform (SLP)) / LMF that uses SUPL.

[0084] SUPL (Positioning) may refer to the A-GNSS positioning method, which utilizes the user plane for assist data transmission bearers (providing assist data). SUPL is defined by the Open Mobile Alliance, which is outside the 3GPP standard.

[0085] LPP positioning may be a positioning method that utilizes the control plane to provide assist data.

[0086] Wi-Fi / Bluetooth® positioning is a positioning method that measures distance based on power-based three-point positioning using RSSI (Received Signal Strength Indicator) measurements between the UE and the access point (AP) / beacon.

[0087] This positioning method is characterized by high positional accuracy due to the relatively small range of radio waves. Furthermore, this positioning is suitable for indoor positioning, requiring the placement of numerous APs / beacons in the area where positioning is to be performed.

[0088] Bluetooth 5.1 and later versions support angle-based positioning that utilizes the phase difference between antennas (e.g., AoA positioning / AoD positioning).

[0089] Barometric pressure sensor positioning is a positioning method that uses a barometric pressure sensor to determine the vertical component of the UE's position. In barometric pressure sensor positioning, the UE measures atmospheric pressure and uses assistance data as needed.

[0090] Barometric pressure sensor positioning is preferably used in combination with other positioning methods to determine the 3D position of the UE.

[0091] Motion sensor positioning is a positioning method that calculates the Universal Equilibrium (UE) using different sensors such as accelerometers, gyroscopes, and geomagnetic sensors. The UE estimates relative displacement based on a reference position / reference time.

[0092] Motion sensor positioning is preferably used in combination with other positioning methods to perform hybrid positioning (for example, positioning that utilizes multiple positioning methods that do not depend on RAT).

[0093] TBS positioning is a positioning method that uses a network of ground-mounted transmitters that transmit signals solely for the purpose of positioning.

[0094] TBS positioning is performed using signals from, for example, a Metropolitan Beacon System (MBS) and at least one of a PRS-based TBS. A positioning method utilizing a PRS-based TBS may be part of Observed Time Difference Of Arrival (OTDOA) positioning.

[0095] In TBS positioning, the UE measures the signal from the TBS it receives and uses assistance data as needed.

[0096] In GNSS / A-GNSS positioning, the information / assistance data shown in Figure 5 may be transmitted / transferred from the UE to the LMF. Figure 5 shows the information transmitted / transferred in both UE-assisted positioning and UE-based positioning ("Yes" means it is transmitted, and "No" means it is not transmitted; the same applies to the following figures).

[0097] In GNSS / A-GNSS positioning, assistance data as shown in Figure 6 may be transmitted / transferred from the LMF to the UE.

[0098] In motion sensor positioning, the information / assistance data shown in Figure 7 may be transmitted / transferred from the UE to the LMF. Figure 7 shows the information transmitted / transferred in both UE-assisted positioning and UE-based positioning.

[0099] In barometric pressure sensor positioning, the information shown in Figure 8A may be transmitted / transferred from the UE to the LMF. Figure 8A shows the information transmitted / transferred in UE-assisted positioning and UE-based positioning, respectively.

[0100] In barometric pressure sensor positioning, assistance data shown in Figure 8B may be transmitted / transferred from the LMF to the UE.

[0101] In WLAN positioning, the information shown in Figure 9A may be transmitted / transferred from the UE to the LMF. Figure 9A shows the information transmitted / transferred in UE-assisted positioning and UE-based positioning, respectively.

[0102] In WLAN positioning, assistance data shown in Figure 9B may be transmitted / transferred from the LMF to the UE.

[0103] In Bluetooth positioning, the information / assistance data shown in Figure 10A may be transmitted / transferred from the UE to the LMF. Figure 10A shows the information transmitted / transferred in UE-assisted positioning and UE-based positioning, respectively.

[0104] In Bluetooth positioning, the information shown in Figure 10B may be transmitted / transferred from the LMF to the UE. The information shown in Figure 10B is the proposed UE Bluetooth AoA transmission parameter.

[0105] In TBS positioning, the information / assistance data shown in Figure 11A may be transmitted / transferred from the UE to the LMF. Figure 11A shows the information transmitted / transferred in both UE-assisted positioning and UE-based positioning.

[0106] In TBS positioning, assistance data shown in Figure 11B may be transmitted / transferred from the LMF to the UE.

[0107] (Analysis) In AI / ML Rel. 18 / 19, support for the above cases 1 / 2a / 2b / 3a / 3b is being considered.

[0108] In this disclosure, UE-based / UE-assisted / NG-RAN node-assisted / LMF-based positioning may also be RAT-dependent positioning. These positionings may also be positionings to which timing measurement / timing information (e.g., DL TDOA / UL RTOA / multiple RTT, etc.) is generally applied.

[0109] For example, in AI / ML-based positioning, input information may be identified as timing information, power information, or phase information.

[0110] For example, in AI / ML-assisted positioning, the output information may be identified as timing information and LOS / NLOS indicators.

[0111] However, the cost and complexity of developing positioning methods that rely on RAT are relatively high.

[0112] When cost and other constraints exist (for example, in indoor scenarios), low-complexity positioning methods (e.g., RAT-independent positioning and hybrid [NR] positioning) are preferable.

[0113] In existing systems, UEs with positioning functions independent of RAT use assistance information obtained from the network.

[0114] In this case, signal interruption / scattering / attenuation, or emergency / extreme circumstances, may reduce the accuracy of RAT-independent positioning and hybrid [NR] positioning, or make such positioning impossible.

[0115] To ensure and improve the accuracy of these positionings, the use of AI / ML is being considered. Furthermore, it is expected that the signaling overhead of redundant network assistance data will be reduced by using AI / ML.

[0116] However, there has been insufficient consideration of the detailed specifications and operation when applying AI / ML models / functionality to RAT-independent positioning and hybrid [NR] positioning.

[0117] For example, in model inference, there is insufficient consideration given to what kind of information should be used as input / output for the model.

[0118] Furthermore, for example, in data collection for model training, sufficient consideration has not been given to the generation / transmission of label information and measurement information, assistance information for data collection, and information / signaling for data collection.

[0119] Furthermore, in model performance monitoring, sufficient consideration has not been given to the entities to be monitored (monitoring entities), the generation / transmission of label information, and the information / signaling for performance monitoring.

[0120] If these considerations are insufficient, it may not be possible to achieve high accuracy, high efficiency, and low complexity positioning using AI / ML technology, potentially hindering improvements in communication quality and throughput.

[0121] Therefore, the inventors of this invention conceived a way to solve these problems.

[0122] The embodiments of this disclosure will be described in detail below with reference to the drawings. Each wireless communication method according to the embodiments may be applied individually or in combination.

[0123] (Various substitutions) In this disclosure, words enclosed in parentheses () may indicate an explanation of the preceding word (e.g., an explanation of spelling), a paraphrase, a specific example, or supplementary explanation. Also, in this disclosure, words enclosed in square brackets [] may be interpreted as part of the overall meaning of the text, or they may be interpreted as being excluded (ignored). Note that parentheses () and square brackets [] may be used for purposes / meanings other than those described above.

[0124] In this disclosure, "A / B" and "at least one of A and B" may be interpreted as mutually exclusive. In this disclosure, "A / B / C" may mean "at least one of A, B, and C".

[0125] In this disclosure, terms such as notice, activate, deactivate, indicate (or specify), select, configure, update, and determine may be interpreted interchangeably. In this disclosure, terms such as support, control, controllable, operate, and capable of operating may be interpreted interchangeably.

[0126] In this disclosure, Radio Resource Control (RRC), RRC parameters, RRC messages, higher-layer parameters, fields, Information Elements (IE), settings, etc., may be interpreted interchangeably. In this disclosure, Medium Access Control elements (MAC Control Elements (CE)), update commands, activation / deactivation commands, etc., may be interpreted interchangeably.

[0127] In this disclosure, the upper layer signaling may be any or a combination thereof, such as Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information, and other messages (e.g., messages from the core network, such as positioning protocol messages (e.g., NR Positioning Protocol A (NRPPPa) / LTE Positioning Protocol (LPP)) messages).

[0128] In this disclosure, MAC signaling may include, for example, MAC Control Elements (MAC CEs) and MAC Protocol Data Units (PDUs). Broadcast information may include, for example, Master Information Blocks (MIBs), System Information Blocks (SIBs), Remaining Minimum System Information (RMSIs), and Other System Information (OSIs).

[0129] In this disclosure, physical layer signaling may include, for example, Downlink Control Information (DCI) and Uplink Control Information (UCI).

[0130] In this disclosure, terms such as drop, suspend, cancel, puncture, rate match, postpone, and not send may be interpreted interchangeably.

[0131] In this disclosure, estimation, prediction, and inference may be interpreted interchangeably. Furthermore, in this disclosure, estimate, predict, and infer may be interpreted interchangeably.

[0132] In this disclosure, positioning may be interpreted interchangeably with location determination, location estimation, location prediction, etc. In this disclosure, KPI (Key Performance Indicator) and performance metrics may be interpreted interchangeably. In this disclosure, performance metrics calculation, model monitoring, and performance monitoring may be interpreted interchangeably.

[0133] In the following embodiments, the relevant entities are UE / gNB / LMF to describe an AI model relating to communication between UE / gNB / LMF, but the application of each embodiment of this disclosure is not limited thereto. For example, for communication between other entities (e.g., UE-UE communication), the UE / gNB / LMF in the embodiments below may be read as a first UE, a second UE, a third, and so on. In other words, any UE / gNB / LMF in this disclosure may be read as any UE / gNB / LMF. Also, NW / base station (BS) / gNB / LMF / TRP may be read as one another.

[0134] In this disclosure, antenna port, subband, angle, and delay may be interpreted interchangeably. In this disclosure, NW, base station, gNB, and LMF may be interpreted interchangeably. LMF may be interpreted as a device that implements LMF (such as a server), or it may simply be called a network node.

[0135] In this disclosure, timing, time, duration, time instance, slot, subslot, symbol, subframe, etc., may be interpreted interchangeably.

[0136] In this disclosure, DL [positioning] and UL [positioning] may be interpreted as mutually interchangeable.

[0137] In this disclosure, the measurement RS, PRS, and SRS may be interpreted as interchangeable. Furthermore, PRS may be used for DL ​​positioning, and SRS may be used for UL positioning.

[0138] In this disclosure, gNB, LMF, and NG-RAN may be interpreted interchangeably. LMF is defined as one of the network functions (NFs) provided in the core network and performs communication control related to location information. LMF may be installed in any device on the core network. The LMF-side model may be an AI / ML model installed in a device on the core network. LMF-based positioning may be any method of deriving location information using the LMF-side model.

[0139] In this disclosure, the terms measurement, measurements, RRM measurement, RRM measurements, measured value, measurement result, measurement information, etc., may be interpreted interchangeably.

[0140] In this disclosure, the terms "prediction," "predicted value," "prediction result," "prediction information," "predicted measurements," and "predicted RRM measurements" may be interpreted interchangeably.

[0141] In this disclosure, KPIs may be values ​​derived by the UE in accordance with the specified / established procedures / definitions. KPIs may also represent the quality of the UE's predicted measurements.

[0142] In this disclosure, KPI (Key Performance Indicator), performance metric [s], RSRP (difference value), RSRQ (difference value), SINR (difference value), etc., may be interpreted as interchangeable.

[0143] In this disclosure, KPIs may be hypothetical KPIs or measured KPIs.

[0144] In this disclosure, beam, RS, resource, etc. may be interpreted interchangeably. In this disclosure, beam measurement value, measured beam, etc. may be interpreted interchangeably. In this disclosure, beam prediction value, predicted beam, etc. may be interpreted interchangeably.

[0145] In this disclosure, the prediction result may be a value derived by the UE by the activated model / function (functionality). The model / function may be interpreted as [RRM] measurement prediction model / function, [spatial / time / frequency domain] measurement prediction model / function, [spatial / time / frequency domain] measurement prediction model / function [report], [RRM] model / function for measurement prediction, [spatial / time / frequency domain] model / function for measurement prediction, [spatial / time / frequency domain] model / function [report] for measurement prediction, etc.

[0146] In this disclosure, RAT-independent positioning and hybrid [NR] positioning may be at least one of the following: • GNSS positioning. • A-GNSS positioning. • Barometric pressure sensor positioning. • WLAN positioning. • Wi-Fi / Bluetooth® positioning. • TBS positioning. • Motion sensor positioning. • Hybrid positioning of RAT-dependent and RAT-independent positioning.

[0147] In this disclosure, RAT-independent positioning and hybrid [NR] positioning may be interpreted interchangeably.

[0148] (Wireless Communication Method) In this disclosure, positioning is the primary example of a use case for the AI ​​model. More specifically, embodiments of this disclosure are applicable to any use case of positioning (DL positioning / UL positioning, UE / gNB / LMF-based positioning, and at least one of cases 1 to 3b described above).

[0149] The UE / NW (base station (gNB) / LMF) may perform positioning and various related operations (measurement / prediction / reporting / transmission / reception) by applying the embodiments shown below.

[0150] The UE / NW (base station) may receive various settings for positioning / measurement / reporting. Furthermore, the UE / NW (base station) may report / transmit the corresponding prediction (positioning) results to the NW (LMF).

[0151] The network (base station / LMF) may transmit various settings for positioning, measurement, and reporting to the user audience (UE) / base station. Furthermore, the network may receive corresponding prediction results (reports) from the user audience (UE) / base station.

[0152] The UE / NW (base station / LMF) may control various positioning operations (transmission and reception of related information) by applying the embodiments of this disclosure and the various provisions described above. Furthermore, the UE / NW (base station / LMF) may perform information exchange among multiple entities to realize these various operations.

[0153] In this disclosure, either positioning using an AI / ML model on the UE side or positioning using an AI / ML model on the NW / LMF side may be performed.

[0154] Furthermore, in this disclosure, positioning may be performed using both the UE-side AI / ML model and the NW / LMF-side AI / ML model. In this case, for example, the positioning by the NW / LMF-side model described in this disclosure may be performed based on the output of the UE-side model. Alternatively, the operation in the UE-side model may be performed based on the output of the NW / LMF-side model described in this disclosure.

[0155] <First Embodiment> The first embodiment relates to input / output to a model / function.

[0156] <<Embodiment 1A>> Embodiment 1A describes the inputs (which may also be called input information) and outputs (which may also be called output information) in each use case.

[0157] This embodiment may also be applied to cases of NW / LMF side model and AI / ML assisted positioning.

[0158] The LMF (LMF Side Model) may calculate and output intermediate / assistance data based on the input.

[0159] The intermediate value / assistance data may be, for example, at least one of quality information and information indicating uncertainty.

[0160] The UE may receive the intermediate / assistance data. The UE may calculate / determine its 2D / 3D position based on measurements [defined in the existing system] and at least one of the intermediate / assistance data.

[0161] Figure 12 shows an example of input and output to a model according to Embodiment 1A. In the example shown in Figure 12, the LMF outputs intermediate values ​​to facilitate UE side positioning based on the input and transmits them to the UE.

[0162] According to Embodiment 1A, the inputs and outputs to a model that conform to a use case can be appropriately defined.

[0163] <<Embodiment 1B>> Embodiment 1B describes the details of the input to the model (input for model inference).

[0164] <<<Embodiment 1B-1>>> The input may be power information.

[0165] The power information in question may, for example, be information indicating RSSI.

[0166] The RSSI in question may be, for example, a WLAN RSSI or a Bluetooth beacon RSSI.

[0167] The power information may, for example, be information indicating the transmitted power.

[0168] The information indicating the transmission power may, for example, be information regarding the strength of the UE signal (e.g., WLAN / Bluetooth signal) received by the beacon.

[0169] The power information may, for example, be information indicating the reception quality (e.g., RSRP / RSRQ) of a signal (e.g., a synchronization signal).

[0170] <<Embodiment 1B-2>> The input may be timing information.

[0171] The timing information may, for example, be information relating to at least one of the following: • Time to arrival (ToA) of satellite / WLAN / Bluetooth signals; • Propagation delay; • Round-the-Time (RTT) between the satellite / WLAN access point and the UE; • Timing advance / timing alignment (TA) information (e.g., at least one of a common TA (for multiple UEs), a UE-specific TA, or TA parameters).

[0172] <<Embodiment 1B-3>> The input may be other information besides power information / timing information.

[0173] The other information may include, for example, information relating to at least one of the following: • Doppler / Doppler spread; • AoA / AoD; • Carrier-to-noise ratio of the received signal; • Carrier phase information (e.g., Accumulated Delta Range (ADR)); • Code phase measurement (e.g., pseudo range); • TBS measurement / Code phase (MBS); • Barometric pressure / measurement by a barometric pressure sensor; • UE displacement / movement information (e.g., direction / velocity / distance).

[0174] According to Embodiment 1B, the input to the model for positioning can be appropriately defined.

[0175] <<Embodiment 1C>> Embodiment 1C will describe the details of the output from the model (output from model inference).

[0176] <<<Embodiment 1C-1>>> The output may be information indicating uncertainty.

[0177] This information may include, for example, at least one of the following: location uncertainty and the shape / distribution of that uncertainty.

[0178] <<<Embodiment 1C-2>>> The output may be information indicating quality (quality indicator).

[0179] This information may, for example, be a quality indicator for UE's WLAN measurement / Bluetooth measurement.

[0180] The information in question may include, for example, at least one of error estimation information and bias information.

[0181] <<<Embodiment 1C-3>>> The output may be information relating to measurements on the UE side.

[0182] The information in question may be, for example, at least one of the pieces of information described in Embodiment 1B above.

[0183] <<<Embodiment 1C-4>>> The output may be information relating to integrity.

[0184] This information may include, for example, the protection level, and, if necessary, the achievable objective integrity risk.

[0185] According to Embodiment 1C, the output from the model for positioning can be appropriately defined.

[0186] According to the first embodiment described above, the input / output in positioning that does not depend on the RAT to which AI / ML is applied can be appropriately defined.

[0187] <Second Embodiment> The second embodiment relates to data collection for model training.

[0188] Data for model training may include information on at least one of the following: • Measurement information (on the UE side) in the UE; • Ground truth label information; • Assistance data for measurement / label information.

[0189] The measurement information on the UE side may be, for example, at least one of the pieces of information for inference input described in Embodiment 1B above.

[0190] For example, the correct label information may be generated by a PRU. This correct label information may be called, for example, PRU information.

[0191] For example, the correct label information may be generated by a specific UE. This specific UE may be one that includes, for example, RAT-independent positioning and RAT-dependent positioning with accuracy / confidence higher than a certain threshold. This correct label information may be called, for example, information about RAT-dependent / independent positioning.

[0192] In this disclosure, PRU information and RAT-dependent / independent positioning information may be interpreted interchangeably.

[0193] The assistance data for measurement information / label information may include information on at least one of the following: • Timestamp; • Measurement quality parameters for each measurement; • Reference position / barometric pressure; • Reference time / absolute time; • Satellite ephemeris; • Real Time Kinematic (RTK) observations (e.g., pseudo-range / phase range / phase range ratio (e.g., Doppler / carrier-to-noise ratio)); • Measurement characteristics.

[0194] During training of the LMF side model, the LMF may send a request to the UE for measurements on the UE side.

[0195] The UE may perform the measurements and send the measurement results to the LMF.

[0196] LMF may be configured to provide UE with RAT-independent positioning measurements and reports.

[0197] When AI / ML models / functions on the UE side are deployed / registered / identified / activated, the UE may report the measurement results to the LMF.

[0198] According to the second embodiment described above, it is possible to appropriately define model training data for positioning that is independent of RAT.

[0199] <Third Embodiment> The third embodiment relates to model performance monitoring.

[0200] UE / LMF may also perform model performance monitoring.

[0201] The UE may perform performance monitoring on the UE side of the model, or it may perform performance monitoring on the LMF side of the model.

[0202] The LMF may perform performance monitoring on the LMF side of the model, or it may perform performance monitoring on the UE side of the model.

[0203] <<Embodiment 3A>> Embodiment 3A describes the calculation of performance metrics (or performance indicators).

[0204] The performance metrics for performance monitoring may include at least one of the following: • Vertical / horizontal accuracy (e.g., expressed in cm / dm / m units); • Uncertainty / quality (e.g., expressed in percentage units); • Estimation error.

[0205] The estimation error may include, for example, at least one of the following: • Measurement error of power information (e.g., expressed in dB units); • Measurement error of timing information (e.g., expressed in ms / s units); • Measurement error of carrier phase information (e.g., expressed in degrees units); • Measurement error of UE displacement (e.g., expressed in cm / dm / m units); • Measurement error of UE movement information (e.g., expressed in m / s, km / s units).

[0206] The performance metrics used and their thresholds may be predetermined by specifications / rules, set / instructed by the network, determined based on whether or not the user environment (UE) reports to the network, or determined based on a combination of at least two of these. Alternatively, the user environment (UE) may determine which performance metrics are used and their thresholds. The UE may report this determination to the network.

[0207] In the case of UE-side performance monitoring (performance monitoring performed by the UE, or performance monitoring for the UE-side model), the UE may send a request for the correct label to the LMF.

[0208] The LMF may transmit a correct label to the UE. This correct label may consist of at least one of the PRU information and location information that is authenticated (qualified) on the LMF side and provided to the UE.

[0209] Assistance data for position calculation may be provided from the LMF to the UE.

[0210] PRU information may be transmitted from the PRU to the UE.

[0211] The UE may perform performance monitoring based on settings / instructions from the NW and at least one of predefined rules.

[0212] For example, the UE may perform performance monitoring based on a set / instructed / specified period / number of times. For example, the UE may perform performance monitoring every X minutes / hours and at least Y times after the activation of the AI / ML model.

[0213] For example, the UE may perform performance monitoring based on configured / instructed / defined thresholds. For example, the UE may perform performance monitoring when the output value exceeds (or falls below) the threshold.

[0214] In the case of LMF-side performance monitoring (performance monitoring performed by the LMF, or performance monitoring for the LMF-side model), the LMF may send a request to the UE for the inference output.

[0215] The UE may report the inference output corresponding to the request to the LMF.

[0216] PRU information may be sent to the UE, and the inference output may be sent by the UE to the LMF.

[0217] The UE may send a request to the LMF asking it to perform performance monitoring.

[0218] According to Embodiment 3A, the determination of performance metrics related to performance monitoring and the implementation of performance monitoring can be appropriately defined.

[0219] <<Embodiment 3B>> Embodiment 3B describes decision-making related to performance monitoring.

[0220] The decision may include, for example, a decision to perform at least one of the following actions: activate / deactivate the model, switch models, update the model, and perform a fallback action.

[0221] The UE may make the decision based on the results of performance monitoring.

[0222] The UE may make decisions regarding performance monitoring of the UE side model, or it may make decisions regarding performance monitoring of the LMF side model.

[0223] The LMF may make decisions regarding performance monitoring of the LMF side model, or it may make decisions regarding performance monitoring of the UE side model.

[0224] If the UE makes such a decision, the UE may or may not report the decision to the LMF.

[0225] If the LMF makes such a decision, the LMF may instruct the UE to perform at least one of the following actions: for example, activating / deactivating the model, switching models, updating models, and performing a fallback action.

[0226] According to Embodiment 3B, the operation based on performance monitoring can be appropriately defined.

[0227] According to the third embodiment described above, it is possible to appropriately define the performance monitoring of a model for positioning that is independent of RAT.

[0228] <Supplement> <<Model Information>> In this disclosure, AI model information (or simply "model") may mean information including at least one of the following: - Input / output information of the AI ​​model. - Pre-processing / post-processing information for the input / output of the AI ​​model. - Parameter information of the AI ​​model. - Training information for the AI ​​model. - Inference information for the AI ​​model. - Performance information regarding the AI ​​model.

[0229] Here, the input / output information of the above AI model may include information about at least one of the following: • Content of the input / output data (e.g., RSRP, SINR, amplitude / phase information in the channel matrix (or precoding matrix), information about the angle of arrival (AoA), information about the angle of departure (AoD), position information). • Auxiliary data information (may be called metadata). • Type of input / output data (e.g., immutable value, floating-point number). • Bit width of the input / output data (e.g., 64 bits for each input value). • Quantization interval (quantization step size) of the input / output data (e.g., 1 dBm for L1-RSRP). • Range of possible input / output data (e.g., [0, 1]).

[0230] In this disclosure, AoA information may include information on at least one of the azimuth angle of arrival and the zenith angle of arrival (ZoA). Also, AoD information may include, for example, information on at least one of the azimuth angle of departure and the zenith angle of departure (ZoD).

[0231] In this disclosure, location information may be location information relating to a UE / NW. Location information may include at least one of the following: information obtained using a positioning system (e.g., satellite positioning system (Global Navigation Satellite System (GNSS), Global Positioning System (GPS), etc.)) (e.g., latitude, longitude, altitude); information of a BS adjacent to (or serving) the UE (e.g., BS / cell identifier (ID), distance between BS and UE, direction / angle of BS(UE) as seen from UE(BS), coordinates of BS(UE) as seen from UE(BS) (e.g., X / Y / Z axis coordinates), etc.); and a specific address of the UE (e.g., Internet Protocol (IP) address). Location information of a UE is not limited to information based on the location of a BS, but may also be information based on a specific point.

[0232] Location information may include information about its own implementation (for example, the location / position of the antenna, the location / position of the antenna panel, the number of antennas, the number of antenna panels, etc.).

[0233] Location information may include mobility information. Mobility information may include information indicating the mobility type, information indicating the movement speed of the UE, the acceleration of the UE, and the direction of movement of the UE, or at least one of these.

[0234] Here, the mobility type may be at least one of the following: fixed location UE, movable / moving UE, no mobility UE, low mobility UE, middle mobility UE, high mobility UE, cell-edge UE, not-cell-edge UE, etc.

[0235] In this disclosure, the environmental information (for the data) may also be information about the environment in which the data is acquired / used, and may include, for example, frequency information (such as band ID), environment type information (information indicating at least one of indoor, outdoor, Urban Macro (UMa), Urban Micro (Umi), etc.), and information indicating Line of Sight (LOS) / Non-Line of Sight (NLOS).

[0236] Here, LOS may mean that the UE and BS are in a line of sight to each other (or there are no obstructions), and NLOS may mean that the UE and BS are not in a line of sight to each other (or there are obstructions). The information indicating LOS / NLOS may be a soft value (e.g., the probability of LOS / NLOS) or a hard value (e.g., either LOS or NLOS).

[0237] In this disclosure, metadata may mean, for example, information about input / output information suitable for an AI model, information about acquired / acquirable data, etc. Specifically, metadata may include information about RS (e.g., CSI-RS / SRS / SSB, etc.) beams (e.g., the angle of each beam, 3dB beamwidth, shape of the beam being directed, number of beams), gNB / UE antenna layout information, frequency information, environmental information, metadata ID, etc. The metadata may also be used as input / output for the AI ​​model.

[0238] The pre-processing / post-processing information for the input / output of the above AI model may include information on at least one of the following: • Whether or not to apply normalization (e.g., Z-score normalization (standardization), min-max normalization). • Parameters for normalization (e.g., mean / variance for Z-score normalization, minimum / maximum value for min-max normalization). • Whether or not to apply a specific numerical transformation method (e.g., one-hot encoding, label encoding, etc.). • Selection rules for whether or not to use the data as training data.

[0239] For example, Z-score normalization (x) is performed as a preprocessing step for input information x. ｎｅｗ Normalized input information x = (x - μ) / σ, where μ is the mean of x and σ is the standard deviation. ｎｅｗ You can also input this into the AI ​​model, and the output y from the AI ​​model will be... ｏｕｔ The final output y may be obtained by applying post-processing to the result.

[0240] The parameter information of the above AI model may include information on at least one of the following: • Weight information in the AI ​​model (e.g., neuron coefficients (connection coefficients)); • Structure of the AI ​​model; • Type of AI model as a model component (e.g., Residual Network (ResNet), DenseNet, RefineNet, Transformer model, CRBlock, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU)); • Function of the AI ​​model as a model component (e.g., decoder, encoder).

[0241] The weight information in the above AI model may include information on at least one of the following: • The bit width (size) of the weight information. • The quantization interval of the weight information. • The granularity of the weight information. • The range of possible weight information. • The weight parameters in the AI ​​model. • Information on the difference from the AI ​​model before the update (if updated). • The method of weight initialization (e.g., zero initialization, random initialization (based on normal distribution / uniform distribution / truncated normal distribution), Xavier initialization (for sigmoid function), He initialization (for Rectified Linear Units (ReLU))).

[0242] Furthermore, the structure of the AI ​​model described above may include information on at least one of the following: • Number of layers; • Layer types (e.g., convolutional layer, activation layer, dense layer, normalization layer, pooling layer, attention layer); • Layer information; • Time-series specific parameters (e.g., bidirectionality, time step); • Training parameters (e.g., type of function (L2 regularization, dropout function, etc.), where to place this function (e.g., after which layer)).

[0243] The above layer information may include information on at least one of the following: • Number of neurons in each layer. • Kernel size. • Stride for pooling / convolutional layers. • Pooling method (e.g., MaxPooling, AveragePooling). • Residual block information. • Number of heads. • Normalization method (e.g., batch normalization, instance normalization, layer normalization). • Activation function (sigmoid, tanh function, ReLU, leaky ReLU information, Maxout, Softmax).

[0244] One AI model may be included as a component of another AI model. For example, one AI model may be one in which processing proceeds in the following order: Model component #1 is ResNet, Model component #2 is a transformer model, a dense layer, and a normalization layer.

[0245] The training information for the above AI model may include information on at least one of the following: • Information on the optimization algorithm (e.g., type of optimization (Stochastic Gradient Descent (SGD)), AdaGrad, Adam, etc.), optimization parameters (learning rate, momentum information, etc.). • Information on the loss function (e.g., information on metrics of the loss function (Mean Absolute Error (MAE), Mean Square Error (MSE), cross-entropy loss, NLLLoss, Kullback-Leibler (KL) divergence, etc.)). • Parameters to be frozen for training (e.g., layers, weights). • Parameters to be updated (e.g., layers, weights). • Parameters that should be initial parameters for training (to be used as initial parameters) (e.g., layers, weights). • How to train / update the AI ​​model (e.g., (recommended) number of epochs, batch size, number of data points to use for training).

[0246] The inference information for the above AI model may include information regarding decision tree branch pruning, parameter quantization, and the functionality of the AI ​​model. Here, the functionality of the AI ​​model may be at least one of the following: time-domain beam prediction, spatial-domain beam prediction, autoencoder for CSI feedback, autoencoder for beam management, etc.

[0247] Autoencoders for CSI feedback may be used as follows: • The UE inputs the CSI / channel matrix / precoding matrix to the encoder's AI model and sends the encoded bits output as CSI feedback (CSI report). • The BS inputs the received encoded bits to the decoder's AI model and reconstructs the CSI / channel matrix / precoding matrix output.

[0248] In spatial domain beam prediction, UE / BS may input measurement results (beam quality, e.g., RSRP) based on a sparse (or wide) beam into an AI model and output a dense (or narrow) beam quality.

[0249] In time-domain beam forecasting, UE / BS may input time-series (past, present, etc.) measurement results (beam quality, e.g., RSRP) into an AI model to output future beam quality.

[0250] The performance information relating to the above AI model may include information regarding the expected value of the loss function defined for the AI ​​model.

[0251] The AI ​​model information in this disclosure may include information regarding the scope of application (applicability) of the AI ​​model. This scope may be indicated by a physical cell ID, a serving cell index, or the like. Information regarding the scope may be included in the environmental information described above.

[0252] AI model information relating to a specific AI model may be predetermined in the standard, or it may be notified to the UE from the Network (NW). An AI model defined in the standard may be called a reference AI model. AI model information relating to a reference AI model may be called reference AI model information.

[0253] Furthermore, the AI ​​model information in this disclosure may include an index for identifying the AI ​​model (which may be called, for example, an AI model index, an AI model ID, or a model ID). The AI ​​model information in this disclosure may include, in addition to or instead of, the AI ​​model index, in addition to the AI ​​model input / output information described above. The association between the AI ​​model index and the AI ​​model information (for example, the AI ​​model input / output information) may be predetermined in the standard or notified from the network to the user architecture.

[0254] The AI ​​model information in this disclosure may be associated with an AI model and may be referred to as relevant information, or simply relevant information. The relevant information does not necessarily have to explicitly include information for identifying an AI model. For example, the relevant information may only include metadata.

[0255] In this disclosure, ML model file information may be at least one of the following: the format / size / encoding of the ML model file, the runtime context (e.g., runtime environment / library), and the required computational resources.

[0256] <<Notification of Information to the UE>> In the embodiments described above, notification of any information from the Network (NW) (e.g., Base Station (BS)) to the UE (in other words, reception of any information from the BS at the UE) may be performed using physical layer signaling (e.g., DCI), higher layer signaling (e.g., RRC signaling, MAC CE), specific signals / channels (e.g., PDCCH, PDSCH, reference signal), or a combination thereof.

[0257] If the above notification is made by a MAC CE, the MAC CE may be identified by the inclusion of a new Logical Channel ID (LCID) not defined in existing standards in the MAC subheader.

[0258] If the above notification is made by DCI, the notification may be made by a specific field of the DCI, a Radio Network Temporary Identifier (RNTI) used to scramble the Cyclic Redundancy Check (CRC) bits assigned to the DCI, or the format of the DCI.

[0259] Furthermore, notification of any information to the UE in the above-described embodiment may be periodic, semi-persistent (triggered by instructions from the UE or gNB), or aperiodic (triggered by instructions from the UE or gNB).

[0260] In the embodiments described above, the UE may receive information from the NW as at least one of the following QCL rules: • QCL type A. • QCL type B. • QCL type C. • QCL type D.

[0261] In the embodiments described above, the QCL source RS for each QCL type may be at least one of the following RSs: • SSB; • CSI-RS with / without repetition; • TRS; • DMRS for PDCCH / PDSCH.

[0262] In the embodiments described above, information from the network may be set / instructed by the following methods: - Common to multiple UEs, or individual to a UE. - Cell-specific, or common to multiple cells. - Per UE / Per CC / Per BWP / Per band / Per cell / Per cell group (CG).

[0263] <<Notification of Information from UE>> Notification of any information from the UE to the NW in the embodiments described above (in other words, transmission / reporting of any information from the UE to the BS) may be performed using physical layer signaling (e.g., UCI), higher layer signaling (e.g., RRC signaling, MAC CE), specific signals / channels (e.g., PUCCH, PUSCH, PRACH, reference signals), or a combination thereof.

[0264] If the above notification is made by a MAC CE, the MAC CE may be identified by the inclusion of a new LCID not specified in existing standards in the MAC subheader.

[0265] If the above notice is made by the UCI, the notice may be transmitted using PUCCH or PUSCH.

[0266] Furthermore, the notification of any information from the UE in the above-described embodiment may be periodic, semi-persistent (triggered by instructions from the UE or gNB), or aperiodic (triggered by instructions from the UE or gNB).

[0267] <<Regarding the application of each embodiment>> In a UE / BS (NW / gNB / LMF / NG-RAN), specific (one or more) processes / operations / controls / assumptions / information for at least one of the embodiments described above may be applied (or used) if any or more of the following conditions are met: - A higher-layer parameter indicating the specific process / operation / control / assumption / information is set; - The specific process / operation / control / assumption / information is determined based on the relevant higher-layer parameter; - The specific process / operation / control / assumption / information is designated / activated / triggered by MAC CE / DCI / UCI / resource / channel / RS; - A specific UE capability indicating (or related to) the specific process / operation / control / assumption / information is reported or supported; - The application of the specific process / operation / control / assumption / information is determined based on specific conditions.

[0268] The above-mentioned specific UE capabilities may include at least one of the following: - Supporting the above-mentioned specific processing / operation / control / assumment / information; - Supporting AI / ML-based (using AI / ML models) RAT-independent positioning.

[0269] Furthermore, the above-mentioned specific UE capability may be a capability that applies across all frequencies (commonly regardless of frequency), a capability per frequency (e.g., one or a combination thereof, such as cell, band, band combination, BWP, component carrier, etc.), a capability per frequency range (e.g., Frequency Range 1 (FR1), FR2, FR3, FR4, FR5, FR2-1, FR2-2), a capability per subcarrier spacing (SCS), or a capability per feature set (FS) or feature set per component-carrier (FSPC).

[0270] Furthermore, the specific UE capabilities described above may be capabilities that apply across all duplexing schemes (common to all duplexing schemes regardless of the duplexing scheme), or they may be capabilities specific to each duplexing scheme (e.g., Time Division Duplex (TDD), Frequency Division Duplex (FDD)).

[0271] If the above conditions are not met, UE / BS may follow the behavior specified in existing 3GPP releases.

[0272] Information on whether one or more of the above embodiments / options / choices / examples apply / are used, or which of the above embodiments / options / choices / examples apply / are used, may be based on several of the following methods: • The information is set by one or more higher-layer parameters / RRC IEs. • The information is determined by one or more relevant higher-layer parameters / RRC IEs. • The information is indicated by MAC CE / DCI. • The information is based on one or more UE capabilities. • The information is described / defined in the specification. • The information is based on conditions described / defined in the specification. • The information is determined by a combination of several of the above. For example, the information is determined by the setting / indication of higher-layer parameters / MAC CE / DCIs and reported by UE capabilities.

[0273] The above multiple embodiments / options / choices may be combined into a single embodiment / option / choice.

[0274] (Note) The following inventions are added with respect to one embodiment of the present disclosure. [Note 1] A network node having a control unit that generates an output relating to Radio Access Technology (RAT) independent positioning using an Artificial Intelligence (AI) model based on input information, and a transmission unit that transmits the output. [Note 2] The network node according to Note 1, wherein the input information includes at least one of power information, timing information, and other information separate from the power information and the timing information. [Note 3] The network node according to Note 1 or Note 2, wherein the output is an intermediate value for positioning at a terminal, and the output includes at least one of uncertainty information, a quality indicator, information regarding measurements at the terminal, and information regarding completeness. [Note 4] The network node according to any one of Notes 1 to 3, wherein the control unit uses at least one of measurement information at the terminal, correct label information, and assistance data of at least one of measurement and label for training the AI ​​model. [Note 5] The control unit is a network node according to any one of Notes 1 to 4 that performs performance monitoring of the AI ​​model.

[0275] (Wireless Communication System) The configuration of a wireless communication system according to one embodiment of this disclosure will be described below. In this wireless communication system, communication is performed using any of the wireless communication methods according to the above embodiments of this disclosure, or a combination thereof.

[0276] Figure 13 shows an example of a schematic configuration of a wireless communication system according to one embodiment. The wireless communication system 1 (which may also be simply called system 1) may be a system that realizes communication using Long Term Evolution (LTE), 5th generation mobile communication system New Radio (5G NR), etc., as specified by the Third Generation Partnership Project (3GPP).

[0277] Furthermore, the wireless communication system 1 may support dual connectivity between multiple Radio Access Technologies (RATs) (Multi-RAT Dual Connectivity (MR-DC)). MR-DC may include dual connectivity between LTE (Evolved Universal Terrestrial Radio Access (E-UTRA)) and NR (E-UTRA-NR Dual Connectivity (EN-DC)), dual connectivity between NR and LTE (NR-E-UTRA Dual Connectivity (NE-DC)), and the like.

[0278] In EN-DC, the LTE (E-UTRA) base station (eNB) is the Master Node (MN), and the NR base station (gNB) is the Secondary Node (SN). In NE-DC, the NR base station (gNB) is the MN, and the LTE (E-UTRA) base station (eNB) is the SN.

[0279] The wireless communication system 1 may support dual connectivity between multiple base stations within the same RAT (for example, dual connectivity where both MN and SN are NR base stations (gNB) (NR-NR Dual Connectivity (NN-DC))).

[0280] The wireless communication system 1 may include a base station 11 that forms a macrocell C1 with relatively wide coverage, and base stations 12 (12a-12c) located within the macrocell C1 that form a small cell C2 that is narrower than the macrocell C1. User terminals 20 may be located within at least one cell. The arrangement, number, shape, size, etc., of each cell and user terminal 20 are not limited to the configuration shown in the figure. Hereinafter, when base stations 11 and 12 are not distinguished, they will be collectively referred to as base station 10.

[0281] The wireless communication system 1 may utilize Multi Input Multi Output (MIMO). For example, one cell may be formed by one antenna / base station 10, or by multiple antennas / base stations 10. One [virtual] cell (which may be called a supercell, for example) may be composed of multiple [virtual] cells (which may be called subcells, for example). A supercell may correspond to a cell with a fixed physical range, and a subcell may correspond to a cell whose physical range fluctuates quasi-statically / dynamically. In this case, the wireless communication system 1 may be called a cell-free system.

[0282] The user terminal 20 may be connected to at least one of the multiple base stations 10. The user terminal 20 may utilize at least one of Carrier Aggregation (CA) using multiple Component Carriers (CC) and Dual Connectivity (DC).

[0283] Each CC may be included in at least one of the first frequency band (Frequency Range 1 (FR1)) and the second frequency band (Frequency Range 2 (FR2)). A macrocell C1 may be included in FR1, and a small cell C2 may be included in FR2. For example, FR1 may be a frequency band of 6 GHz or less (sub-6 GHz), and FR2 may be a frequency band above 24 GHz. Note that the frequency bands and definitions of FR1 and FR2 are not limited to these, and for example, FR1 may be in a frequency band higher than FR2.

[0284] Furthermore, the user terminal 20 may communicate in each CC using at least one of Time Division Duplex (TDD) and Frequency Division Duplex (FDD).

[0285] Multiple base stations 10 may be connected by wire (e.g., optical fiber compliant with Common Public Radio Interface (CPRI), X2 / Xn interface, etc.) or wireless (e.g., NR communication). For example, when NR communication is used as a backhaul between base stations 11 and 12, base station 11, which is the upstream station, may be called an Integrated Access Backhaul (IAB) donor, and base station 12, which is the relay station, may be called an IAB node.

[0286] Base station 10 may be connected to the core network 30 via other base stations 10 or directly. The core network 30 may include at least one of the following: Evolved Packet Core (EPC), 5G Core Network (5GCN), Next Generation Core (NGC), etc.

[0287] The core network 30 may 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), and Operation, Administration and Maintenance (Management) (OAM). Multiple functions may be provided by a single network node. Furthermore, communication with an external network (e.g., the Internet) may occur via the DN.

[0288] The user terminal 20 may be a terminal that supports at least one of the following communication methods: LTE, LTE-A, 5G, etc.

[0289] In the wireless communication system 1, an orthogonal frequency division multiplexing (OFDM)-based wireless access scheme may be used. For example, Cyclic Prefix OFDM (CP-OFDM), Discrete Fourier Transform Spread OFDM (DFT-s-OFDM), Orthogonal Frequency Division Multiple Access (OFDMA), Single Carrier Frequency Division Multiple Access (SC-OFDM), etc., may be used in at least one of the downlink (DL) and uplink (UL).

[0290] The wireless access method may also be called a waveform. In wireless communication system 1, other wireless access methods (for example, other single-carrier transmission methods, other multi-carrier transmission methods) may be used for the UL and DL wireless access methods.

[0291] In the wireless communication system 1, a Physical Downlink Shared Channel (PDSCH), a Broadcast Channel (PBCH), or a Physical Downlink Control Channel (PDCCH) may be used as the downlink channel, which is shared by each user terminal 20.

[0292] Furthermore, in the wireless communication system 1, the uplink channel may include a Physical Uplink Shared Channel (PUSCH), a Physical Uplink Control Channel (PUCCH), a Physical Random Access Channel (PRACH), or the like, all of which are shared by each user terminal 20.

[0293] User data, higher-layer control information, and System Information Blocks (SIBs) are transmitted via PDSCH. User data and higher-layer control information may also be transmitted via PUSCH. Furthermore, Master Information Blocks (MIBs) may be transmitted via PBCH.

[0294] Lower-layer control information may be transmitted by PDCCH. The lower-layer control information may include, for example, Downlink Control Information (DCI) which includes scheduling information for at least one of PDSCH and PUSCH.

[0295] Furthermore, the DCI that schedules PDSCH may be called DL assignment, DL DCI, etc., and the DCI that schedules PUSCH may be called UL grant, UL DCI, etc. Furthermore, PDSCH may be read as DL data, and PUSCH may be read as UL data.

[0296] PDCCH detection may utilize a Control Resource Set (CORESET) and a search space. A CORESET corresponds to the resources used to search for DCIs. A search space corresponds to the search area and search method for PDCCH candidates. A single CORESET may be associated with one or more search spaces. A UE may monitor CORESETs associated with a given search space based on the search space configuration.

[0297] A single search space may correspond to one or more PDCCH candidates corresponding to aggregation levels. One or more search spaces may be referred to as a search space set. In this disclosure, "search space," "search space set," "search space configuration," "search space set configuration," "CORESET," and "CORESET configuration" may be interpreted interchangeably.

[0298] PUCCH may transmit uplink control information (UCI) including at least one of channel state information (CSI), delivery acknowledgment information (for example, Hybrid Automatic Repeat reQuest ACKnowledgement (HARQ-ACK), ACK / NACK, etc.), and scheduling request (SR). PRACH may transmit a random access preamble for establishing a connection with the cell.

[0299] In this disclosure, downlinks, uplinks, etc., may be expressed without the prefix "link." Also, the prefix "physical" may be omitted from the names of various channels.

[0300] In the wireless communication system 1, a synchronization signal (SS), a downlink reference signal (DL-RS), etc., may be transmitted. In the wireless communication system 1, the DL-RS may include a cell-specific reference signal (CRS), a channel state information reference signal (CSI-RS), a demodulation reference signal (DMRS), a positioning reference signal (PRS), a phase tracking reference signal (PTRS), etc.

[0301] The synchronization signal may be, for example, at least one of a Primary Synchronization Signal (PSS) and a Secondary Synchronization Signal (SSS). A signal block including SS (PSS, SSS) and PBCH (and DMRS for PBCH) may be called an SS / PBCH block, SS Block (SSB), etc. Note that SS, SSB, etc. may also be called reference signals.

[0302] Furthermore, in the wireless communication system 1, the uplink reference signal (UL-RS) may include a sounding reference signal (SRS), a demodulation reference signal (DMRS), etc. The DMRS may also be called a user-specific reference signal (UE-specific Reference Signal).

[0303] (Base Station) Figure 14 shows an example of the configuration of a base station according to one embodiment. The base station 10 includes a control unit 110, a transmitting / receiving unit 120, a transmitting / receiving antenna 130, and a transmission line interface 140. Note that one or more of the control unit 110, the transmitting / receiving unit 120, the transmitting / receiving antenna 130, and the transmission line interface 140 may be provided.

[0304] In this example, the functional blocks of the characteristic parts of this embodiment are mainly shown, and it may be assumed that the base station 10 also has other functional blocks necessary for wireless communication. Some of the processing of each part described below may be omitted.

[0305] The control unit 110 controls the entire base station 10. The control unit 110 can be composed of a controller, control circuit, etc., as described based on common understanding in the technical field related to this disclosure.

[0306] The control unit 110 may control signal generation, scheduling (e.g., resource allocation, mapping), etc. The control unit 110 may also control transmission and reception, measurement, etc., using the transmitting / receiving unit 120, transmitting / receiving antenna 130, and transmission path interface 140. The control unit 110 may generate data to be transmitted as signals, control information, sequences, etc., and transfer them to the transmitting / receiving unit 120. The control unit 110 may also perform call processing of communication channels (setting, releasing, etc.), status management of the base station 10, management of wireless resources, etc.

[0307] The transmitting / receiving unit 120 may include a baseband unit 121, a radio frequency (RF) unit 122, and a measurement unit 123. The baseband unit 121 may include a transmission processing unit 1211 and a reception processing unit 1212. The transmitting / receiving unit 120 can be composed of a transmitter / receiver, RF circuit, baseband circuit, filter, phase shifter, measurement circuit, transmitting / receiving circuit, etc., as described based on common understanding in the art relating to this disclosure.

[0308] The transmitting / receiving unit 120 may be configured as an integrated transmitting / receiving unit, or it may be composed of a transmitting unit and a receiving unit. The transmitting unit may consist of a transmitting processing unit 1211 and an RF unit 122. The receiving unit may consist of a receiving processing unit 1212, an RF unit 122 and a measuring unit 123.

[0309] The transmitting and receiving antenna 130 can be composed of an antenna described based on common understanding in the art relating to this disclosure, such as an array antenna.

[0310] The transmitting / receiving unit 120 may transmit the downlink channel, synchronization signal, downlink reference signal, etc. The transmitting / receiving unit 120 may also receive the uplink channel, uplink reference signal, etc.

[0311] The transmitting / receiving unit 120 may use digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), or the like to form at least one of the transmitting beam and the receiving beam.

[0312] The transmitting / receiving unit 120 (transmission processing unit 1211) may perform processing on data and control information acquired from the control unit 110, for example, at the Packet Data Convergence Protocol (PDCP) layer, the Radio Link Control (RLC) layer (e.g., RLC retransmission control), and the Medium Access Control (MAC) layer (e.g., HARQ retransmission control), to generate a bit sequence to be transmitted.

[0313] The transmitting / receiving unit 120 (transmission processing unit 1211) may perform transmission processing on the bit sequence to be transmitted, such as channel coding (which may include error correction coding), modulation, mapping, filtering, discrete Fourier transform (DFT) processing (if necessary), inverse fast Fourier transform (IFFT) processing, precoding, and digital-to-analog conversion, and output a baseband signal.

[0314] The transmitting / receiving unit 120 (RF unit 122) may perform modulation, filtering, amplification, etc., of the baseband signal to the radio frequency band and transmit the signal in the radio frequency band via the transmitting / receiving antenna 130.

[0315] On the other hand, the transmitting / receiving unit 120 (RF unit 122) may perform amplification, filtering, demodulation to a baseband signal, etc., on the radio frequency band signal received by the transmitting / receiving antenna 130.

[0316] The transmitting / receiving unit 120 (receiving processing unit 1212) may apply reception processing such as analog-to-digital conversion, Fast Fourier Transform (FFT) processing, Inverse Discrete Fourier Transform (IDFT) processing (if necessary), filtering, demapping, demodulation, decoding (may include error correction decoding), MAC layer processing, RLC layer processing, and PDCP layer processing to the acquired baseband signal to acquire user data, etc.

[0317] The transmitting / receiving unit 120 (measurement unit 123) may perform measurements related to the received signal. For example, the measurement unit 123 may perform Radio Resource Management (RRM) measurements, Channel State Information (CSI) measurements, etc., based on the received signal. The measurement unit 123 may also measure received power (e.g., Reference Signal Received Power (RSRP)), reception quality (e.g., Reference Signal Received Quality (RSRQ), Signal to Interference plus Noise Ratio (SINR), Signal to Noise Ratio (SNR)), signal strength (e.g., Received Signal Strength Indicator (RSSI)), propagation path information (e.g., CSI), etc. The measurement results may be output to the control unit 110.

[0318] The transmission path interface 140 may send and receive signals (backhaul signaling) with devices included in the core network 30 (e.g., network nodes that provide NF), other base stations 10, etc., and may acquire and transmit user data (user plane data), control plane data, etc. for the user terminal 20.

[0319] In this disclosure, the transmitting and receiving units of the base station 10 may consist of at least one of a transmitting / receiving unit 120, a transmitting / receiving antenna 130, and a transmission path interface 140.

[0320] The base station 10 may be separated into three elements: a Radio Unit (RU), a Distributed Unit (DU), and a Central Unit (CU). For example, the RU may implement RF processing (digital beamforming, digital-to-analog conversion, analog beamforming, etc.) and lower-level physical layer functions (precoding, IFFT, FFT, etc.). The DU may implement higher-level physical layer functions (coding to resource element mapping, etc.), MAC layer functions, and RLC layer functions. The CU may implement PDCP layer, Service Data Adaptation Protocol (SDAP) layer, and RRC layer functions.

[0321] In this disclosure, base station 10 may include a single device that implements all the functions of RU, DU, and CU, or it may include multiple devices that each implement some of the functions of RU, DU, and CU and are connected to each other. In this disclosure, base station 10 may be interpreted as RU / DU / CU.

[0322] The LMF / network node in this disclosure may be implemented at a base station.

[0323] The control unit 110 may generate an output related to Radio Access Technology (RAT) independent positioning using an Artificial Intelligence (AI) model based on the input information. The transmitting / receiving unit 120 may transmit the output (first embodiment).

[0324] The input information may include at least one of power information, timing information, and other information separate from the power information and timing information (first embodiment).

[0325] The output may be an intermediate value for positioning at the terminal. The output may include at least one of uncertainty information, a quality indicator, measurement information at the terminal, and completeness information (first embodiment).

[0326] The control unit 110 may use at least one of the following for training the AI ​​model: information regarding the measurement at the terminal, information regarding the correct label, and assistance data for at least one of the measurement and / or label (second embodiment).

[0327] The control unit 210 may perform performance monitoring of the AI ​​model (third embodiment).

[0328] (User Terminal) Figure 15 shows an example of the configuration of a user terminal according to one embodiment. The user terminal 20 includes a control unit 210, a transmitting / receiving unit 220, and a transmitting / receiving antenna 230. Note that one or more of the control unit 210, the transmitting / receiving unit 220, and the transmitting / receiving antenna 230 may be provided.

[0329] In this example, the functional blocks of the characteristic parts of this embodiment are mainly shown, and it may be assumed that the user terminal 20 also has other functional blocks necessary for wireless communication. Some of the processing of each part described below may be omitted.

[0330] The control unit 210 controls the entire user terminal 20. The control unit 210 can be composed of a controller, control circuit, etc., as described based on common understanding in the technical field related to this disclosure.

[0331] The control unit 210 may control signal generation, mapping, etc. The control unit 210 may also control transmission and reception, measurement, etc., using the transmitting / receiving unit 220 and the transmitting / receiving antenna 230. The control unit 210 may generate data to be transmitted as signals, control information, sequences, etc., and transfer them to the transmitting / receiving unit 220.

[0332] The transmitting / receiving unit 220 may include a baseband unit 221, an RF unit 222, and a measurement unit 223. The baseband unit 221 may include a transmission processing unit 2211 and a reception processing unit 2212. The transmitting / receiving unit 220 can be composed of a transmitter / receiver, RF circuit, baseband circuit, filter, phase shifter, measurement circuit, transmitting / receiving circuit, etc., as described based on common understanding in the art relating to this disclosure.

[0333] The transmitting / receiving unit 220 may be configured as an integrated transmitting / receiving unit, or it may be composed of a transmitting unit and a receiving unit. The transmitting unit may consist of a transmitting processing unit 2211 and an RF unit 222. The receiving unit may consist of a receiving processing unit 2212, an RF unit 222 and a measuring unit 223.

[0334] The transmitting and receiving antenna 230 can be composed of an antenna described based on common understanding in the art relating to this disclosure, such as an array antenna.

[0335] The transmitting / receiving unit 220 may receive the downlink channel, synchronization signal, downlink reference signal, etc. The transmitting / receiving unit 220 may also transmit the uplink channel, uplink reference signal, etc.

[0336] The transmitting / receiving unit 220 may use digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), or the like to form at least one of the transmitting beam and the receiving beam.

[0337] The transmitting / receiving unit 220 (transmission processing unit 2211) may perform PDCP layer processing, RLC layer processing (e.g., RLC retransmission control), MAC layer processing (e.g., HARQ retransmission control), etc., on data and control information acquired from the control unit 210 to generate a bit sequence to be transmitted.

[0338] The transmitting / receiving unit 220 (transmission processing unit 2211) may perform transmission processing on the bit sequence to be transmitted, such as channel coding (which may include error correction coding), modulation, mapping, filtering, DFT processing (if necessary), IFFT processing, precoding, and digital-to-analog conversion, and output a baseband signal.

[0339] Whether or not to apply DFT processing may be based on the transform precoding settings. The transmitting / receiving unit 220 (transmission processing unit 2211) may perform DFT processing as part of the transmission process to transmit a channel (for example, PUSCH) using a DFT-s-OFDM waveform if transform precoding is enabled for that channel, or it may not perform DFT processing as part of the transmission process if transform precoding is not enabled for that channel.

[0340] The transmitting / receiving unit 220 (RF unit 222) may perform modulation, filtering, amplification, etc., of the baseband signal to the radio frequency band and transmit the signal in the radio frequency band via the transmitting / receiving antenna 230.

[0341] On the other hand, the transmitting / receiving unit 220 (RF unit 222) may perform amplification, filtering, demodulation to a baseband signal, etc., on the radio frequency band signal received by the transmitting / receiving antenna 230.

[0342] The transmitting / receiving unit 220 (receiving processing unit 2212) may apply reception processing such as analog-to-digital conversion, FFT processing, IDFT processing (if necessary), filtering, demapping, demodulation, decoding (may include error correction decoding), MAC layer processing, RLC layer processing, and PDCP layer processing to the acquired baseband signal to acquire user data, etc.

[0343] The transmitting / receiving unit 220 (measuring unit 223) may perform measurements related to the received signal. For example, the measuring unit 223 may perform RRM measurement, CSI measurement, etc., based on the received signal. The measuring unit 223 may also measure received power (e.g., RSRP), received quality (e.g., RSRQ, SINR, SNR), signal strength (e.g., RSSI), propagation path information (e.g., CSI), etc. The measurement results may be output to the control unit 210.

[0344] The measurement unit 223 may derive channel measurements for CSI calculation based on channel measurement resources. Channel measurement resources may be, for example, Non Zero Power (NZP) CSI-RS resources. The measurement unit 223 may also derive interference measurements for CSI calculation based on interference measurement resources. Interference measurement resources may be at least one of the following: NZP CSI-RS resources for interference measurement, CSI-Interference Measurement (IM) resources, etc. CSI-IM may also be called CSI-Interference Management (IM), and may be interpreted interchangeably with Zero Power (ZP) CSI-RS. In this disclosure, CSI-RS, NZP CSI-RS, ZP CSI-RS, CSI-IM, CSI-SSB, etc., may be interpreted interchangeably.

[0345] In this disclosure, the transmitting unit and receiving unit of the user terminal 20 may be composed of at least one of a transmitting / receiving unit 220 and a transmitting / receiving antenna 230.

[0346] (Hardware Configuration) The block diagram used in the description of the above embodiment shows functional units. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized using one device that is physically or logically coupled, or it may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wired or wireless connections). A functional block may also be realized by combining the above one device or the above multiple devices with software.

[0347] Here, functions include, but are not limited to, judgment, decision, determination, calculation, calculation, processing, derivation, investigation, exploration, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating (mapping), and assigning. For example, a functional block (configuration part) that enables transmission may be called a transmitting unit or transmitter. In all cases, as mentioned above, the method of implementation is not particularly limited.

[0348] For example, a base station, user terminal, etc. in one embodiment of the present disclosure may function as a computer that processes the wireless communication method of the present disclosure. Figure 16 is a diagram showing an example of the hardware configuration of a base station and user terminal according to one embodiment. The base station 10 and user terminal 20 described above may be physically configured as a computer device including a processor 1001, memory 1002, storage 1003, communication device 1004, input device 1005, output device 1006, bus 1007, etc.

[0349] In this disclosure, terms such as apparatus, circuit, device, section, and unit are interchangeable. 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 it may be configured without some of the devices.

[0350] For example, although only one processor 1001 is shown in the diagram, there may be multiple processors. Furthermore, the processing may be performed by one processor, or it may be performed by two or more processors simultaneously, sequentially, or by other means. Note that the processor 1001 may be implemented using one or more chips.

[0351] Each function in the base station 10 and the user terminal 20 is realized, for example, by loading predetermined software (programs) onto hardware such as the processor 1001 and memory 1002, which allows the processor 1001 to perform calculations and control communication via the communication device 1004, or control at least one of reading and writing data in the memory 1002 and storage 1003.

[0352] The processor 1001 controls the entire computer, for example, by running an operating system. The processor 1001 may be composed of a central processing unit (CPU) that includes interfaces with peripheral devices, control devices, arithmetic units, registers, etc. For example, at least a part of the control unit 110 (210) and the transmitting / receiving unit 120 (220) described above may be implemented by the processor 1001.

[0353] Furthermore, the processor 1001 reads programs (program code), software modules, data, etc., from at least one of the storage 1003 and the communication device 1004 into the memory 1002 and executes various processes accordingly. The program used is one that causes the computer to execute at least a part of the operations described in the above embodiment. For example, the control unit 110 (210) may be implemented by a control program stored in the memory 1002 and running on the processor 1001, and other functional blocks may be implemented similarly.

[0354] The memory 1002 is a computer-readable recording medium and may consist of at least one of the following: Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically EPROM (EEPROM), Random Access Memory (RAM), or other suitable storage medium. The memory 1002 may also be called a register, cache, or main memory. The memory 1002 can store executable programs (program code), software modules, etc., for carrying out a wireless communication method according to one embodiment of the present disclosure.

[0355] The storage 1003 is a computer-readable recording medium and may consist of at least one of the following: a flexible disk, a floppy disk, a magneto-optical disk (e.g., a Compact Disk (Compact Disc ROM (CD-ROM)), a Digital Use Disk, a Blu-ray (registered trademark) disk), a removable disk, a hard disk drive, a smart card, a flash memory device (e.g., a card, stick, key drive), a magnetic stripe, a database, a server, or other suitable storage medium. The storage 1003 may also be called an auxiliary storage device.

[0356] The communication device 1004 is hardware (transmitting / 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, network controller, network card, communication module, etc. The communication device 1004 may be configured to include, for example, a high-frequency switch, duplexer, filter, frequency synthesizer, etc., in order to implement at least one of frequency division duplex (FDD) and time division duplex (TDD). For example, the above-mentioned transmitting / receiving unit 120 (220), transmitting / receiving antenna 130 (230), etc., may be implemented by the communication device 1004. The transmitting / receiving unit 120 (220) may be implemented with physically or logically separated transmitting unit 120a (220a) and receiving unit 120b (220b).

[0357] The input device 1005 is an input device that accepts input from an external source (e.g., a keyboard, mouse, microphone, switch, button, sensor, etc.). The output device 1006 is an output device that outputs to an external source (e.g., a display, speaker, light-emitting diode (LED) lamp, etc.). The input device 1005 and the output device 1006 may be configured as an integrated unit (e.g., a touch panel).

[0358] Furthermore, each device, such as the processor 1001 and memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or different buses may be configured for each device.

[0359] Furthermore, the base station 10 and the user terminal 20 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a programmable logic device (PLD), and a field programmable gate array (FPGA), and some or all of each functional block may be implemented using such hardware. For example, the processor 1001 may be implemented using at least one of these hardware components.

[0360] Furthermore, devices included in the core network 30 (for example, network nodes that provide NF) may also be implemented using the functional block / hardware configuration described above.

[0361] (Variations) Terms used in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, channel, symbol and signal (signal or signaling) may be used interchangeably. Also, a signal may be a message. A reference signal may be abbreviated as RS and may be called a pilot, pilot signal, etc., depending on the applicable standard. Also, a component carrier (CC) may be called a cell, frequency carrier, carrier frequency, etc.

[0362] A wireless frame may consist of one or more periods (frames) in the time domain. Each of these periods (frames) constituting a wireless frame may be called a subframe. Furthermore, a subframe may consist of one or more slots in the time domain. A subframe may have a fixed time length (e.g., 1 ms) that is independent of numerology.

[0363] Here, the neurology may be communication parameters applied to at least one of the transmission and reception of a signal or channel. The neurology may be, for example, at least one of the following: subcarrier spacing (SCS), bandwidth, symbol length, cyclic prefix length, transmission time interval (TTI), number of symbols per TTI, radio frame configuration, specific filtering processes performed by the transceiver in the frequency domain, and specific windowing processes performed by the transceiver in the time domain.

[0364] A slot may consist of one or more symbols in the time domain (such as Orthogonal Frequency Division Multiplexing (OFDM) symbols or Single Carrier Frequency Division Multiple Access (SC-FDMA) symbols). Alternatively, a slot may be a time unit based on neurology.

[0365] A slot may include multiple minislots. Each minislot may consist of one or more symbols in the time domain. Minislots may also be called subslots. Minislots may consist of fewer symbols than a slot. A PDSCH (or PUSCH) transmitted in a time unit larger than a minislot may be called a PDSCH (PUSCH) mapping type A. A PDSCH (or PUSCH) transmitted using minislots may be called a PDSCH (PUSCH) mapping type B.

[0366] Wireless frames, subframes, slots, minislots, and symbols all represent units of time when transmitting a signal. Wireless frames, subframes, slots, minislots, and symbols may each be referred to by different names. Furthermore, the units of time such as frames, subframes, slots, minislots, and symbols in this disclosure may be interpreted as interchangeable.

[0367] For example, one subframe may be called a TTI, multiple consecutive subframes may be called a TTI, and one slot or one mini-slot may be called a TTI. In other words, at least one of a subframe and a TTI may be a subframe in existing LTE (1 ms), a period shorter than 1 ms (e.g., 1-13 symbols), or a period longer than 1 ms. Note that the unit representing a TTI may be called a slot, mini-slot, etc., instead of a subframe.

[0368] Here, TTI refers to, for example, the smallest time unit for scheduling in wireless communication. For example, in an LTE system, the base station schedules each user terminal to allocate wireless resources (such as the frequency bandwidth and transmission power available to each user terminal) in TTI units. However, the definition of TTI is not limited to this.

[0369] TTI may be a transmission time unit for channel-encoded data packets (transport blocks), code blocks, code words, etc., or it may be a processing unit for scheduling, link adaptation, etc. When a TTI is given, the actual time interval (e.g., number of symbols) in which the transport block, code block, code word, etc. are mapped may be shorter than the TTI.

[0370] Furthermore, if one slot or one mini-slot is referred to as a TTI, then one or more TTIs (i.e., one or more slots or one or more mini-slots) may constitute the minimum time unit for scheduling. In addition, the number of slots (number of mini-slots) that constitute this minimum time unit for scheduling may be controlled.

[0371] A TTI with a time length of 1 ms may be called a normal TTI, long TTI, normal subframe, long subframe, slot, etc. A TTI shorter than a normal TTI may be called a shortened TTI, short TTI, partial or fractional TTI, shortened subframe, short subframe, mini slot, sub slot, slot, etc.

[0372] Furthermore, long TTIs (e.g., normal TTIs, subframes, etc.) may be interpreted as TTIs with a time length exceeding 1 ms, and short TTIs (e.g., shortened TTIs, etc.) may be interpreted as TTIs with a TTI length less than that of a long TTI but 1 ms or more.

[0373] A Resource Block (RB) is a resource allocation unit in the time domain and frequency domain, and in the frequency domain, it may contain one or more consecutive subcarriers. The number of subcarriers in an RB may be the same regardless of the neurology, for example, 12. The number of subcarriers in an RB may be determined based on the neurology.

[0374] Furthermore, an RB may contain one or more symbols in the time domain and may have the length of one slot, one minislot, one subframe, or one TTI. One TTI, one subframe, etc., may each consist of one or more resource blocks.

[0375] One or more RBs may also be called Physical RBs (PRBs), Sub-Carrier Groups (SCGs), Resource Element Groups (REGs), PRB pairs, RB pairs, etc.

[0376] Furthermore, a resource block may consist of one or more resource elements (REs). For example, one RE may be a radio resource area comprising one subcarrier and one symbol.

[0377] A Bandwidth Part (BWP), also known as a partial bandwidth, may represent a subset of consecutive common resource blocks (RBs) for a given neurology in a given carrier. These common RBs may be identified by an index of the RBs relative to a common reference point of the carrier. The PRBs may be defined and numbered within a given BWP.

[0378] A BWP may include UL BWP (BWP for UL) and DL BWP (BWP for DL). One or more BWPs may be configured within a single carrier for a UE.

[0379] At least one of the configured BWPs may be active, and the UE does not need to assume that it will transmit or receive a predetermined signal / channel outside of the active BWP. In this disclosure, terms such as "cell" and "carrier" may be read as "BWP".

[0380] The structures of wireless frames, subframes, slots, minislots, and symbols described above are merely examples. For example, the number of subframes included in a wireless frame, the number of slots per subframe or wireless frame, the number of minislots included in a slot, the number of symbols and RBs included in a slot or minislot, the number of subcarriers included in an RB, and the number of symbols, symbol length, and cyclic prefix (CP) length within the TTI can be varied in various ways.

[0381] Furthermore, the information, parameters, etc., described in this disclosure may be expressed using absolute values, relative values ​​from a predetermined value, or corresponding other information. For example, wireless resources may be indicated by a predetermined index.

[0382] The names used for parameters and other elements in this disclosure are not restrictive in any way. Furthermore, mathematical formulas and other elements using these parameters may differ from those expressly disclosed in this disclosure. Various channels (PUCCH, PDCCH, etc.) and information elements can be identified by any suitable name, and therefore, the various names assigned to these various channels and information elements are not restrictive in any way.

[0383] The information, signals, etc. described in this disclosure may be represented using any of the various different techniques. For example, the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0384] Furthermore, information, signals, etc., can be output from upper layers to lower layers and from lower layers to upper layers, or to at least one of the two. Information, signals, etc., may also be input and output via multiple network nodes.

[0385] Input and output information and signals may be stored in a specific location (e.g., memory) or managed using a management table. Input and output information and signals may be overwritten, updated, or appended to. Output information and signals may be deleted. Input information and signals may be transmitted to other devices.

[0386] Any information described in this disclosure (e.g., variables, constants, parameters) may be communicated from any first device (e.g., UE / base station) to any second device (e.g., base station / UE) that indicates / specifies (or relates to) the value of such any information, even if not specifically stated in the embodiments described above.

[0387] Information notification is not limited to the embodiments described herein and may be carried out by other means. For example, information notification in this disclosure may be carried out by physical layer signaling (e.g., Downlink Control Information (DCI), Uplink Control Information (UCI)), higher layer signaling (e.g., Radio Resource Control (RRC) signaling, broadcast information (Master Information Block (MIB), System Information Block (SIB)), Medium Access Control (MAC) signaling), other signals, or a combination thereof.

[0388] Physical layer signaling may also be called Layer 1 / Layer 2 (L1 / L2) control information (L1 / L2 control signals), L1 control information (L1 control signals), etc. RRC signaling may also be called RRC messages, for example, RRC Connection Setup messages, RRC Connection Reconfiguration messages, etc. MAC signaling may also be communicated using, for example, MAC Control Elements (CEs).

[0389] Furthermore, notification of the specified information (for example, notification that "X is the case") is not limited to explicit notification, but may also be made implicitly (for example, by not notifying the specified information or by notifying other information).

[0390] The determination may be made by a value represented by one bit (0 or 1), by a boolean value represented as true or false, or by a numerical comparison (for example, a comparison with a predetermined value).

[0391] Software should be broadly interpreted to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, functions, and so on, whether they are called software, firmware, middleware, microcode, hardware description languages, or by any other name.

[0392] Furthermore, software, instructions, information, etc., may be transmitted and received via a transmission medium. For example, if software is transmitted from a website, server, or other remote source using at least one of wired technology (such as coaxial cable, fiber optic cable, twisted pair, or Digital Subscriber Line (DSL)) and wireless technology (such as infrared or microwave), then at least one of these wired and wireless technologies is included in the definition of a transmission medium.

[0393] The terms “system” and “network” as used in this disclosure may be used interchangeably. “Network” may also mean the equipment included in the network (e.g., base stations).

[0394] In this disclosure, terms such as “precoding,” “precoder,” “weight (precoding weight),” “quasi-co-location (QCL),” “transmission configuration indication state (TCI state),” “spatial relation,” “spatial domain filter,” “transmit power,” “phase rotation,” “antenna port,” “layer,” “number of layers,” “rank,” “resource,” “resource set,” “beam,” “beam width,” “beam angle,” “antenna,” “antenna element,” “panel,” “UE panel,” “transmitting entity,” and “receiving entity” may be used interchangeably.

[0395] In this disclosure, "antenna port" may be interpreted interchangeably with "antenna port for any signal / channel" (e.g., a Demodulation Reference Signal (DMRS) port). In this disclosure, "resource" may be interpreted interchangeably with "resource for any signal / channel" (e.g., a reference signal resource, an SRS resource, etc.). Resources may include time / frequency / code / spatial / power resources. Furthermore, a spatial domain transmit filter may include at least one of a spatial domain transmit filter and a spatial domain receive filter.

[0396] The above group may include, for example, at least one of the following: a spatial relationship group, a code division multiplexing (CDM) group, a reference signal (RS) group, a control resource set (CORESET) group, a PUCCH group, an antenna port group (e.g., a DMRS port group), a layer group, a resource group, a beam group, an antenna group, or a panel group.

[0397] Furthermore, in this disclosure, terms such as beam, SRS Resource Indicator (SRI), CORESET, CORESET pool, PDSCH, PUSCH, Codeword (CW), Transport Block (TB), and RS may be interpreted interchangeably.

[0398] Furthermore, in this disclosure, TCI state, downlink TCI state (DL TCI state), uplink TCI state (UL TCI state), unified TCI state, common TCI state, joint TCI state, etc., may be interpreted interchangeably.

[0399] Furthermore, in this disclosure, terms such as "QCL," "QCL assumption," "QCL relationship," "QCL type information," "QCL property / properties," "specific QCL type (e.g., Type A, Type D) properties," and "specific QCL type (e.g., Type A, Type D)" may be interpreted interchangeably.

[0400] In this disclosure, terms such as index, identifier (ID), indicator, indication, and resource ID may be interpreted interchangeably. In this disclosure, terms such as sequence, list, set, group, cluster, subset may be interpreted interchangeably.

[0401] Furthermore, the spatial relationship information Identifier (ID) (TCI state ID) and spatial relationship information (TCI state) may be interpreted as mutually exclusive. "Spatial relationship information (TCI state)" may be interpreted as mutually exclusive as "a set of spatial relationship information (TCI state)," "one or more pieces of spatial relationship information," etc. TCI state and TCI may be interpreted as mutually exclusive. Spatial relationship information and spatial relationship may be interpreted as mutually exclusive.

[0402] In this 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” may be used interchangeably. Base stations may also be referred to by terms such as macrocell, small cell, femtocell, and picocell.

[0403] A base station may house one or more (e.g., three) cells. If a base station houses multiple cells, the entire coverage area of ​​the base station may be divided into several smaller areas, each of which may also be provided with communication services by a base station subsystem (e.g., a small indoor base station (Remote Radio Head (RRH))). The terms “cell” or “sector” refer to part or all of the coverage area of ​​at least one of the base station and / or base station subsystems that provide communication services in that coverage.

[0404] In this disclosure, the transmission of information by a base station to a terminal may be interpreted as the base station instructing the terminal to perform a control / operation based on said information.

[0405] In this disclosure, terms such as "Mobile Station (MS)," "user terminal," "User Equipment (UE)," and "terminal" may be used interchangeably.

[0406] A mobile station may also be called a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other appropriate term.

[0407] At least one of the base station and the mobile station may be called a transmitting device, a receiving device, a wireless communication device, etc. At least one of the base station and the mobile station may also be a device mounted on a moving object, the moving object itself, etc.

[0408] The term "mobile object" refers to any movable object, regardless of its speed, and naturally includes cases where the mobile object is stationary. Examples of such mobile objects include, but are not limited to, vehicles, transport vehicles, automobiles, motorcycles, bicycles, connected cars, excavators, bulldozers, wheel loaders, dump trucks, forklifts, trains, buses, handcarts, rickshaws, ships and other watercraft, airplanes, rockets, satellites, drones, multicopters, quadcopters, balloons, and items carried on them. Furthermore, such mobile objects may be autonomously driven objects operating based on operational commands.

[0409] The mobile entity may be a vehicle (e.g., a car, an airplane), an unmanned mobile entity (e.g., a drone, an autonomous vehicle), or a robot (manned or unmanned). At least one of the base station and the mobile station may be a device that does not necessarily move during communication operations. For example, at least one of the base station and the mobile station may be an Internet of Things (IoT) device such as a sensor.

[0410] Figure 17 shows an example of a vehicle according to one embodiment. The vehicle 40 includes a drive unit 41, a steering unit 42, an accelerator pedal 43, a brake pedal 44, a shift lever 45, left and right front wheels 46, left and right rear wheels 47, an axle 48, an electronic control unit 49, various sensors (including a current sensor 50, a rotation speed sensor 51, a pneumatic 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.

[0411] The drive unit 41 consists of, for example, at least one of an engine, a motor, or an engine-motor hybrid. The steering unit 42 includes at least a steering wheel (also called a handle) and is configured to steer at least one of the front wheels 46 and the rear wheels 47 based on the operation of the steering wheel operated by the user.

[0412] The electronic control unit 49 consists of a microprocessor 61, memory (ROM, RAM) 62, and communication ports (e.g., input / output (IO) ports) 63. Signals from various sensors 50-58 installed in the vehicle are input to the electronic control unit 49. The electronic control unit 49 may also be called an Electronic Control Unit (ECU).

[0413] Signals from various sensors 50-58 include current signals from current sensor 50 for sensing motor current, rotational speed signals of front wheels 46 / rear wheels 47 acquired by rotational speed sensor 51, air pressure signals of front wheels 46 / rear wheels 47 acquired by air pressure sensor 52, vehicle speed signals acquired by vehicle speed sensor 53, acceleration signals acquired by acceleration sensor 54, accelerator pedal depression amount signals acquired by accelerator pedal sensor 55, brake pedal depression amount signals acquired by brake pedal sensor 56, operation signals of shift lever 45 acquired by shift lever sensor 57, and detection signals acquired by object detection sensor 58 for detecting obstacles, vehicles, pedestrians, etc.

[0414] The information service unit 59 consists of various devices for providing (outputting) various types of information such as driving information, traffic information, and entertainment information, including a car navigation system, audio system, speakers, display, television, and radio, and one or more ECUs that control these devices. The information service unit 59 uses information acquired from external devices via a communication module 60 or the like to provide various types of information / services (for example, multimedia information / multimedia services) to the occupants of the vehicle 40.

[0415] The information service unit 59 may include input devices that accept input from the outside (e.g., keyboard, mouse, microphone, switch, button, sensor, touch panel, etc.) or output devices that perform output to the outside (e.g., display, speaker, LED lamp, touch panel, etc.).

[0416] The driver assistance system unit 64 consists of various devices that provide functions to prevent accidents or reduce the driver's workload, such as millimeter-wave radar, Light Detection and Ranging (LiDAR), cameras, positioning locators (e.g., Global Navigation Satellite System (GNSS)), map information (e.g., High Definition (HD) maps, Autonomous Vehicle (AV) maps), gyro systems (e.g., Inertial Measurement Unit (IMU), Inertial Navigation System (INS)), artificial intelligence (AI) chips, and AI processors, as well as one or more ECUs that control these devices. The driver assistance system unit 64 also transmits and receives various information via the communication module 60 to realize driver assistance functions or autonomous driving functions.

[0417] The communication module 60 can communicate with the microprocessor 61 and components of the vehicle 40 via the communication port 63. For example, the communication module 60 sends and receives data (information) via the communication port 63 to the drive unit 41, steering unit 42, accelerator pedal 43, brake pedal 44, shift lever 45, left and right front wheels 46, left and right rear wheels 47, axle 48, the microprocessor 61 and memory (ROM, RAM) 62 in the electronic control unit 49, and various sensors 50-58 provided in the vehicle 40.

[0418] The communication module 60 is a communication device that can be controlled by the microprocessor 61 of the electronic control unit 49 and can communicate with external devices. For example, it can send and receive various types of information to and from external devices via wireless communication. The communication module 60 may be located either inside or outside the electronic control unit 49. The external device may be, for example, the base station 10 or the user terminal 20 described above. Alternatively, the communication module 60 may be, for example, at least one of the base station 10 and the user terminal 20 (it may function as at least one of the base station 10 and the user terminal 20).

[0419] The communication module 60 may transmit at least one of the following to an external device via wireless communication: signals from the various sensors 50-58 input to the electronic control unit 49, information obtained based on said signals, and information based on input from an external source (user) obtained via the information service unit 59. The electronic control unit 49, the various sensors 50-58, the information service unit 59, etc., may also be called input units that accept input. For example, the PUSCH transmitted by the communication module 60 may include the information based on the above input.

[0420] The communication module 60 receives various information (traffic information, signal information, inter-vehicle information, etc.) transmitted from an external device and displays it on the information service unit 59 installed in the vehicle. The information service unit 59 may also be called an output unit, which outputs information (for example, it outputs information to devices such as displays and speakers based on the PDSCH (or data / information decoded from the PDSCH) received by the communication module 60).

[0421] Furthermore, the communication module 60 stores various information received from external devices in a memory 62 that can be used by the microprocessor 61. Based on the information stored in the memory 62, the microprocessor 61 may control the drive unit 41, steering unit 42, accelerator pedal 43, brake pedal 44, shift lever 45, left and right front wheels 46, left and right rear wheels 47, axle 48, various sensors 50-58, etc., which are provided in the vehicle 40.

[0422] Furthermore, the term "base station" in this disclosure may be interpreted as "user terminal." For example, the various aspects / embodiments of this disclosure may be applied to a configuration in which communication between a base station and a user terminal is replaced with communication between multiple user terminals (which may be called, for example, Device-to-Device (D2D), Vehicle-to-Everything (V2X)). In this case, the user terminal 20 may have the functions of the base station 10 described above. Also, terms such as "uplink" and "downlink" may be interpreted as terms corresponding to terminal-to-terminal communication (for example, "sidelink"). For example, uplink channel, downlink channel, etc., may be interpreted as sidelink channel.

[0423] Similarly, the term "user terminal" in this disclosure may be replaced with "base station." In this case, the base station 10 may be configured to have the same functions as the user terminal 20 described above.

[0424] In this disclosure, operations performed by a base station may, in some cases, be performed by its upper node. In a network including one or more network nodes having base stations, it is clear that various operations performed for communication with terminals may be performed by the base station, one or more network nodes other than the base station (for example, a Mobility Management Entity (MME), a Serving Gateway (S-GW), etc., but not limited to these), or a combination thereof.

[0425] Each aspect / embodiment described in this disclosure may be used individually, in combination, or switched between as needed during execution. Furthermore, the processing procedures, sequences, flowcharts, etc., of each aspect / embodiment described in this disclosure may be rearranged in order, provided they are consistent. For example, the methods described in this disclosure present various step elements using exemplary order and are not limited to the specific order presented.

[0426] Each aspect / embodiment described in this disclosure is Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 6th generation mobile communication system (6G), xth generation mobile communication system (xG (where x is, for example, an integer or 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®), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi®), IEEE 802.16 (WiMAX®), IEEE 802.20, systems utilizing Ultra-WideBand (UWB), Bluetooth®, or other appropriate wireless communication methods, and next-generation systems extended, modified, created, or defined based thereon may also be applied. Furthermore, multiple systems may be applied in combination (for example, a combination of LTE or LTE-A and 5G).

[0427] In this disclosure, the phrase "based on" does not mean "based solely on" unless otherwise specified. In other words, the phrase "based on" means both "based solely on" and "based at least on."

[0428] Any reference to elements using the designations “first,” “second,” etc., as used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Accordingly, the references to the first and second elements do not imply that only two elements may be employed or that the first element must precede the second element in any way.

[0429] The term “determining” as used in this disclosure may encompass a wide variety of actions. For example, “determining” may be considered to mean judging, calculating, computing, processing, deriving, investigating, looking up, searching, or inquiring (e.g., searching in tables, databases, or other data structures), ascertaining, etc.

[0430] Furthermore, "judgment (decision)" may be considered as "judging (deciding)" things like receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory).

[0431] Furthermore, “judgment (decision)” may be considered as “judgment (decision)” of resolving, selecting, choosing, establishing, comparing, etc. In other words, “judgment (decision)” may be considered as “judgment (decision)” of some action. In this disclosure, “judgment (decision)” may be interpreted as mutually interchangeable with the actions described above.

[0432] Furthermore, in this disclosure, “determine / determining” may be interpreted as “assume / assuming,” “expect / expecting,” or “consider / considering.” In addition, in this disclosure, “not expecting to do…” may be interpreted as “expecting not to do….”

[0433] In this disclosure, "expect" may be rephrased as "be expected." For example, "expect(s) ..." (where "..." may be expressed as a that clause, an infinitive, etc.) may be rephrased as "be expected ..." or "do (the verb without "to" if "..." is an infinitive)." Similarly, "does not expect ..." may be rephrased as "be not expected ..." or "do not (the verb without "to" if "..." is an infinitive)." Furthermore, "An apparatus A is not expected ..." may be rephrased as "An apparatus B other than apparatus A does not expect ... from apparatus A" (for example, if apparatus A is a UE, apparatus B may be a base station).

[0434] The term "maximum transmit power" as used in this disclosure may mean the maximum transmit power, the nominal UE maximum transmit power, or the rated UE maximum transmit power.

[0435] As used in this disclosure, the terms “connected,” “coupled,” and any variations thereof mean any direct or indirect connection or coupling between two or more elements, and may include one or more intermediate elements between two elements that are “connected” or “coupled” with each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, “connection” may be replaced with “access.”

[0436] In this disclosure, when two elements are connected, they can be considered to be "connected" or "coupled" to each other using one or more wires, cables, printed electrical connections, etc., and, in some non-exclusive and non-exclusive examples, electromagnetic energy having wavelengths in the radio frequency domain, microwave domain, and optical (both visible and invisible) domain.

[0437] In this disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "combine" may be interpreted similarly to "different."

[0438] Where the terms “include,” “including,” and variations thereof are used in this disclosure, these terms are intended to be inclusive, as is the term “comprising.” Furthermore, the term “or” as used in this disclosure is not intended to mean exclusive OR.

[0439] In this disclosure, if articles are added by translation, such as a, an, and the in English, this disclosure may include the fact that the noun following these articles is plural.

[0440] In this disclosure, "less than or equal to," "less than," "greater than or equal to," "more than," and "equal to" may be interpreted interchangeably. In addition, in this disclosure, words meaning "good," "bad," "big," "small," "high," "low," "early," "slow," "wide," and "narrow" may be interpreted interchangeably, not limited to the positive, comparative, and superlative degrees. In addition, in this disclosure, words meaning "good," "bad," "big," "small," "high," "low," "early," "slow," "wide," and "narrow" may be interpreted interchangeably, not limited to the positive, comparative, and superlative degrees, by adding "i-th" (where i is any integer) to the expression (for example, "highest" may be interpreted interchangeably with "i-th highest").

[0441] In this disclosure, "of," "for," "regarding," "related to," and "associated with" may be interpreted as being interchangeable.

[0442] In the present disclosure, expressions such as "when A, B", "if A, then B", "B upon A", "B in response to A", "B based on A", "B during / while A", "B before A", "B at (the same time as) / on A", "B after A", "B since A", "B until A" may be read as each other. Here, A, B, etc. may be appropriately replaced with suitable expressions such as nouns, gerunds, normal sentences, etc. according to the context. The time difference between A and B may be approximately 0 (immediately before or after). Also, a time offset may be applied to the time when A occurs. For example, "A" may be read as "before / after the time offset when A occurs". The time offset (e.g., one or more symbols / slots) may be predefined or may be specified by the UE based on the notified information.

[0443] In the present disclosure, timing, time, hour, time instance, any time unit (e.g., slot, sub-slot, symbol, sub-frame), period, occasion, resource, etc. may be read as each other.

[0444] As described above, the invention according to the present disclosure has been described in detail. However, it is clear to those skilled in the art that the invention according to the present disclosure is not limited to the embodiments described in the present disclosure. The description of the present disclosure is for illustrative purposes and does not bring any limiting meaning to the invention according to the present disclosure.

Claims

A control unit that generates output regarding positioning that is independent of Radio Access Technology (RAT) and uses an Artificial Intelligence (AI) model based on input information, A network node having a transmitting unit that transmits the output. 　 The network node according to claim 1, wherein the input information includes at least one of power information, timing information, and other information separate from the power information and the timing information. 　 The output is an intermediate value for positioning at the terminal, The network node according to claim 1, wherein the output includes at least one of uncertainty information, a quality indicator, measurement information at the terminal, and integrity information. 　 The network node according to claim 1, wherein the control unit uses at least one of the following for training the AI ​​model: information regarding measurements at the terminal, information regarding correct labels, and assistance data of at least one of the measurements and labels. 　 The control unit performs performance monitoring of the AI ​​model, as described in claim 1. 　 A step of generating output regarding positioning that is independent of Radio Access Technology (RAT) using an Artificial Intelligence (AI) model, based on input information, and A wireless communication method for a network node, comprising the step of transmitting the output.

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