Method performed by terminal or network in wireless communication system, and device therefor

Abstract

A method performed by a first device according to at least one of the embodiments disclosed in the present specification may comprise: receiving, from a second device, a configuration for data to be reported by the first device; performing a measurement to acquire the data on the basis of the configuration; and reporting, to the second device, the data acquired through the measurement, wherein the data acquired through the measurement is stored in a buffer of the first device and, on the basis of the buffer usage having reached or exceeded a threshold, the first device stops the measurement and reports the data stored in the buffer to the second device.

Description

Method performed by a terminal or network in a wireless communication system and device therefor

[0001] The present disclosure relates to a wireless communication system, and more particularly, to a method and device for transmitting or receiving an uplink / downlink wireless signal by a terminal or a network in a wireless communication system.

[0002] Wireless communication systems are widely deployed to provide various types of communication services, such as voice and data. Typically, wireless communication systems are multiple access systems that support communication with multiple users by sharing available system resources (e.g., bandwidth, transmission power). Examples of multiple access systems include code division multiple access (CDMA), frequency division multiple access (FDMA), time division multiple access (TDMA), orthogonal frequency division multiple access (OFDMA), and single-carrier frequency division multiple access (SC-FDMA).

[0003] Wireless communications leveraging AI / ML are currently being discussed in 3GPP standardization. Representative use cases include CSI compression or accuracy enhancement through AI / ML-based CSI feedback, AI / ML-based beam management, and AI / ML-based positioning. AI / ML is expected to be introduced starting with 5G Rel.19, and future 6G deployments are expected to support AI / ML in various forms across wireless communications.

[0004] Meanwhile, data collection for AI / ML functionality / model training / re-training / update / inference has a significant impact on the performance of AI / ML models, so a method for collecting good quality data is needed.

[0005] The technical task of this disclosure is to provide a method and device for efficiently performing wireless signal transmission and reception processes. For example, a method and device for more accurately and efficiently collecting data for AI / ML functionality / models may be provided.

[0006] In addition to the technical challenges described above, other technical challenges can be inferred from the description below.

[0007] According to one aspect of the present disclosure, a method performed by a first device includes receiving, from a second device, a setting for data to be reported by the first device; performing a measurement for obtaining the data based on the setting; and reporting data obtained through the measurement to the second device, wherein the data obtained through the measurement is stored in a buffer of the first device, and the first device can stop the measurement based on the buffer being used more than a threshold and report the data stored in the buffer to the second device.

[0008] The above data may be data for training, retraining, or updating an AI / ML (Artificial intelligence / machine learning) model.

[0009] The first device may request resource allocation from the second device based on whether the buffer has been used beyond a threshold. In response to the request, the data may be reported through the allocated resources.

[0010] The above settings may include criteria related to data quality.

[0011] The first device can select data samples that satisfy the above criteria from among all data samples included in the data acquired through the measurement and report the data samples to the second device.

[0012] The report of the above data may include quality assessment information of the selected data samples.

[0013] The report of the above data may include a ratio between the total data samples and the selected data samples.

[0014] The above settings may include information about a reference data set to be referenced by the first device for measuring and reporting the data.

[0015] Information about the above reference data set may be an ID of the above reference data set.

[0016] The above measurement may include at least one of a channel measurement for channel state information (CSI), a reference signal received power (RSRP) measurement for beam management, or a positioning reference signal (PRS) measurement for positioning.

[0017] The above first device may be a terminal.

[0018] The second device may be a base station, a network node that collects the data for training an AI / ML (Artificial intelligence / machine learning) model, or a network node that trains the AI / ML model based on the data.

[0019] According to another aspect of the present disclosure, a non-transitory computer-readable recording medium having recorded thereon a program for performing the method described above may be provided.

[0020] According to another aspect of the present disclosure, a first device comprises at least one processor; and at least one memory configured to store instructions that, when executed by the at least one processor, cause the at least one processor to perform operations, wherein the operations of the at least one processor include: a method performed by the first device, comprising: receiving a setting for data to be reported by the first device from a second device; performing a measurement for obtaining the data based on the setting; and reporting the data obtained through the measurement to the second device, wherein the data obtained through the measurement is stored in a buffer configured in the at least one memory, and the first device can stop the measurement based on the buffer being used more than a threshold and report the data stored in the buffer to the second device.

[0021] The first device may be a terminal including a transceiver or a processing device configured to control the terminal.

[0022] According to another aspect of the present disclosure, a method performed by a second device comprises transmitting, to a first device, a setting for data reporting of the first device; and receiving the data report based on a result of a measurement performed by the first device, wherein the second device can request the first device to configure the data report by selecting data samples that satisfy criteria related to data quality from among all data samples included in the result of the measurement through the setting.

[0023] According to another aspect of the present disclosure, a second device comprises at least one processor; and at least one memory configured to store instructions that, when executed by the at least one processor, cause the at least one processor to perform operations, wherein the operations of the at least one processor include transmitting a setting for data reporting of the first device to the first device; and receiving the data report based on a result of a measurement performed in the first device, wherein the second device can request the first device to configure the data report by selecting data samples that satisfy a criterion related to data quality from among all data samples included in the result of the measurement through the setting.

[0024] According to the present disclosure, wireless signal transmission and reception can be efficiently performed in a wireless communication system. According to one embodiment, a device performing data collection explicitly signals configuration information regarding what data should be measured / reported to a data reporting device, thereby enabling the collection of high-quality data. As a result, training, retraining, or updating of AI / ML models can be performed more accurately and efficiently.

[0025] In addition to the technical effects described above, other technical effects can be inferred from the description below.

[0026] Figure 1 illustrates physical channels used in a 3GPP system, which is an example of a wireless communication system, and a general signal transmission method using the channels.

[0027] Figure 2 illustrates the structure of a radio frame.

[0028] Figure 3 illustrates a resource grid of slots.

[0029] Figure 4 illustrates an example of physical channels being mapped within a slot.

[0030] Figure 5 illustrates the PDSCH and ACK / NACK transmission process.

[0031] Figure 6 illustrates a PUSCH transmission process.

[0032] Figure 7 shows an example of a CSI-related procedure.

[0033] Figure 8 is a diagram to explain the concept of AI / ML / Deep learning.

[0034] Figures 9 to 12 illustrate various AI / ML models of deep learning.

[0035] Figure 13 is a diagram illustrating segmentation AI inference.

[0036] Figure 14 is a diagram illustrating a framework for 3GPP RAN Intelligence.

[0037] Figures 15 to 17 illustrate AI Model Training and Inference environments.

[0038] Figure 18 illustrates a communication procedure between a first node (e.g., terminal) and a second node (e.g., base station) to which an AI / ML model is applied.

[0039] FIG. 19 is a diagram for explaining the operation of a terminal and a network according to one embodiment.

[0040] FIG. 20 illustrates a flow of a method performed in a first device according to one embodiment.

[0041] FIG. 21 illustrates a flow of a method performed in a second device according to one embodiment.

[0042] Figures 22 to 25 illustrate a communication system (1) and a wireless device applicable to the present disclosure.

[0043] The following technologies can be used in various wireless access systems, such as CDMA (code division multiple access), FDMA (frequency division multiple access), TDMA (time division multiple access), OFDMA (orthogonal frequency division multiple access), and SC-FDMA (single carrier frequency division multiple access). CDMA can be implemented with radio technologies such as UTRA (Universal Terrestrial Radio Access) or CDMA2000. TDMA can be implemented with radio technologies such as GSM (Global System for Mobile communications) / GPRS (General Packet Radio Service) / EDGE (Enhanced Data Rates for GSM Evolution). OFDMA can be implemented with radio technologies such as IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802-20, and E-UTRA (Evolved UTRA). UTRA is a part of UMTS (Universal Mobile Telecommunications System). 3GPP (3rd Generation Partnership Project) LTE (long term evolution) is part of E-UMTS (Evolved UMTS) that uses E-UTRA, and LTE-A (Advanced) is an evolved version of 3GPP LTE. 3GPP NR (New Radio or New Radio Access Technology) is an evolved version of 3GPP LTE / LTE-A.

[0044] As more and more communication devices demand greater communication capacity, the need for improved mobile broadband communications compared to existing Radio Access Technology (RAT) is emerging. Furthermore, massive Machine Type Communications (MTC), which connects multiple devices and objects to provide diverse services anytime, anywhere, is also a key issue to be considered in next-generation communications. Furthermore, communication system design that considers reliability and latency-sensitive services / terminals is being discussed. Accordingly, the introduction of next-generation RATs that consider enhanced Mobile BroadBand Communication (eMBB), massive MTC, and Ultra-Reliable and Low Latency Communication (URLLC) is being discussed. For convenience, this disclosure will refer to these technologies as NR (New Radio or New RAT).

[0045] For clarity of explanation, the description will focus on 3GPP NR, but the technical idea of ​​the present disclosure is not limited thereto.

[0046] In this specification, the expression "setting" can be replaced with the expression "configure / configuration", and the two can be used interchangeably. In addition, conditional expressions (e.g., "if", "in a case", or "when", etc.) can be replaced with the expression "based on that ~~" or "in a state / status". In addition, the operation of the terminal / base station or the SW / HW configuration according to the satisfaction of the condition can be inferred / understood. In addition, if the process of the receiving (or transmitting) side can be inferred / understood from the process of the transmitting (or receiving) side in signal transmission / reception between wireless communication devices (e.g., base stations, terminals), the description thereof can be omitted. For example, signal determination / generation / encoding / transmission, etc. of the transmitting side can be understood as signal monitoring reception / decoding / determination, etc. of the receiving side. In addition, the expression that the terminal performs (or does not perform) a specific operation can also be interpreted as meaning that the base station operates while expecting / assuming (or expecting / assuming that the terminal does not perform) the specific operation. In addition, the expression that the base station performs (or does not perform) a specific operation can also be interpreted as meaning that the terminal operates while expecting / assuming (or expecting / assuming that the base station does not perform) the specific operation. In addition, the division and index of each section, embodiment, example, option, method, plan, etc. in the following description are for the convenience of explanation and should not be interpreted as meaning that each constitutes an independent invention or that each must be implemented only individually. In addition, in describing each section, embodiment, example, option, method, plan, etc., if there is no explicitly conflicting / opposing description, it can be inferred / interpreted that at least some of them can be combined and implemented together, or at least some can be implemented with the omission of each.

[0047] In a wireless communication system, a terminal receives information from a base station via the downlink (DL) and transmits it to the base station via the uplink (UL). The information transmitted and received between the base station and the terminal includes data and various control information, and various physical channels exist depending on the type and purpose of the information being transmitted and received.

[0048] Figure 1 is a drawing for explaining physical channels used in a 3GPP NR system and a general signal transmission method using them.

[0049] When a terminal is powered on again from a powered-off state or enters a new cell, it performs an initial cell search operation, such as synchronizing with the base station, in step S101. To this end, the terminal receives a Synchronization Signal Block (SSB) from the base station. The SSB includes a Primary Synchronization Signal (PSS), a Secondary Synchronization Signal (SSS), and a Physical Broadcast Channel (PBCH). Based on the PSS / SSS, the terminal synchronizes with the base station and obtains information such as a cell ID (cell identity). In addition, the terminal can obtain broadcast information within the cell based on the PBCH. Meanwhile, the terminal can check the downlink channel status by receiving a Downlink Reference Signal (DL RS) during the initial cell search phase.

[0050] After completing the initial cell search, the terminal can obtain more specific system information by receiving a physical downlink control channel (PDCCH) and a physical downlink shared channel (PDSCH) based on the physical downlink control channel information in step S102.

[0051] Thereafter, the terminal may perform a random access procedure such as steps S103 to S106 to complete connection to the base station. To this end, the terminal may transmit a preamble through a physical random access channel (PRACH) (S103) and receive a response message to the preamble through a physical downlink control channel and a corresponding physical downlink shared channel (S104). In the case of contention-based random access, a contention resolution procedure such as transmission of an additional physical random access channel (S105) and reception of a physical downlink control channel and a corresponding physical downlink shared channel (S106) may be performed.

[0052] The terminal that has performed the procedure as described above can then perform the general uplink / downlink signal transmission procedure, such as receiving a physical downlink control channel / physical downlink shared channel (S107) and transmitting a physical uplink shared channel (PUSCH) / physical uplink control channel (PUCCH) (S108). The control information that the terminal transmits to the base station is collectively referred to as uplink control information (UCI). UCI includes HARQ ACK / NACK (Hybrid Automatic Repeat and request Acknowledgement / Negative-ACK), SR (Scheduling Request), CSI (Channel State Information), etc. CSI includes CQI (Channel Quality Indicator), PMI (Precoding Matrix Indicator), RI (Rank Indication), etc. UCI is generally transmitted through PUCCH, but can be transmitted through PUSCH when control information and traffic data must be transmitted simultaneously. Additionally, UCI can be transmitted aperiodically via PUSCH upon request / instruction from the network.

[0053] Figure 2 illustrates the structure of a radio frame. In NR, uplink and downlink transmissions are organized into frames. Each radio frame is 10 ms long and is divided into two 5 ms half-frames (HF). Each half-frame is divided into five 1 ms sub-frames (SF). A sub-frame is divided into one or more slots, and the number of slots within a sub-frame depends on the subcarrier spacing (SCS). Each slot contains 12 or 14 OFDM (Orthogonal Frequency Division Multiplexing) symbols, depending on the cyclic prefix (CP). When a normal CP is used, each slot contains 14 OFDM symbols. When an extended CP is used, each slot contains 12 OFDM symbols.

[0054] Table 1 illustrates that when CP is normally used, the number of symbols per slot, the number of slots per frame, and the number of slots per subframe vary depending on the SCS.

[0055] SCS (15*2^u)N slot symb N frame,u slot N subframe,u slot 15KHz (u=0)1410130KHz (u=1)1420260KHz (u=2)14404120KHz (u=3)14808240KHz (u=4)1416016

[0056] * N slot symb : Number of symbols in the slot

[0057] * N frame,u slot : Number of slots in the frame

[0058] * N subframe,u slot : Number of slots in a subframe

[0059] Table 2 illustrates that when extended CP is used, the number of symbols per slot, the number of slots per frame, and the number of slots per subframe vary depending on the SCS.

[0060] SCS (15*2^u)N slot symb N frame,u slot N subframe,u slot 60KHz (u=2)12404

[0061] The structure of the frame is only an example, and the number of subframes, number of slots, and number of symbols in the frame can be varied.

[0062] In an NR system, OFDM numerology (e.g., SCS) may be set differently between multiple cells that are merged into a single terminal. Accordingly, the (absolute time) interval of a time resource (e.g., SF, slot, or TTI) (conveniently referred to as TU (Time Unit)) consisting of the same number of symbols may be set differently between the merged cells. Here, the symbol may include an OFDM symbol (or CP-OFDM symbol), an SC-FDMA symbol (or Discrete Fourier Transform-spread-OFDM, DFT-s-OFDM symbol).

[0063] Figure 3 illustrates a resource grid of a slot. A slot contains multiple symbols in the time domain. For example, in the case of a regular CP, one slot contains 14 symbols, but in the case of an extended CP, one slot contains 12 symbols. A carrier contains multiple subcarriers in the frequency domain. A Resource Block (RB) is defined as multiple (e.g., 12) consecutive subcarriers in the frequency domain. A Bandwidth Part (BWP) is defined as multiple consecutive Physical RBs (PRBs) in the frequency domain and can correspond to a single numerology (e.g., SCS, CP length, etc.). A carrier can contain up to N (e.g., 5) BWPs. Data communication is performed through activated BWPs, and only one BWP can be activated for a single terminal. Each element in the resource grid is referred to as a Resource Element (RE), to which one complex symbol can be mapped.

[0064] Figure 4 illustrates an example of how physical channels are mapped within a slot. A PDCCH can be transmitted in the DL control region, and a PDSCH can be transmitted in the DL data region. A PUCCH can be transmitted in the UL control region, and a PUSCH can be transmitted in the UL data region. GP provides a time gap between the base station and the terminal when switching from transmission mode to reception mode or from reception mode to transmission mode. Some symbols within a subframe at the time of transition from DL to UL can be set as GP.

[0065] Below, each physical channel is described in more detail.

[0066] The PDCCH carries Downlink Control Information (DCI). For example, the PCCCH (i.e., DCI) carries the transmission format and resource allocation of the downlink shared channel (DL-SCH), resource allocation information for the uplink shared channel (UL-SCH), paging information for the paging channel (PCH), system information on the DL-SCH, resource allocation information for upper layer control messages such as random access responses transmitted on the PDSCH, transmission power control commands, activation / deactivation of Configured Scheduling (CS), etc. The DCI includes a cyclic redundancy check (CRC), which is masked / scrambled with various identifiers (e.g., Radio Network Temporary Identifier, RNTI) depending on the owner or usage of the PDCCH. For example, if the PDCCH is for a specific terminal, the CRC is masked with a terminal identifier (e.g., Cell-RNTI, C-RNTI). If the PDCCH is for paging, the CRC is masked with the Paging-RNTI (P-RNTI). If the PDCCH is for system information (e.g., a System Information Block, SIB), the CRC is masked with the System Information RNTI (SI-RNTI). If the PDCCH is for a random access response, the CRC is masked with the Random Access-RNTI (RA-RNTI).

[0067] The PDCCH consists of 1, 2, 4, 8, or 16 Control Channel Elements (CCEs) depending on the Aggregation Level (AL). A CCE is a logical allocation unit used to provide a PDCCH with a predetermined code rate depending on the radio channel status. A CCE consists of six Resource Element Groups (REGs). A REG is defined as one OFDM symbol and one (P)RB. The PDCCH is transmitted through a Control Resource Set (CORESET). A CORESET is defined as a set of REGs with a given numerology (e.g., SCS, CP length, etc.). Multiple CORESETs for a single UE can overlap in the time / frequency domain. A CORESET can be configured through system information (e.g., Master Information Block, MIB) or UE-specific upper layer (e.g., Radio Resource Control, RRC, layer) signaling. Specifically, the number of RBs and the number of OFDM symbols (up to 3) that constitute the CORESET can be set by upper layer signaling.

[0068] To receive / detect PDCCH, the UE monitors PDCCH candidates. PDCCH candidates represent the CCE(s) that the UE should monitor for PDCCH detection. Each PDCCH candidate is defined as 1, 2, 4, 8, or 16 CCEs depending on the AL. Monitoring involves (blind) decoding the PDCCH candidates. The set of PDCCH candidates that the UE monitors is defined as a PDCCH Search Space (SS). The search space includes a Common Search Space (CSS) or a UE-specific search space (USS). The UE can acquire DCI by monitoring PDCCH candidates in one or more search spaces configured by the MIB or higher-layer signaling. Each CORESET is associated with one or more search spaces, and each search space is associated with one COREST. The search space can be defined based on the following parameters.

[0069] - controlResourceSetId: Indicates the CORESET associated with the search space.

[0070] - monitoringSlotPeriodicityAndOffset: Indicates the PDCCH monitoring period (in slots) and the PDCCH monitoring interval offset (in slots).

[0071] - monitoringSymbolsWithinSlot: Indicates the PDCCH monitoring symbols within the slot (e.g., the first symbol(s) of the CORESET).

[0072] - nrofCandidates: AL={1, 2, 4, 8, 16} indicates the number of PDCCH candidates (one of 0, 1, 2, 3, 4, 5, 6, 8)

[0073] * An opportunity (e.g., time / frequency resource) for monitoring PDCCH candidates is defined as a PDCCH (monitoring) opportunity. One or more PDCCH (monitoring) opportunities can be configured within a slot.

[0074] Table 3 illustrates the characteristics of each search space type.

[0075] TypeSearch SpaceRNTIUse CaseType0-PDCCHCommonSI-RNTI on a primary cellSIB DecodingType0A-PDCCHCommonSI-RNTI on a primary cellSIB DecodingType1-PDCCHCommonRA-RNTI or TC-RNTI on a primary cellMsg2, Msg4 decoding in RACHType2-PDCCHCommonP-RNTI on a primary cellPaging DecodingType3-PDCCHCommonINT-RNTI, SFI-RNTI, TPC-PUSCH-RNTI, TPC-PUCCH-RNTI, TPC-SRS-RNTI, C-RNTI, MCS-C-RNTI, or CS-RNTI(s)UE SpecificUE SpecificC-RNTI, or MCS-C-RNTI, or CS-RNTI(s)User specific PDSCH decoding

[0076] Table 4 illustrates DCI formats transmitted via PDCCH.

[0077] DCI formatUsage0_0Scheduling of PUSCH in one cell0_1Scheduling of PUSCH in one cell1_0Scheduling of PDSCH in one cell1_1Scheduling of PDSCH in one cell2_0Notifying a group of UEs of the slot format2_1Notifying a group of UEs of the PRB(s) and OFDM symbol(s) where UE may assume no transmission is intended for the UE2_2Transmission of TPC commands for PUCCH and PUSCH2_3Transmission of a group of TPC commands for SRS transmissions by one or more UEs

[0078] DCI format 0_0 is used to schedule a TB-based (or TB-level) PUSCH, and DCI format 0_1 ​​can be used to schedule a TB-based (or TB-level) PUSCH or a CBG (Code Block Group)-based (or CBG-level) PUSCH. DCI format 1_0 is used to schedule a TB-based (or TB-level) PDSCH, and DCI format 1_1 can be used to schedule a TB-based (or TB-level) PDSCH or a CBG-based (or CBG-level) PDSCH (DL grant DCI). DCI format 0_0 / 0_1 may be referred to as UL grant DCI or UL scheduling information, and DCI format 1_0 / 1_1 may be referred to as DL grant DCI or DL ​​scheduling information. DCI format 2_0 is used to convey dynamic slot format information (e.g., dynamic SFI) to the terminal, and DCI format 2_1 is used to convey downlink pre-emption information to the terminal. DCI format 2_0 and / or DCI format 2_1 can be conveyed to the terminals within a group through the group common PDCCH, which is a PDCCH conveyed to the terminals defined as a group.

[0079] DCI format 0_0 and DCI format 1_0 may be referred to as fallback DCI formats, while DCI format 0_1 ​​and DCI format 1_1 may be referred to as non-fallback DCI formats. In the fallback DCI format, the DCI size / field configuration remains the same regardless of the terminal configuration. On the other hand, in the non-fallback DCI format, the DCI size / field configuration varies depending on the terminal configuration.

[0080] PDSCH carries downlink data (e.g., DL-SCH transport block, DL-SCH TB) and applies modulation methods such as Quadrature Phase Shift Keying (QPSK), 16 Quadrature Amplitude Modulation (QAM), 64 QAM, and 256 QAM. TB is encoded to generate a codeword. PDSCH can carry up to two codewords. Scrambling and modulation mapping are performed for each codeword, and modulation symbols generated from each codeword can be mapped to one or more layers. Each layer is mapped to resources along with a Demodulation Reference Signal (DMRS), generated as an OFDM symbol signal, and transmitted through the corresponding antenna port.

[0081] PUCCH carries Uplink Control Information (UCI). UCI includes:

[0082] - SR (Scheduling Request): Information used to request UL-SCH resources.

[0083] - HARQ(Hybrid Automatic Repeat reQuest)-ACK(Acknowledgement): This is a response to a downlink data packet (e.g., codeword) on the PDSCH. It indicates whether the downlink data packet was successfully received. One HARQ-ACK bit can be transmitted in response to a single codeword, and two HARQ-ACK bits can be transmitted in response to two codewords. The HARQ-ACK response includes a positive ACK (simply, ACK), a negative ACK (NACK), a DTX, or a NACK / DTX. Here, HARQ-ACK is used interchangeably with HARQ ACK / NACK and ACK / NACK.

[0084] - CSI (Channel State Information): Feedback information for the downlink channel. MIMO (Multiple Input Multiple Output)-related feedback information includes the Rank Indicator (RI) and Precoding Matrix Indicator (PMI).

[0085] Table 5 illustrates PUCCH formats. Depending on the PUCCH transmission length, they can be classified into Short PUCCH (formats 0 and 2) and Long PUCCH (formats 1, 3, and 4).

[0086] PUCCH formatLength in OFDM symbols N PUCCH symb Number of bitsUsageEtc01 - 2≤2HARQ, SRSequence selection14 - 14≤2HARQ, [SR]Sequence modulation21 - 2>2HARQ, CSI, [SR]CP-OFDM34 - 14>2HARQ, CSI, [SR]DFT-s-OFDM(no UE multiplexing)44 - 14>2HARQ, CSI, [SR]DFT-s-OFDM(Pre DFT OCC)

[0087] PUCCH format 0 carries UCI of up to 2 bits in size and is mapped and transmitted based on sequence. Specifically, the terminal transmits a specific UCI to the base station by transmitting one of multiple sequences through the PUCCH of PUCCH format 0. The terminal transmits the PUCCH of PUCCH format 0 within the PUCCH resources for the corresponding SR configuration only when transmitting a positive SR.

[0088] PUCCH format 1 carries UCI of up to 2 bits in size, and modulation symbols are spread in the time domain using an orthogonal cover code (OCC) (which is set differently depending on whether frequency hopping is used). DMRS are transmitted in symbols where modulation symbols are not transmitted (i.e., transmitted using Time Division Multiplexing (TDM).

[0089] PUCCH format 2 carries UCI with a bit size greater than 2 bits, and modulation symbols are transmitted by frequency division multiplexing (FDM) with DMRS. DM-RSs are located at symbol indices #1, #4, #7, and #10 within a given resource block with a density of 1 / 3. Pseudo Noise (PN) sequences are used for DM_RS sequences. Frequency hopping can be enabled for 2-symbol PUCCH format 2.

[0090] PUCCH format 3 does not multiplex terminals within the same physical resource blocks and carries UCI with a bit size greater than 2 bits. In other words, PUCCH resources in PUCCH format 3 do not include orthogonal cover codes. Modulation symbols are transmitted through time division multiplexing (TDM) with DMRS.

[0091] PUCCH format 4 supports multiplexing of up to four terminals within the same physical resource blocks and carries UCI with a bit size greater than 2 bits. In other words, PUCCH resources in PUCCH format 3 include orthogonal cover codes. Modulation symbols are transmitted through time division multiplexing (TDM) with DMRS.

[0092] At least one of one or more configured cells in a terminal may be configured for PUCCH transmission. At least the primary cell may be configured as a cell for PUCCH transmission. At least one PUCCH cell group may be configured in the terminal based on at least one cell configured for PUCCH transmission, and each PUCCH cell group includes one or more cells. The PUCCH cell group may be simply referred to as a PUCCH group. PUCCH transmission may be configured not only for the primary cell but also for the SCell, and the primary cell belongs to the primary PUCCH group, and the PUCCH-SCell configured for PUCCH transmission belongs to the secondary PUCCH group. For cells belonging to the primary PUCCH group, the PUCCH on the primary cell may be used, and for cells belonging to the secondary PUCCH group, the PUCCH on the PUCCH-SCell may be used.

[0093] PUSCH carries uplink data (e.g., UL-SCH transport block, UL-SCH TB) and / or uplink control information (UCI), and is transmitted based on a CP-OFDM (Cyclic Prefix - Orthogonal Frequency Division Multiplexing) waveform or a DFT-s-OFDM (Discrete Fourier Transform - spread - Orthogonal Frequency Division Multiplexing) waveform. When the PUSCH is transmitted based on a DFT-s-OFDM waveform, the UE transmits the PUSCH by applying transform precoding. For example, when transform precoding is disabled (e.g., transform precoding is disabled), the UE transmits the PUSCH based on the CP-OFDM waveform, and when transform precoding is enabled (e.g., transform precoding is enabled), the UE can transmit the PUSCH based on the CP-OFDM waveform or the DFT-s-OFDM waveform. PUSCH transmissions can be dynamically scheduled by UL grants in DCI, or semi-statically scheduled (configured grant) based on higher layer (e.g., RRC) signaling (and / or Layer 1 (L1) signaling (e.g., PDCCH)). PUSCH transmissions can be performed in a codebook-based or non-codebook-based manner.

[0094] Figure 5 illustrates an ACK / NACK transmission process. Referring to Figure 5, a terminal can detect a PDCCH in slot #n. Here, the PDCCH includes downlink scheduling information (e.g., DCI formats 1_0, 1_1), and the PDCCH indicates a DL assignment-to-PDSCH offset (K0) and a PDSCH-HARQ-ACK reporting offset (K1). For example, DCI formats 1_0, 1_1 can include the following information:

[0095] - Frequency domain resource assignment: Indicates the set of RBs allocated to the PDSCH.

[0096] - Time domain resource assignment: K0 (e.g., slot offset), indicates the starting position of the PDSCH within slot #n+K0 (e.g., OFDM symbol index), and the length of the PDSCH (e.g., number of OFDM symbols).

[0097] - PDSCH-to-HARQ_feedback timing indicator: Indicates K1

[0098] - HARQ process number (4 bits): Indicates the HARQ process ID (Identity) for data (e.g., PDSCH, TB)

[0099] - PUCCH resource indicator (PRI): Indicates the PUCCH resource to be used for UCI transmission among multiple PUCCH resources within the PUCCH resource set.

[0100] Thereafter, the terminal may receive PDSCH from slot #(n+K0) according to the scheduling information of slot #n, and then transmit UCI through PUCCH in slot #(n1+K1) when reception of PDSCH is finished in slot #n1 (where, n+K0≤n1). Here, UCI may include HARQ-ACK response for PDSCH. In Fig. 5, for convenience, it is assumed that SCS for PDSCH and SCS for PUCCH are the same and slot # n1 = slot #n+K0, but the present disclosure is not limited thereto. If the SCSs are different, K1 may be indicated / interpreted based on the SCS of PUCCH.

[0101] When the PDSCH is configured to transmit at most 1 TB, the HARQ-ACK response may consist of 1 bit. When the PDSCH is configured to transmit at most 2 TB, the HARQ-ACK response may consist of 2 bits if spatial bundling is not configured, and may consist of 1 bit if spatial bundling is configured. When the HARQ-ACK transmission timing for multiple PDSCHs is designated as slot #(n+K1), the UCI transmitted in slot #(n+K1) includes HARQ-ACK responses for multiple PDSCHs.

[0102] Whether a UE should perform spatial bundling for a HARQ-ACK response can be configured (e.g., via RRC / higher layer signaling) for each cell group. For example, spatial bundling can be individually configured for each HARQ-ACK response transmitted over the PUCCH and / or each HARQ-ACK response transmitted over the PUSCH.

[0103] Spatial bundling can be supported when the maximum number of TBs (or codewords) that can be received at a time (or scheduled via 1 DCI) in the serving cell is 2 (or more than 2) (e.g., when the upper layer parameter maxNrofCodeWordsScheduledByDCI corresponds to 2-TB). Meanwhile, more than 4 layers can be used for 2-TB transmission, and up to 4 layers can be used for 1-TB transmission. Consequently, when spatial bundling is configured for the cell group, spatial bundling can be performed for serving cells that can schedule more than 4 layers among the serving cells in the cell group. On the serving cell, a terminal that wishes to transmit a HARQ-ACK response via spatial bundling can generate the HARQ-ACK response by performing a (bit-wise) logical AND operation on the A / N bits for multiple TBs.

[0104] For example, assuming that a terminal receives a DCI scheduling 2 TB and receives 2 TB via PDSCH based on the DCI, the terminal performing spatial bundling can generate a single A / N bit by logically ANDing the first A / N bit for the first TB and the second A / N bit for the second TB. Consequently, if both the first TB and the second TB are ACK, the terminal reports the ACK bit value to the base station, and if either TB is NACK, the terminal reports the NACK bit value to the base station.

[0105] For example, if only 1-TB is actually scheduled on a serving cell configured to receive 2-TB, the terminal can generate a single A / N bit by logically ANDing the A / N bit for the 1-TB with bit value 1. Consequently, the terminal reports the A / N bit for the 1-TB to the base station as is.

[0106] A base station / terminal has multiple parallel DL HARQ processes for DL ​​transmission. These multiple parallel HARQ processes allow DL transmissions to be performed continuously while waiting for HARQ feedback regarding the success or failure of the previous DL transmission. Each HARQ process is associated with a HARQ buffer in the MAC (Medium Access Control) layer. Each DL HARQ process manages state variables such as the number of transmissions of MAC Physical Data Blocks (PDUs) in the buffer, HARQ feedback for MAC PDUs in the buffer, and the current redundancy version. Each HARQ process is identified by a HARQ process ID.

[0107] Figure 6 illustrates a PUSCH transmission process. Referring to Figure 6, a terminal can detect a PDCCH in slot #n. Here, the PDCCH includes uplink scheduling information (e.g., DCI formats 0_0 and 0_1). DCI formats 0_0 and 0_1 can include the following information.

[0108] - Frequency domain resource assignment: Indicates the set of RBs allocated to PUSCH.

[0109] - Time domain resource assignment: Slot offset K2 indicates the starting position (e.g., symbol index) and length (e.g., number of OFDM symbols) of the PUSCH within the slot. The starting symbol and length can be indicated through SLIV (Start and Length Indicator Value) or can be indicated separately.

[0110] Thereafter, the terminal can transmit a PUSCH in slot #(n+K2) according to the scheduling information of slot #n. Here, the PUSCH includes a UL-SCH TB.

[0111] CSI-related actions

[0112] Figure 7 shows an example of a CSI-related procedure.

[0113] The terminal receives configuration information related to CSI from the base station via RRC signaling (710). The configuration information related to CSI may include at least one of CSI-IM (interference management) resource-related information, CSI measurement configuration-related information, CSI resource configuration-related information, CSI-RS resource-related information, or CSI report configuration-related information.

[0114] - CSI-IM resources can be configured for interference measurement (IM) of the terminal. In the time domain, the CSI-IM resource set can be configured periodically, semi-persistently, or aperiodicly. The CSI-IM resources can be configured as Zero Power (ZP)-CSI-RS for the terminal. The ZP-CSI-RS can be configured separately from the Non-Zero Power (NZP)-CSI-RS.

[0115] - The UE may assume that the CSI-RS resource(s) for channel measurement configured for one CSI reporting and the CSI-IM / NZP CSI-RS resource(s) for interference measurement (when NZP CSI-RS resource(s) are used for interference measurement) are in a QCL relationship with respect to 'QCL-TypeD' per resource.

[0116] - The CSI resource configuration may include at least one of a CSI-IM resource for interference measurement, an NZP CSI-RS resource for interference measurement, and an NZP CSI-RS resource for channel measurement. The CMR (channel measurement resource) may be an NZP CSI-RS for CSI acquisition, and the IMR (Interference measurement resource) may be an NZP CSI-RS for CSI-IM and IM.

[0117] - CSI-RS can be configured for one or more terminals. Different CSI-RS configurations may be provided for each terminal, or the same CSI-RS configuration may be provided to multiple terminals. CSI-RS can support up to 32 antenna ports. CSI-RS corresponding to N (N is 1 or greater) antenna ports can be mapped to N RE locations within a time-frequency unit corresponding to one slot and one RB. When N is 2 or greater, N-port CSI-RS can be multiplexed using CDM, FDM, and / or TDM schemes. CSI-RS can be mapped to REs other than REs to which CORESET, DMRS, and SSB are mapped. In the frequency domain, CSI-RS can be configured for the entire bandwidth, a portion of the bandwidth (BWP), or a portion of the bandwidth. CSI-RS may be transmitted in each RB within the bandwidth for which CSI-RS is configured (i.e., density = 1), or in every second RB (e.g., even or odd RB) (i.e., density = 1 / 2). When CSI-RS is used as a Tracking Reference Signal (TRS), a single-port CSI-RS may be mapped on three subcarriers in each resource block (i.e., density = 3). One or more CSI-RS resource sets may be configured for a UE in the time domain. Each CSI-RS resource set may include one or more CSI-RS configurations. Each CSI-RS resource set may be configured periodically, semi-persistently, or aperiodicly.

[0118] - The CSI report configuration may include configurations for feedback type, measurement resources, report type, etc. The NZP-CSI-RS resource set may be used for the CSI report configuration of the corresponding terminal. The NZP-CSI-RS resource set may be associated with CSI-RS or SSB. In addition, multiple periodic NZP-CSI-RS resource sets may be configured as TRS resource sets. (i) The feedback type may include a Channel Quality Indicator (CQI), a Precoding Matrix Indicator (PMI), a CSI-RS Resource Indicator (CRI), an SSB Resource block Indicator (SSBRI), a Layer Indicator (LI), a Rank Indicator (RI), a Layer 1-Reference Signal Received Strength (RSRP), etc. (ii) Measurement resources may include configurations for downlink signals and / or downlink resources on which the terminal performs measurements to determine feedback information. The measurement resources may be configured as ZP and / or NZP CSI-RS resource sets associated with CSI reporting configurations. The NZP CSI-RS resource set may include a CSI-RS set or an SSB set. For example, L1-RSRP may be measured for a CSI-RS set or an SSB set. (iii) Reporting types may include configurations for a time point at which the terminal performs reporting and an uplink channel, etc. The reporting time point may be configured as periodic, semi-persistent, or aperiodic. Periodic CSI reporting may be transmitted on PUCCH. Semi-persistent CSI reporting may be transmitted on PUCCH or PUSCH based on a MAC CE indicating activation / deactivation. Aperiodic CSI reporting may be indicated by DCI signaling.For example, the CSI request field of an uplink grant may indicate one of several report trigger sizes. Aperiodic CSI reports may be transmitted on the PUSCH.

[0119] The terminal measures CSI based on configuration information related to CSI. CSI measurement may include a procedure of receiving a CSI-RS (720) and computing the received CSI-RS to acquire CSI (730).

[0120] The UE can transmit a CSI report to the base station (740). For the CSI report, the time and frequency resources that the UE can use are controlled by the base station. The CSI (channel state information) can include at least one of a channel quality indicator (CQI), a precoding matrix indicator (PMI), a CSI-RS resource indicator (CRI), an SS / PBCH block resource indicator (SSBRI), a layer indicator (LI), a rank indicator (RI), L1-RSRP, and / or L-SINR.

[0121] The time domain behavior of CSI reporting supports periodic, semi-persistent, and aperiodic. i) Periodic CSI reporting is performed on short PUCCH and long PUCCH. The periodicity and slot offset of periodic CSI reporting can be configured by RRC, and refer to the CSI-ReportConfig IE. ii) SP (semi-periodic) CSI reporting is performed on short PUCCH, long PUCCH, or PUSCH. In case of SP CSI on short / long PUCCH, the periodicity and slot offset are configured by RRC, and CSI reporting is activated / deactivated by separate MAC CE / DCI. In case of SP CSI on PUSCH, the periodicity of SP CSI reporting is configured by RRC, but the slot offset is not configured by RRC, and SP CSI reporting is activated / deactivated by DCI (format 0_1). For SP CSI reporting on PUSCH, a separate RNTI (SP-CSI C-RNTI) is used. The initial CSI reporting timing follows the PUSCH time domain allocation value indicated in the DCI, and subsequent CSI reporting timings follow the cycle set by RRC. DCI format 0_1 ​​includes a CSI request field and can activate / deactivate a specific configured SP-CSI trigger state. SP CSI reporting has the same or similar activation / deactivation mechanism as the data transmission mechanism on the SPS PUSCH.iii) Aperiodic CSI reporting is performed on PUSCH and is triggered by DCI. In this case, information related to the triggering of aperiodic CSI reporting can be transmitted / indicated / configured via MAC-CE. For AP CSI with AP CSI-RS, the AP CSI-RS timing is configured by RRC, and the timing for AP CSI reporting is dynamically controlled by DCI.

[0122] CSI codebooks defined in the NR standard (e.g., PMI codebooks) can be broadly divided into Type I and Type II codebooks. Type I codebooks are primarily targeted at SU (Single User)-MIMO, which supports both high-order and low-order signals. Type II codebooks can primarily support MI-MIMO, which supports up to two layers. Compared to Type I, Type II codebooks can provide more accurate CSI, but may increase signaling overhead. Meanwhile, Enhanced Type II codebooks were introduced to address the CSI overhead shortcomings of existing Type II codebooks. Enhanced Type II codebooks were introduced by reducing the codebook payload by considering frequency-axis correlation.

[0123] CSI reporting via PUSCH can be configured as Part 1 and Part 2. Part 1 has a fixed payload size and is used to identify the number of information bits in Part 2. Part 1 is transmitted in its entirety before Part 2.

[0124] - For Type I CSI feedback, Part 1 contains the RI (if reported), the CRI (if reported), and the CQI of the first code word. Part 2 contains the PMI, and when RI > 4, Part 2 contains the CQI.

[0125] - For Type II CSI feedback, Part 1 contains the RI (if reported), CQI, and an indication of the number of non-zero WB amplitude coefficients per layer of Type II CSI. Part 2 contains the PMI of Type II CSI.

[0126] - For Enhanced Type II CSI feedback, Part 1 contains the RI (if reported), CQI, and the total number of non-zero WB amplitude coefficients for all layers of Enhanced Type II CSI. Part 2 contains the PMI of Enhanced Type II CSI.

[0127] If CSI reporting on PUSCH includes two parts and the CSI payload to be reported is less than the payload size provided by the PUSCH resources allocated for CSI reporting, the UE may omit part of Part 2 CSI.

[0128] Meanwhile, semi-persistent CSI reporting performed in PUCCH format 3 or 4 supports Type II CSI feedback, but only Part 1 of Type II CSI feedback.

[0129] QCL (quasi-co location)

[0130] Two antenna ports are quasi-co-located if the channel properties of one antenna port can be inferred from the channel properties of the other antenna port. The channel properties may include one or more of Delay spread, Doppler spread, Frequency / Doppler shift, Average received power, Received Timing / average delay, and Spatial RX parameters.

[0131] A terminal can configure a list of multiple TCI-State configurations via the upper layer parameter PDSCH-Config. Each TCI-State is associated with one or two DL reference signals and a QCL configuration parameter between the DM-RS port of the PDSCH. The QCL can include qcl-Type1 for the first DL RS and qcl-Type2 for the second DL RS. The QCL type can correspond to one of the following:

[0132] - 'QCL-TypeA': {Doppler shift, Doppler spread, average delay, delay spread}

[0133] - 'QCL-TypeB': {Doppler shift, Doppler spread}

[0134] - 'QCL-TypeC': {Doppler shift, average delay}

[0135] - 'QCL-TypeD': {Spatial Rx parameter}

[0136] Beam Management (BM)

[0137] The BM process is a process for acquiring and maintaining a set of BS (or transmission and reception point (TRP)) and / or UE beams that can be used for downlink (DL) and uplink (UL) transmission / reception, and may include the following processes and terms.

[0138] - Beam measurement: An operation in which a BS or UE measures the characteristics of a received beamforming signal.

[0139] - Beam determination: An operation in which a BS or UE selects its own transmit beam (Tx beam) / receive beam (Rx beam).

[0140] - Beam sweeping: An operation of covering a spatial domain using transmit and / or receive beams over a predetermined time interval in a predetermined manner.

[0141] - Beam report: An operation in which a UE reports information about a beamformed signal based on beam measurement.

[0142] The BM process can be divided into (1) a DL BM process using SSB or CSI-RS, and (2) a UL BM process using SRS (sounding reference signal). In addition, each BM process can include Tx beam sweeping to determine a Tx beam and Rx beam sweeping to determine an Rx beam.

[0143] At this time, the DL BM process may include (1) transmission of beamformed DL RSs (e.g., CSI-RS or SSB) by the BS and (2) beam reporting by the UE.

[0144] Here, the beam report may include preferred DL RS ID(s) and corresponding reference signal received power (RSRP). The DL RS ID may be an SSB Resource Indicator (SSBRI) or a CSI-RS Resource Indicator (CRI).

[0145] Positioning

[0146] Positioning may refer to determining the geographic location and / or velocity of a UE by measuring radio signals. Position information may be requested by a client (e.g., an application) associated with the UE and reported to the client. Furthermore, the location information may be contained within the core network or requested by a client connected to the core network. The location information may be reported in a standard format, such as cell-based or geographic coordinates, and may also include an estimated error value for the UE's position and velocity and / or the positioning method used for positioning.

[0147] LPP can be used as a point-to-point between a location server (E-SMLC and / or SLP and / or LMF) and a target device (UE and / or SET) to position the target device using position-related measurements obtained from one or more reference sources. Through LPP, the target device and the location server can exchange measurement and / or position information based on Signal A and / or Signal B.

[0148] NRPPa can be used to exchange information between a reference source (ACCESS NODE and / or BS and / or TP and / or NG-RAN node) and a location server.

[0149] The functions provided by the NRPPa protocol may include:

[0150] - E-CID Location Information Transfer. This function allows location information to be exchanged between the reference source and the LMF for E-CID positioning purposes.

[0151] - OTDOA Information Transfer. This function allows information to be exchanged between the reference source and the LMF for OTDOA positioning purposes.

[0152] - Reporting of General Error Situations. This feature allows reporting of general error situations for which no function-specific error message is defined.

[0153] The positioning methods supported by NG-RAN may include GNSS (Global Navigation Satellite System), OTDOA, E-CID (enhanced cell ID), barometric positioning, WLAN positioning, Bluetooth positioning, terrestrial beacon system (TBS), and UTDOA (Uplink Time Difference of Arrival). Among the above positioning methods, the position of the UE may be measured using any one of the positioning methods, but the position of the UE may also be measured using two or more positioning methods.

[0154] OTDOA (Observed Time Difference Of Arrival)

[0155] The OTDOA positioning method utilizes the timing measurements of downlink signals received by the UE from multiple TPs, including the eNB, ng-eNB, and PRS-dedicated TPs. The UE measures the timing of the received downlink signals using location assistance data received from a location server. Based on these measurement results and the geographic coordinates of neighboring TPs, the UE's location can be determined.

[0156] A UE connected to a gNB can request a measurement gap for OTDOA measurements from a TP. If the UE does not recognize the SFN for at least one TP in the OTDOA assistance data, the UE can use an autonomous gap to obtain the SFN of the OTDOA reference cell before requesting a measurement gap to perform Reference Signal Time Difference (RSTD) measurements.

[0157] Here, the RSTD can be defined based on the smallest relative time difference between the boundaries of two subframes received from the reference cell and the measurement cell, respectively. That is, it can be calculated based on the relative time difference between the start time of the subframe of the reference cell that is closest to the start time of the subframe received from the measurement cell. Meanwhile, the reference cell can be selected by the UE.

[0158] Accurate OTDOA measurement requires measuring the time of arrival (TOA) of signals received from three or more geographically dispersed TPs or base stations. For example, the TOA for TP 1, TP 2, and TP 3 can be measured, and based on the three TOAs, the RSTD for TP 1-TP 2, the RSTD for TP 2-TP 3, and the RSTD for TP 3-TP 1 can be calculated. Based on these TOAs, a geometric hyperbola can be determined, and the point where these hyperbolas intersect can be used to estimate the UE's location. Since each TOA measurement may have inaccuracies and / or uncertainties, the estimated UE's location can be known within a certain range depending on the measurement uncertainty.

[0159] E-CID (Enhanced Cell ID)

[0160] In the Cell ID (CID) positioning method, the location of the UE can be measured through geographic information of the UE's serving ng-eNB, serving gNB, and / or serving cell. For example, geographic information of the serving ng-eNB, serving gNB, and / or serving cell can be obtained through paging, registration, etc.

[0161] Meanwhile, the E-CID positioning method may utilize additional UE measurements and / or NG-RAN radio resources in addition to the CID positioning method to improve the UE position estimate. In the E-CID positioning method, some of the same measurement methods as the measurement control system of the RRC protocol may be used, but generally, additional measurements are not performed solely for UE position measurement. In other words, a separate measurement configuration or measurement control message may not be provided to measure the UE's position, and the UE may not expect to be requested to perform additional measurement operations solely for position measurement, and may report measurement values ​​obtained through measurement methods that the UE can generally measure.

[0162] For example, a serving gNB can implement an E-CID positioning method using E-UTRA measurements provided from the UE.

[0163] AI / ML (Artificial intelligence / machine learning)

[0164] Technological advancements in AI / ML are leading to the intelligence / advanced development of node(s) and terminal(s) that make up wireless communication networks. In particular, the intelligence of networks / base stations will enable the rapid optimization / derivation / application of various network / base station decision parameter values ​​(e.g., transmit / receive power of each base station, transmit power of each terminal, precoder / beam of base stations / terminals, time / frequency resource allocation for each terminal, duplexing method of each base station, etc.) based on various environmental parameters (e.g., distribution / location of base stations, distribution / location / material of buildings / furniture, etc., location / movement direction / speed of terminals, climate information, etc.). In line with this trend, many standardization organizations (e.g., 3GPP, O-RAN) are considering its introduction, and research on it is also actively underway.

[0165] AI / ML can be easily referred to as artificial intelligence based on deep learning in a narrow sense, but conceptually it is as shown in Figure 8.

[0166] - Artificial Intelligence: This can refer to all automation where machines can replace tasks that people would otherwise do.

[0167] - Machine Learning: Machines can learn patterns for decision-making from data without explicitly programming rules.

[0168] Deep Learning: An AI / ML model based on artificial neural networks. Machines simultaneously extract features from unstructured data and make judgments. The algorithms rely on multilayer networks of interconnected nodes for feature extraction and transformation, inspired by the biological nervous system, or neural networks. Common deep learning network architectures include deep neural networks (DNNs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs).

[0169] Classification of AI / ML types based on various criteria

[0170] 1. Offline vs. Online

[0171] (1) Offline Learning: This follows a sequential process of database collection, learning, and prediction. In other words, collection and learning are performed offline, and the completed program can be installed on-site for use in prediction tasks. This offline learning method is used in most situations. In offline learning, the system does not learn incrementally; learning is performed using all available collected data and applied to the system without further training. If learning on new data is required, learning can be restarted using the entire new data set.

[0172] (2) Online Learning: Online learning leverages the continuous availability of data available for learning via the Internet. This method incrementally improves performance by learning from additional data. Learning is performed in real time on specific data (sets) collected online, enabling the system to quickly adapt to changing data.

[0173] To build an AI system, only online learning may be used, so that learning is performed using only real-time data, or offline learning may be performed using a predetermined data set, and then additional learning may be performed using additional real-time data (online + offline learning).

[0174] 2. Classification by AI / ML Framework Concept

[0175] (1) Centralized Learning: Training data collected from multiple different nodes are reported to a centralized node, and all data resources / storage / learning (e.g., supervised, unsupervised, reinforcement learning) are performed in one central node.

[0176] (2) Federated Learning: A collective AI / ML model is built based on data across distributed data owners. Instead of importing data into an AI / ML model, the AI / ML model is imported as a data source, allowing local nodes / individual devices to collect data and train their own copies of the AI / ML model, eliminating the need to report source data to a central node. In federated learning, the parameters / weights of the AI / ML model are simply sent back to the centralized node to support general AI / ML model training. The advantages of federated learning include increased computational speed and superior information security. This eliminates the need to upload personal data to a central server, preventing personal information leaks and misuse.

[0177] (3) Distributed Learning: This concept represents the concept of machine learning processes being scaled and distributed across a cluster of nodes. Training AI / ML models are split and shared across multiple nodes operating simultaneously to accelerate AI / ML model training.

[0178] 3. Classification by learning method

[0179] (1) Supervised Learning: Supervised learning is a machine learning task that aims to learn a mapping function from input to output given a labeled data set. The input data is called training data and has known labels or outcomes. Examples of supervised learning include (i) Regression: Linear Regression, Logistic Regression, (ii) Instance-based Algorithms: k-Nearest Neighbor (KNN), (iii) Decision Tree Algorithms: CART, (iv) Support Vector Machines: SVM, (v) Bayesian Algorithms: Naive Bayes, and (vi) Ensemble Algorithms: Extreme Gradient Boosting, Bagging: Random Forest. Supervised learning can be further grouped into regression and classification problems, where classification predicts labels and regression predicts quantities.

[0180] (2) Unsupervised Learning: A machine learning task that aims to learn features that explain hidden structures in unlabeled data. The input data is unlabeled and has no known outcome. Some examples of unsupervised learning include K-means clustering, principal component analysis (PCA), nonlinear independent component analysis (ICA), and long-term memory (LSTM).

[0181] (3) Reinforcement Learning: In reinforcement learning (RL), an agent interacts with the environment through a trial-and-error process, aiming to optimize a long-term goal. It is a goal-oriented learning method based on interaction with the environment. Examples of RL algorithms include (i) Q-learning, (ii) multi-armed bandit learning, (iii) deep Q network, state-action-reward-state-action (SARSA), (iv) temporal difference learning, (v) actor-critic reinforcement learning, (vi) deep deterministic policy gradient, and (vii) Monte-Carlo tree search. Reinforcement learning can be further grouped into AI / ML model-based reinforcement learning and AI / ML model-free reinforcement learning. Model-based reinforcement learning is an RL algorithm that uses a predictive AI / ML model to obtain transition probabilities between states by using various dynamic states of the environment and the AI / ML model that leads to these states as rewards. Model-free reinforcement learning is a value- or policy-based RL algorithm that maximizes future rewards. It is computationally less complex in multi-agent environments / states and does not require an accurate representation of the environment. RL algorithms can also be categorized into value-based RL versus policy-based RL, and policy-based RL versus non-policy RL.

[0182] AI / ML models

[0183] Figure 9 illustrates an FFNN (Feed-Forward Neural Network) AI / ML model. Referring to Figure 9, the FFNN AI / ML model includes an input layer, a hidden layer, and an output layer.

[0184] Figure 10 illustrates an RNN (Recurrent Neural Network) AI / ML model. Referring to Figure 10, the RNN AI / ML model is a type of artificial neural network in which hidden nodes are connected by directed edges to form a cyclic structure (directed cycle), and is an AI / ML model suitable for processing data that appears sequentially, such as voice and text. One type of RNN is LSTM (Long Short-Term Memory), and LSTM is a structure that adds a cell state to the hidden state of the RNN. Specifically, in LSTM, an input gate, a forget gate, and an output gate are added to the RNN cell, and a cell state is added. In Figure 10, A represents a neural network, x t is the input value, h t represents the output value. Here, h t can mean a status value that represents the present based on time, and h t-1 can represent the previous state value.

[0185] Figure 11 illustrates a CNN (Convolution Neural Network) AI / ML model. CNN uses convolution operations commonly used in image processing and video processing to achieve two goals: reducing AI / ML model complexity and extracting good features. Referring to Figure 11, a kernel or filter refers to a unit / structure that applies weights to inputs within a specific range / unit. The kernel (or filter) can be modified through learning. The stride refers to the range of movement of the kernel within the input. The feature map refers to the result of applying the kernel to the input. Padding refers to a value added to adjust the size of the feature map. Multiple feature maps can be extracted to induce robustness to distortion and changes. Pooling refers to an operation (e.g., max pooling, average pooling) to reduce the size of the feature map by downsampling it.

[0186] Figure 12 illustrates an auto-encoder AI / ML model. Referring to Figure 12, an auto-encoder is a neural network that receives a feature vector x as input and outputs the same or similar vector x'. The input and output nodes have the same features, and it is a type of unsupervised learning. Since an auto-encoder reconstructs the input, the output can be referred to as a reconstruction. The loss function can be expressed as in Mathematical Formula 1.

[0187]

[0188] The loss function of the auto encoder exemplified in Figure 12 is calculated based on the difference between the input and the output, and based on this, the degree of loss of the input is identified, and the auto encoder performs an optimization process to minimize the loss.

[0189] Figure 13 is a diagram illustrating segmentation AI inference.

[0190] Figure 13 illustrates a case in which, among split AI operations, the Model Inference function is performed collaboratively by an end device such as a UE and a network AI / ML endpoint.

[0191] In addition to the Model Inference function, the Model Training function, Actor, and Data Collection functions can each be split into multiple parts depending on the current task and environment, and performed by multiple entities collaborating.

[0192] For example, computationally intensive and energy-intensive parts may be performed at the network endpoint, while privacy-sensitive and latency-sensitive parts may be performed at the end device. In this case, the end device may execute the task / model from input data up to a specific part / layer, and then transmit the intermediated data to the network endpoint. The network endpoint then executes the remaining parts / layers and provides the inference outputs to one or more devices that perform the actions / tasks.

[0193] The following describes a functional framework for AI operations.

[0194] Below, to explain AI (or AI / ML) more specifically, the terms can be defined as follows.

[0195] - Data collection: Data collected from network nodes, management entities, or UEs as a basis for AI model training, data analysis, and inference.

[0196] - AI Model: A data-driven algorithm that applies AI technology to generate a set of outputs containing predictive information and / or decision parameters based on a set of inputs.

[0197] - AI / ML Training: An online or offline process of training an AI model by learning features and patterns that best represent the data and obtain a trained AI / ML model for inference.

[0198] - AI / ML Inference: The process of making predictions or inducing decisions based on collected data and the AI ​​model using a trained AI model.

[0199] Referring to FIG. 14, the Data Collection function (10) is a function that collects input data and provides processed input data to the Model Training function (20) and the Model Inference function (30).

[0200] Examples of input data may include measurements from UEs or other network entities, feedback from actors, and output from AI models.

[0201] The Data Collection function (10) performs data preparation based on input data and provides input data processed through data preparation. Here, the Data Collection function (10) does not perform data preparation specific to each AI algorithm (e.g., data pre-processing and cleaning, formatting, and transformation), but can perform data preparation common to AI algorithms.

[0202] After the data preparation process is performed, the Model Training function (10) provides training data (11) to the Model Training function (20) and provides inference data (Inference Data) (12) to the Model Inference function (30). Here, the Training Data (11) is data required as input for the AI ​​Model Training function (20). The Inference Data (12) is data required as input for the AI ​​Model Inference function (30).

[0203] The Data Collection function (10) may be performed by a single entity (e.g., UE, RAN node, network node, etc.) or may be performed by multiple entities. In this case, Training Data (11) and Inference Data (12) may be provided to the Model Training function (20) and Model Inference function (30), respectively, from multiple entities.

[0204] The Model Training function (20) is a function that performs AI model training, validation, and testing, which can generate model performance metrics as part of the AI ​​model testing process. If necessary, the Model Training function (20) also handles data preparation (e.g., data pre-processing and cleaning, forming, and transformation) based on the Training Data (11) provided by the Data Collection function (10).

[0205] Here, Model Deployment / Update (13) is used to initially deploy the trained, verified, and tested AI model to the Model Inference function (30) or to provide the updated model to the Model Inference function (30).

[0206] The Model Inference function (30) is a function that provides AI model inference output (16) (e.g., prediction or decision). If applicable, the Model Inference function (30) may provide model performance feedback (14) to the Model Training function (20). In addition, the Model Inference function (30) is also responsible for data preparation (e.g., data pre-processing and cleaning, forming, and transformation) based on the Inference Data (12) provided by the Data Collection function (10), if necessary.

[0207] Here, Output (16) refers to the inference output of the AI ​​model generated by the Model Inference function (30), and the details of the inference output may vary depending on the use case.

[0208] Model Performance Feedback (14) can be used to monitor the performance of the AI ​​model if available, and this feedback may be omitted.

[0209] The actor function (40) is a function that receives an output (16) from the model inference function (30) and triggers or performs a corresponding task / action. The actor function (40) can trigger tasks / actions for other entities (e.g., one or more UEs, one or more RAN nodes, one or more network nodes, etc.) or for itself.

[0210] Feedback (15) can be used to derive training data (11), inference data (12), or to monitor the performance of the AI ​​model, its impact on the network, etc.

[0211] Meanwhile, the definitions of training / validation / test in the data set used in AI / ML can be distinguished as follows.

[0212] - Training data: This refers to the data set for learning the model.

[0213] - Validation data: This refers to a data set used to validate a model that has already completed training. In other words, it refers to a data set typically used to prevent overfitting of the training data set.

[0214] It also refers to a data set for selecting the best model among the various models learned during the learning process. Therefore, it can be viewed as a type of learning.

[0215] - Test data: This refers to the data set for final evaluation. This data is unrelated to learning.

[0216] In the case of the above data set, if the training set is generally divided, the training data and validation data can be divided and used in a ratio of 8:2 or 7:3 within the entire training set, and if the test is included, it can be divided and used in a ratio of 6:2:2 (training: validation: test).

[0217] Depending on the capability of the AI / ML function between the base station and the terminal, the level of cooperation can be defined as follows, and variations due to combination of multiple levels or separation of any one level are also possible.

[0218] Cat 0a) No collaboration framework: AI / ML algorithms are purely implementation-based and do not require any changes to the wireless interface.

[0219] Cat 0b) This level corresponds to a framework with a modified wireless interface tailored to efficient implementation-based AI / ML algorithms, but without collaboration.

[0220] Category 1) involves inter-node support to improve the AI / ML algorithms of each node. This applies when the UE receives support from the gNB (for training, adaptation, etc.) and vice versa. At this level, model exchange between network nodes is not required.

[0221] Category 2) Joint ML tasks can be performed between the UE and gNB. This level requires the exchange of AI / ML model commands or network nodes.

[0222] The functions exemplified in FIG. 14 above may be implemented in a RAN node (e.g., a base station, a TRP, a central unit (CU) of a base station, etc.), a network node, an operation administration maintenance (OAM) of a network operator, or a UE.

[0223] Alternatively, two or more entities, such as a RAN, a network node, a network operator's OAM, or a UE, may cooperate to implement the functions exemplified in FIG. 14. For example, one entity may perform some of the functions of FIG. 14, and another entity may perform the remaining functions. In this way, since some of the functions exemplified in FIG. 14 are performed by a single entity (e.g., a UE, a RAN node, a network node, etc.), the transmission / provision of data / information between each function may be omitted. For example, if the Model Training function (20) and the Model Inference function (30) are performed by the same entity, the transmission / provision of Model Deployment / Update (13) and Model Performance Feedback (14) may be omitted.

[0224] Alternatively, any one of the functions illustrated in FIG. 14 may be performed through collaboration between two or more entities, including a RAN, a network node, a network operator's OAM, or a UE. This may be referred to as a split AI operation.

[0225] Figure 15 illustrates a case where the AI ​​Model Training function is performed by a network node (e.g., a core network node, an OAM of a network operator, etc.) and the AI ​​Model Inference function is performed by a RAN node (e.g., a base station, a TRP, a CU of a base station, etc.).

[0226] Step 1: RAN node 1 and RAN node 2 transmit input data (i.e., training data) for AI model training to the network node. Here, RAN node 1 and RAN node 2 can also transmit data collected from the UE (e.g., UE measurements related to RSRP, RSRQ, SINR of the serving cell and neighboring cells, UE location, speed, etc.) to the network node.

[0227] Step 2: Network nodes train the AI ​​model using the received training data.

[0228] Step 3: The network node distributes / updates the AI ​​Model to RAN Node 1 and / or RAN Node 2. RAN Node 1 (and / or RAN Node 2) may continue model training based on the received AI Model.

[0229] For convenience of explanation, we assume that the AI ​​Model is deployed / updated only to RAN node 1.

[0230] Step 4: RAN node 1 receives input data for AI Model Inference (i.e., Inference data) from UE and RAN node 2.

[0231] Step 5: RAN node 1 performs AI Model Inference using the received Inference data to generate output data (e.g., prediction or decision).

[0232] Step 6: If applicable, RAN node 1 may transmit model performance feedback to the network nodes.

[0233] Step 7: RAN node 1, RAN node 2, and the UE (or 'RAN node 1 and the UE', or 'RAN node 1 and the RAN node 2') perform actions based on the output data. For example, in the case of a load balancing operation, the UE may move from RAN node 1 to RAN node 2.

[0234] Step 8: RAN node 1 and RAN node 2 transmit feedback information to the network nodes.

[0235] Figure 16 illustrates a case where both the AI ​​Model Training function and the AI ​​Model Inference function are performed by RAN nodes (e.g., base stations, TRPs, CUs of base stations, etc.).

[0236] Step 1: UE and RAN node 2 transmit input data (i.e., training data) for AI model training to RAN node 1.

[0237] Step 2: RAN node 1 trains the AI ​​model using the received training data.

[0238] Step 3: RAN node 1 receives input data for AI Model Inference (i.e., Inference data) from the UE and RAN node 2.

[0239] Step 4: RAN node 1 performs AI Model Inference using the received Inference data to generate output data (e.g., prediction or decision).

[0240] Step 5: RAN node 1, RAN node 2, and the UE (or 'RAN node 1 and the UE', or 'RAN node 1 and the RAN node 2') perform actions based on the output data. For example, in the case of a load balancing operation, the UE may move from RAN node 1 to RAN node 2.

[0241] Step 6: RAN node 2 sends feedback information to RAN node 1.

[0242] Figure 17 illustrates a case where the AI ​​Model Training function is performed by a RAN node (e.g., a base station, a TRP, a CU of the base station, etc.) and the AI ​​Model Inference function is performed by a UE.

[0243] Step 1: The UE transmits input data (i.e., training data) for AI model training to the RAN node. Here, the RAN node can collect data (e.g., UE measurements related to RSRP, RSRQ, SINR of the serving cell and neighboring cells, UE location, speed, etc.) from various UEs and / or from other RAN nodes.

[0244] Step 2: The RAN node trains the AI ​​model using the received training data.

[0245] Step 3: The RAN node distributes / updates the AI ​​model to the UE. The UE may also continue model training based on the received AI model.

[0246] Step 4: Receive input data (i.e., Inference data) for AI Model Inference from the UE and RAN nodes (and / or from other UEs).

[0247] Step 5: The UE performs AI Model Inference using the received Inference data to generate output data (e.g., prediction or decision).

[0248] Step 6: If applicable, the UE may send model performance feedback to the RAN node.

[0249] Step 7: The UE and RAN nodes perform actions based on the output data.

[0250] Step 8: The UE transmits feedback information to the RAN node.

[0251] Figure 18 illustrates a communication procedure between a first node (e.g., terminal) and a second node (e.g., base station) to which an AI / ML model is applied.

[0252] The operations described below can be described / interpreted based on the AI / ML model proposed in this specification, as shown in Figure 18 below, even without separate mention (i.e., without explicit mention of being by / based on / for the AI / ML model). In addition, unless specifically limited, the AI / ML model can correspond to a one-side model in which inference is entirely performed by a single node, or a two-side model in which joint inference is performed by multiple nodes.

[0253] First signaling (1801): In the description below, the signaling (e.g., information / data / channel / signal, etc.) or set of signaling between a specific node (e.g., terminal, network, etc.) and another node may be interpreted as the signaling or set of signaling of the first signaling used to perform an operation based on an AI / ML model, even if not otherwise stated. For example, it may correspond to training data for training (i.e., generation and / or reconstruction) an AI / ML model, or correspond to inference data used for inference of an AI / ML model, or correspond to feedback for an AI / ML model, etc. If signaling between nodes is not required prior to an operation based on an AI / ML model in this specification, the first signaling may be omitted. If a one-side model is used in this specification, the one-way / two-way signaling (set) in this specification may correspond to the signaling of the first signaling. In addition, when a two-side model is used in this specification, unidirectional / bidirectional signaling in this specification may correspond to the first signaling, and also a repetitive signaling operation may correspond to the first signal.

[0254] For example, in AI / ML model-based beam management (BM), if a base station predicts (i.e., infers) beam(s) with good quality based on an AI / ML model, the base station can receive quality / intensity information for multiple beams from a terminal. Furthermore, if a terminal predicts (i.e., infers) beam(s) with good quality based on an AI / ML model, the terminal can receive multiple beams from the base station.

[0255] AI / ML model-based operations (1802): In the description below, operations (e.g., calculation, selection, prediction, etc.) in a specific node (e.g., terminal, network, etc.) or joint operations (e.g., calculation, selection, prediction, etc.) in multiple nodes (e.g., terminal, network, etc.) may correspond to AI / ML model-based operations based on one or more functions in the functional framework of the AI / ML model, even if not mentioned separately. For example, they may correspond to training (i.e., generation and / or reconstruction) of the AI / ML model or inference of the AI / ML model, etc. When a one-side model is used, an operation performed by a single node in the present specification may correspond to an AI / ML model-based operation, and also, when a two-side model is used, joint operations performed by multiple nodes in the present specification may correspond to an AI / ML model-based operation.

[0256] For example, in an AI / ML model-based BM, the base station can use quality / intensity information for multiple beams received from the terminal as inference data to predict (i.e., infer) beam(s) with good quality based on the AI / ML model. Furthermore, the terminal can measure multiple beams received from the base station and use the measurement results as inference data to predict (i.e., infer) beam(s) with good quality based on the AI / ML model.

[0257] Second signaling (1803): In the description below, signaling (e.g., information / data / channel / signal, etc.) or a set of signaling between a specific node (e.g., terminal, network, etc.) and another node may be interpreted as a second signaling or a set of signaling generated due to (as a result of) an operation based on an AI / ML model, even if not otherwise stated. For example, it may correspond to an output resulting from inference of an AI / ML model. If signaling between nodes is not required as a result of an operation based on an AI / ML model in this specification, the second signaling may be omitted. If a one-side model is used in this specification, a one-way / two-way signaling (set) in this specification may correspond to the second signaling. In addition, if a two-side model is used in this specification, a one-way / two-way signaling in this specification may correspond to the second signaling, and a repetitive signaling operation may also correspond to the second signaling.

[0258] For example, in an AI / ML model-based BM, the base station can transmit to the terminal the beam(s) predicted based on the AI / ML model as candidates so that the terminal can determine the optimal beam. Furthermore, the terminal can report to the base station the beam(s) predicted based on the AI / ML model to request the base station to transmit the candidate beams as candidates for determining the optimal beam.

[0259] Data measurement, creation, and / or management for AI / ML

[0260] We propose dataset classification and generation methods to achieve good AI / ML performance when collecting data for training / re-training / update / inference of AI / ML functionality / models.

[0261] The operations of measuring / monitoring / reporting based on (configured) AI / ML functionality / model described below may also be expressed as operations of performing measuring / monitoring / reporting based on specific configuration information (e.g., configuration information provided through upper layer signaling such as RRC in relation to AI / ML functionality / model).

[0262] In relation to the introduction of AI / ML, improvements in CSI feedback (e.g., CSI compression, CSI prediction), beam management (e.g., spatial domain beam prediction, temporal domain beam prediction), and positioning accuracy enhancement (e.g., direct positioning, AI / ML assisted positioning) are being considered. Except for CSI compression, which is one of the use cases for AI / ML-based CSI feedback, all are one-sided models that have AI / ML models / functionality only in the base station or terminal. In the case of CSI compression, it is a two-sided model in which the base station (e.g., decoder model) and the terminal (e.g., encoder model) each have their own models and collaborate to perform CSI feedback.

[0263] In AI / ML-based wireless communications that include these use cases, communication transmission and reception can be performed using AI / ML functionality / models (hereinafter collectively referred to as "AI / ML models"). In this case, because AI / ML is data-driven, its performance can (dynamically) change as channel conditions change. If training / retraining / updating is performed with an incorrect / degraded dataset, the performance of the AI / ML model can deteriorate. For example, an incorrect / degraded dataset can be caused by the following:

[0264] 1. Poorly classified or unclassified dataset

[0265] 2. Dataset corruption from malicious data collection entities or data measurement nodes.

[0266] The first cause can be that the dataset is not classified or classified incorrectly in the data collecting entity or the entity that manages such data, or the labeling is incorrect, or the data collection entity has relatively low capabilities, and when training / re-training / update, etc. are performed using such dataset, the performance of the AI / ML model deteriorates.

[0267] The second cause is that there is a data collection entity that intentionally / maliciously contaminates the dataset, and training / re-training / update, etc. are performed based on the dataset from this entity, which may degrade the performance of the AI / ML model.

[0268] This specification proposes a method to solve or prevent performance degradation caused by an inappropriate dataset.

[0269] The data / datasets assumed in the proposals described below can be configured differently depending on the use case in which each AI / ML model is used. For example, the data / dataset for CSI compression / CSI prediction can be related to channel information in the form of a raw channel matrix measured / estimated based on CSI-RS or a precoder that pre-processes it. The data / dataset for beam management can be related to values ​​such as RSRP or RSRP difference measured / estimated based on SSB / CSI-RS, etc. The data / dataset for positivity can be related to information such as power / phase / angle (Angle of arrival, Angle of departure) for each path / sample, which is measured / estimated using PRS / SRS, etc.

[0270] In the description below, entities and nodes may be used interchangeably depending on the context. An entity that measures / reports data / data sets to a data collection entity (e.g., a base station or a separate network node) may be, for example, but is not limited to, a terminal. For example, a base station or a PRU node (e.g., a TRP or terminal configured as a PRU) may measure / report positioning-related data / data sets to an LMF. Additionally, a base station may measure / provide data / data sets to a terminal for CSI compression. For convenience, the entity that measures / reports data / data sets is referred to as a first device, and the entity that receives them is referred to as a second device. For example, the second device may be a data collection entity and / or a training entity. The data collection entity may be configured separately from, or identical to, a training entity that trains an AI / ML model based on the data / data sets. The second device may not necessarily be a single entity, and may be used to mean, for example, at least one of the data collection entity and the training entity. For example, in the description below, the expression that the first device transmits information A to the second device and the first device receives information B from the second device may be used to encompass the case where the first device transmits information A to the data collection entity and receives information B from the training entity, or vice versa. This interpretation of the second device can also be applied to the expression for the first device.

[0271] Proposal 1

[0272] The training entity and / or data collection entity may provide information about at least one of criteria (e.g., metric and / or threshold), data / dataset format and / or reference dataset related to data collection / classification to nodes (e.g., UE, positioning resource unit (PRU), base station) that measure / estimate data.

[0273] - The first device (e.g., UE, PRU or base station) that measures / estimates data can select / classify a dataset that satisfies the criteria based on information instructed / set from the training entity / base station, and report / deliver / transfer the dataset to the second device (e.g., data collection entity / training entity).

[0274] When reporting data / data sets, the first device can also report data omission rate / dropping rate / data quality information about the data / data set composition.

[0275] - Reference dataset can be, for example, a reference dataset representing a specific site / configuration / cell, or can be delivered based on a standardized / predefined dataset format and / or dataset ID.

[0276] Below, we describe specific examples of Proposal 1 mentioned above.

[0277] A data collection entity may be an entity that collects data / data sets used for training / inference / monitoring / update of AI / ML based on reference resources (e.g., SSB, CSI-RS, PRS), or raw measurements before being processed into input data. The data collection entity may be an entity existing on the network (NW) side / UE side. For example, if the data collection entity is on the NW side, it may be an entity that includes NWDAF (NetWork Data Analytic Function). In addition, the training entity and the data collection entity may be the same. Alternatively, the first device that measures / measures data may be the data collection entity.

[0278] In the above proposal 1, the data collection / classification criteria are indicators / information that can be instructed to the first device(s) that measure / measure data in order to collect good quality data. For example, the data collection / classification criteria can be a statistic / distribution value such as variance / mean of the measured / collected data samples, or a threshold that restricts the value above / below a specific value (e.g., (when measuring data samples) RSRP / SNR / SINR). The margin value of such statistic / distribution values ​​can be additionally set and instructed. In addition, the first device that performs data measurement can store the evaluation value for each sample that satisfies the criteria in the buffer / memory when measuring / estimating / buffering the data sample that satisfies the criteria, and can transmit the evaluation value separately from or together with the corresponding data sample when reporting to the second device (e.g., data collecting entity). For example, if the SNR / SINR / RSRP threshold is specified as a criterion, the first device can transmit the SNR / SINR / RSRP measurement value of the collected sample as the evaluation value.

[0279] For example, the data / dataset format may be a format for such measurements and / or reports when the first device measures data and reports it to a data collection entity, and the formats of the measurements and / or reports may be different. For example, measurements may be made in raw channel format with Nr-by-Nt size, but reports may be made using data formats with different dimensions, such as Nt-by-rank, or the granularity of quantization used in measurements and reports may be different. Additionally, time information (e.g., time stamp) of such data / dataset measurements may be included in the data / dataset format or measured / reported separately.

[0280] For example, a reference dataset is a reference dataset that can be referenced when measuring / reporting data. The base station / network can set / instruct the first device (e.g., measurement node) in advance about a dataset suitable for the cell / site / configuration. Alternatively, if there is a standardized / predefined dataset, the reference dataset can be indicated using an indicator / identifier representing it. In general, NW configuration (e.g., N_ports, cell type, power, etc.), scenario / cell-related ID, etc. can be set and instructed to the first device (e.g., data measurement / estimation node), and the first device can measure / estimate / buffer data / dataset with high similarity based on the set information.

[0281] When the first devices (e.g., measurement nodes) perform a report to the second device (e.g., data collection entity), information related to data omission rate / dropping rate / data quality and / or assistant information therefor may be additionally reported to the second device (e.g., data collection entity). Here, the data omission (dropping) rate may refer to the ratio of data that is excluded / not selected based on the criteria set / instructed to the first device (e.g., data measurement node). For example, the data omission (dropping) rate may be ratio information such as [number of selected data samples / total number of measured data samples]. In the case of data quality information, the first device may report data quality information using information that can represent the environment at the time of data measurement (e.g., RSRP, SNR, SINR, CQI (of RS used during data measurement)), or the first device may determine / calculate the data quality information and report it as a soft value between

[0001] . This information can be used by data collection nodes to select measurement nodes or to manage data / datasets.

[0282] Meanwhile, rather than being instructed / set to the first device by the network (e.g., base station / training entity) for data selection criteria, the first device may determine / create and use the criteria on its own. For example, the terminal may determine / create criteria (e.g., mean, variance, threshold) based on past historical data measurements in a buffer / memory, and may measure / select data / datasets based on these criteria and report the results to a second device (e.g., data collection entity). Furthermore, when reporting the data, information regarding the criteria used / determined by the first device may also be reported to the second device (e.g., data collection entity).

[0283] In the process of performing a data collection procedure based on the above proposal 1, the first device can transmit / report the measured / stored / archived data / dataset to the second device (e.g., data collection entity), and such transmission / reporting can be performed based on at least one of the following options.

[0284] - Option 1: Transmission / reporting based on a second device (e.g., training entity / data collection entity) trigger

[0285] Here, the trigger of the second device can be Periodic / Semi-persistent / Aperiodic triggering.

[0286] - Option 2: Transmission / reporting based on the first device (e.g., data measurement node) / event trigger

[0287] Option 1 is a method in which a second device triggers reporting to a first device (e.g., data measurement nodes) periodically / semi-periodically / aperiodically for data collection purposes. For periodic / semi-periodic triggering, a specific (data measurement) timing window can be introduced, and based on this window, the first device (e.g., data measurement nodes) can report data measurement samples buffered / stored since the previous report. For reporting, specific resource(s) (e.g., PUSCH / PUCCH resources) can be allocated. For aperiodic reporting, the second device (e.g., training entity / data collection entity) can trigger reporting as needed, or the second device can determine whether to trigger aperiodic reporting based on buffer / memory status information reported by the first device (e.g., data measurement node). As an example for this, a first device (e.g., data measurement nodes) can report the buffer / memory status information to a second device (e.g., training entity / data collection entity) periodically / semi-periodically / aperiodically. After the report, the first device (e.g., data measurement nodes) can flush out the corresponding buffer / memory.

[0288] For Option 2, the first devices (e.g., data measurement nodes) can determine whether to transmit data / data sets to the second devices (e.g., training entities / data collection entities), i.e., can trigger transmission on their own. For example, events such as the memory of the first device (e.g., data measurement node) being occupied beyond a threshold (e.g., fully occupied) or the measurement being performed for a pre-agreed / set number of data samples can be considered events and transmitted to the second device (e.g., training entities / data collection entities). For transmission, transmission resources can be set in advance and transmission can be performed using the corresponding resources only when an event occurs, or when the first device requests a resource when an event occurs and the corresponding resource is scheduled, transmission can be performed using the corresponding resource.

[0289] In the above option 1 / 2, if the memory / buffer of the first device (e.g., data measurement nodes) is occupied more than a threshold (e.g., fully occupied), it can operate based on at least one of the following options.

[0290] (a) The first device can stop Measurement for data collection, perform a report, or request the network to allocate resources for the report.

[0291] (b) The first device discards the most outdated data (e.g., clears it from the buffer), buffers / memorizes the new (valid) measurement, and continues the measurement.

[0292] (c) The first device discards (e.g., deletes from the buffer) data in the order of measurements associated with low values ​​of a specific indicator (e.g., an indicator based on measurement accuracy or confidence level) among the buffered / memorized data / dataset, and continues to perform the measurement by buffering / memorizing a new (valid) measurement.

[0293] Additionally, the first device can report its capabilities for measuring data samples (e.g., measurement accuracy capability, memory / buffer capability) to the network (e.g., data collection entity / training entity / BS) in the form of capability reports. These capability reports can be utilized for data classification / selection by the first device (e.g., terminal).

[0294] Additionally, the mechanism introduced in MDT (minimization drive test) can be utilized. Measurement and data collection are necessary for wireless network optimization, and there was a conventional method of performing these using vehicles, etc. In the case of MDT, measurements are performed using terminals (to save time / cost) rather than through drive tests using actual vehicles. The terminals perform and report these measurements (e.g., measurements of various network performance (e.g., Cell Power, Interference) or UE performance (e.g., Call Drop, Throughput, Handover performance, Cell Reselection Performance, etc.), and the base station can utilize these measurements to optimize base station functionality or identify any problems.

[0295] A method of (re)utilizing such MDT for Proposal 1 can be considered. In performing data collection, the contents measured by the first devices (e.g., measurement nodes) may include (i) (in the case of BM) RSRP per beam, (ii) (in the case of positioning) power / PDP / DP / phase per PRS path, (iii) (in the case of CSI) raw channel matrix or its representation (e.g., precoder (eigenvector), etc.), depending on the AI / ML use case. If the terminal is equipped with AI / ML model / functionality, the initial inference output / intermediate KPI / Monitoring-related information (e.g., monitoring metric / output) utilizing the above measurements can also be reported for fast model selection, etc.

[0296] Proposal 2

[0297] For example, a specific node may report / transfer / deliver synthetic data generated based on AI / ML as data / dataset samples to a second device (e.g., data collection entity / training entity). Accordingly, when a specific node transmits data / dataset samples, it may report / transfer / deliver to a second device (e.g., data collection entity / training entity) by distinguishing / indicating whether the data / dataset samples are based on (real-world) real measurements or are (or include) synthetic data samples generated based on AI / ML.

[0298] Additionally, if synthetic data / dataset is transmitted, at least one of the reliability / confidence level of the synthetic data / dataset and / or the difference (from a statistical point of view, e.g., mean / variance) with the real data / datasets may be additionally reported.

[0299] AI / ML-based synthetic data generation is a method / process for generating artificial data that mimics the statistical patterns and properties of real-world data using algorithms, models, and other technologies. Typically, real data samples are used for synthetic data generation, and the final output, synthetic data, may not include real input data. The advantages of this type of synthetic data generation are that, unlike real-world data that may contain sensitive or personally identifiable information, synthetic data / datasets can guarantee privacy protection and reduce the burden of measuring large amounts of real-world data / datasets. Representative examples of this type of synthetic data generation include generative AI modeled using Generative Pre-trained Transformers (GPTs), Generative Adversarial Networks (GANs), or Variational Auto-Encoders (VAEs).

[0300] From the perspective of triggering a report based on Proposal 2, a second device (e.g., training node / data collection node) can receive data / dataset by setting / instructing one of synthetic data or real data (or both, in this case, additionally information on a portion of both) to a first device (e.g., measurement node).

[0301] Meanwhile, in Proposal 2, the specific node generating synthetic data may be a data measurement node, a data collection entity, or a training entity. In such cases, reporting or related instructions regarding the selection of synthetic or real data may be signaling between multiple nodes / entities. Examples include the following cases:

[0302] - Case 1: Signaling between data collection node and training node

[0303] - Case 2: Signaling between training nodes and inference nodes

[0304] Case 1 can be used to review errors that may occur during training. Case 2 can be used as assistant information for monitoring inference performance and taking follow-up actions (e.g., updating, fine-tuning).

[0305] In the above proposal 2, the input data of AI / ML that generates synthetic data is not limited to real measurement values, and values ​​related to the terminal environment during measurement (e.g., terminal location / speed, SNR, SINR, RSRP, cell ID, zone ID, configuration ID) can also be used as AI / ML input data as assistant information.

[0306] When performing the above AI / ML-based synthetic data generation, as explained above, there may be a problem of degraded model performance due to the use of incorrect data. Therefore, additional management may be required for AI / ML models that have been trained / re-trained / updated using such synthetic data. To this end, a specific AI / ML model can be managed and distinguished based on whether it has been trained / re-trained / updated solely with synthetic data or trained / re-trained / updated using data included in the synthetic data. An example of such distinction / management is that different identifiers or different model pools can be distinguished / managed. A specific example is the following pool, which can be managed as follows.

[0307] - Model pool#1: Models based on full real measurements

[0308] - Model pool#2: Models based on real measurement + synthetic data / dataset

[0309] - Model pool#3: Models based on synthetic data / dataset

[0310] For example, models in model pool #3 may be restricted to undergo fine-tuning with real data / datasets before actual inference, and / or may be given a low priority level when performing operations such as model selection / activation / switching. Alternatively, models in model pool #3 may be given a low priority for inference reporting (e.g., CSI priority / CSI omission in CSI report).

[0311] Conversely, in cases where real data measurements are very weak (e.g., low SNR UEs), training based on synthetic data may actually yield better performance. In such cases, AI / ML models in model pool #3 may be given higher priority. Alternatively, the base station can set / instruct the priorities for each model pool.

[0312] To prevent data / dataset corruption and AI / ML model performance degradation due to malicious data measurement / collection nodes, monitoring information on data / datasets can be reported periodically / semi-periodically / aperiodically to training entities / data collection entities / BSs. Monitoring information can be statistical information on data over a specific time period or measurement period. Thresholds and / or criteria for monitoring can also be set. A separate time window / timer can be set for monitoring. In addition, a separate authentication procedure can be introduced to identify such malicious nodes. For example, the location and data measurement results can be compared with those of (authenticated) terminals with similar channel environments to determine whether the measurement of the corresponding node is appropriate.

[0313] The above suggestions 1 and 2 can be applied alone or in combination.

[0314] FIG. 19 is a diagram illustrating the operation of a terminal and a network (e.g., a base station / data collection entity / training entity) according to one embodiment. FIG. 19 may correspond to an implementation example of terminal and network operation based on at least a portion of Proposals 1 and 2. In FIG. 19, the terminal may be a first device that measures data / dataset.

[0315] Referring to Figure 19, a terminal can transmit a terminal capability report to the network (A05). The terminal capability report can include information regarding data / dataset measurement capability.

[0316] The terminal may receive configuration information(s) from the network (A10). The configuration information may include configuration information related to data collection (e.g., measurement time interval and measurable criteria) and / or configuration information regarding RSs (e.g., SSB, CSI-RS, PRS) to be transmitted for data measurement / collection.

[0317] The network can transmit RS to the terminal (A15).

[0318] The terminal can perform data measurement based on RS and store it in memory / buffer (A20).

[0319] The terminal can report the results of data measurements stored in memory / buffer to the network (A25).

[0320] Meanwhile, if data reporting is triggered by the network, the network may transmit a signal to the terminal for triggering. Additionally, the network may transmit uplink grant information to the terminal, allocating resources for data reporting.

[0321] Depending on the implementation, (some) specific steps in the above terminal / network operations may be omitted.

[0322] FIG. 20 illustrates a flowchart of a method performed in a first device according to one embodiment. The first device may be a terminal.

[0323] Referring to FIG. 20, the first device can receive settings for data to be reported by the first device from the second device (B05).

[0324] The first device can perform measurements to obtain the data based on the above settings (B10).

[0325] The first device can report data obtained through the above measurement to the second device (B15).

[0326] Data obtained through the above measurement may be stored in a buffer of the first device. The first device may stop the measurement based on whether the buffer has been used beyond a threshold and report the data stored in the buffer to the second device.

[0327] The above data may be data for training, retraining, or updating an AI / ML (Artificial intelligence / machine learning) model.

[0328] The first device may request resource allocation from the second device based on whether the buffer has been used beyond a threshold. In response to the request, the data may be reported through the allocated resources.

[0329] The above settings may include criteria related to data quality.

[0330] The first device can select data samples that satisfy the above criteria from among all data samples included in the data acquired through the measurement and report the data samples to the second device.

[0331] The report of the above data may include quality assessment information of the selected data samples.

[0332] The report of the above data may include a ratio between the total data samples and the selected data samples.

[0333] The above settings may include information about a reference data set to be referenced by the first device for measuring and reporting the data.

[0334] Information about the above reference data set may be an ID of the above reference data set.

[0335] The above measurement may include at least one of a channel measurement for channel state information (CSI), a reference signal received power (RSRP) measurement for beam management, or a positioning reference signal (PRS) measurement for positioning.

[0336] The second device may be a base station, a network node that collects the data for training an AI / ML (Artificial intelligence / machine learning) model, or a network node that trains the AI / ML model based on the data.

[0337] Figure 21 illustrates a flowchart of a method performed in a second device according to one embodiment. The second device may be a base station, a network node that collects the data for training an AI / ML (Artificial intelligence / machine learning) model, or a network node that trains the AI / ML model based on the data.

[0338] Referring to FIG. 21, the second device can transmit settings for data reporting of the first device to the first device (C05).

[0339] The second device can receive the data report based on the results of the measurement performed by the first device (C10).

[0340] The second device may, through the above settings, request the first device to select data samples that satisfy criteria related to data quality from among all data samples included in the measurement results and configure the data report. For example, the second device may request this by including criteria related to data quality in the above settings.

[0341] The above data samples may be stored in a buffer of the first device. The first device may stop the measurement based on the buffer being used beyond a threshold and report the data samples stored in the buffer to the second device.

[0342] The above data samples may be for training, retraining, or updating an AI / ML (Artificial intelligence / machine learning) model.

[0343] The second device may receive a resource allocation request from the first device when the buffer of the first device is used beyond a threshold. The second device may allocate resources in response to the resource allocation request and thereby receive data reports.

[0344] The above data report may include quality assessment information of the selected data samples.

[0345] The above data report may include a ratio between the total data samples and the selected data samples.

[0346] The above settings may include information about a reference data set to be referenced by the first device for data measurement and reporting.

[0347] Information about the above reference data set may be an ID of the above reference data set.

[0348] The above measurement may include at least one of a channel measurement for channel state information (CSI), a reference signal received power (RSRP) measurement for beam management, or a positioning reference signal (PRS) measurement for positioning.

[0349] Fig. 22 illustrates a communication system (1) applicable to the present disclosure.

[0350] Referring to FIG. 22, a communication system (1) includes a wireless device, a base station, and a network. Here, the wireless device refers to a device that performs communication using a wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution)) and may be referred to as a communication / wireless / 5G device. Although not limited thereto, the wireless device may include a robot (100a), a vehicle (100b-1, 100b-2), an XR (eXtended Reality) device (100c), a hand-held device (100d), a home appliance (100e), an IoT (Internet of Things) device (100f), and an AI device / server (400). For example, the vehicle may include a vehicle equipped with a wireless communication function, an autonomous vehicle, a vehicle capable of performing vehicle-to-vehicle communication, etc. Here, the vehicle may include an Unmanned Aerial Vehicle (UAV) (e.g., a drone). XR devices include AR (Augmented Reality) / VR (Virtual Reality) / MR (Mixed Reality) devices, and can be implemented in the form of HMD (Head-Mounted Device), HUD (Head-Up Display) installed in a vehicle, television, smartphone, computer, wearable device, home appliance, digital signage, vehicle, robot, etc. Mobile devices can include smartphone, smart pad, wearable device (e.g., smart watch, smart glass), computer (e.g., laptop, etc.), etc. Home appliances can include TV, refrigerator, washing machine, etc. IoT devices can include sensors, smart meters, etc. For example, base stations and networks can also be implemented as wireless devices, and a specific wireless device (200a) can act as a base station / network node to other wireless devices.

[0351] Wireless devices (100a to 100f) can be connected to a network (300) via a base station (200). Artificial Intelligence (AI) technology can be applied to the wireless devices (100a to 100f), and the wireless devices (100a to 100f) can be connected to an AI server (400) via the network (300). The network (300) can be configured using a 3G network, a 4G (e.g., LTE) network, a 5G (e.g., NR) network, etc. The wireless devices (100a to 100f) can communicate with each other via the base station (200) / network (300), but can also communicate directly (e.g., sidelink communication) without going through the base station / network. For example, vehicles (100b-1, 100b-2) can communicate directly (e.g., V2V (Vehicle to Vehicle) / V2X (Vehicle to Everything) communication). In addition, IoT devices (e.g., sensors) can communicate directly with other IoT devices (e.g., sensors) or other wireless devices (100a to 100f).

[0352] Wireless communication / connection (150a, 150b, 150c) can be established between wireless devices (100a~100f) / base stations (200), and base stations (200) / base stations (200). Here, wireless communication / connection can be achieved through various wireless access technologies (e.g., 5G NR) such as uplink / downlink communication (150a), sidelink communication (150b) (or D2D communication), and communication between base stations (150c) (e.g., relay, IAB (Integrated Access Backhaul). Through wireless communication / connection (150a, 150b, 150c), wireless devices and base stations / wireless devices, and base stations and base stations can transmit / receive wireless signals to each other. For example, wireless communication / connection (150a, 150b, 150c) can transmit / receive signals through various physical channels. To this end, at least some of various configuration information setting processes for transmitting / receiving wireless signals, various signal processing processes (e.g., channel encoding / decoding, modulation / demodulation, resource mapping / demapping, etc.), and resource allocation processes can be performed based on various proposals of the present disclosure.

[0353] Figure 23 illustrates a wireless device applicable to the present disclosure.

[0354] Referring to FIG. 23, the first wireless device (100) and the second wireless device (200) can transmit and receive wireless signals through various wireless access technologies (e.g., LTE, NR). Here, {the first wireless device (100), the second wireless device (200)} can correspond to {the wireless device (100x), the base station (200)} and / or {the wireless device (100x), the wireless device (100x)} of FIG. 22.

[0355] A first wireless device (100) includes one or more processors (102) and one or more memories (104), and may further include one or more transceivers (106) and / or one or more antennas (108). The processor (102) controls the memories (104) and / or the transceivers (106), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document. For example, the processor (102) may process information in the memory (104) to generate first information / signal, and then transmit a wireless signal including the first information / signal via the transceiver (106). In addition, the processor (102) may receive a wireless signal including second information / signal via the transceiver (106), and then store information obtained from signal processing of the second information / signal in the memory (104). The memory (104) may be connected to the processor (102) and may store various information related to the operation of the processor (102). For example, the memory (104) may perform some or all of the processes controlled by the processor (102), or may store software code including commands for performing the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. Here, the processor (102) and the memory (104) may be part of a communication modem / circuit / chip designed to implement a wireless communication technology (e.g., LTE, NR). The transceiver (106) may be connected to the processor (102) and may transmit and / or receive wireless signals via one or more antennas (108). The transceiver (106) may include a transmitter and / or a receiver. The transceiver (106) may be used interchangeably with an RF (Radio Frequency) unit. In the present disclosure, a wireless device may also mean a communication modem / circuit / chip.

[0356] The second wireless device (200) includes one or more processors (202), one or more memories (204), and may further include one or more transceivers (206) and / or one or more antennas (208). The processor (202) controls the memories (204) and / or the transceivers (206), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document. For example, the processor (202) may process information in the memory (204) to generate third information / signals, and then transmit a wireless signal including the third information / signals via the transceivers (206). Furthermore, the processor (202) may receive a wireless signal including fourth information / signals via the transceivers (206), and then store information obtained from signal processing of the fourth information / signals in the memory (204). The memory (204) may be connected to the processor (202) and may store various information related to the operation of the processor (202). For example, the memory (204) may perform some or all of the processes controlled by the processor (202), or may store software code including commands for performing the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. Here, the processor (202) and the memory (204) may be part of a communication modem / circuit / chip designed to implement wireless communication technology (e.g., LTE, NR). The transceiver (206) may be connected to the processor (202) and may transmit and / or receive wireless signals via one or more antennas (208). The transceiver (206) may include a transmitter and / or a receiver. The transceiver (206) may be used interchangeably with an RF unit. In the present disclosure, a wireless device may also mean a communication modem / circuit / chip.

[0357] Hereinafter, the hardware elements of the wireless device (100, 200) will be described in more detail. Although not limited thereto, one or more protocol layers may be implemented by one or more processors (102, 202). For example, one or more processors (102, 202) may implement one or more layers (e.g., functional layers such as PHY, MAC, RLC, PDCP, RRC, SDAP). One or more processors (102, 202) may generate one or more Protocol Data Units (PDUs) and / or one or more Service Data Units (SDUs) according to the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. One or more processors (102, 202) may generate messages, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. One or more processors (102, 202) can generate signals (e.g., baseband signals) including PDUs, SDUs, messages, control information, data or information according to the functions, procedures, proposals and / or methods disclosed herein, and provide the signals to one or more transceivers (106, 206). One or more processors (102, 202) can receive signals (e.g., baseband signals) from one or more transceivers (106, 206) and obtain PDUs, SDUs, messages, control information, data or information according to the descriptions, functions, procedures, proposals, methods and / or operational flowcharts disclosed herein.

[0358] One or more processors (102, 202) may be referred to as a controller, a microcontroller, a microprocessor, or a microcomputer. One or more processors (102, 202) may be implemented by hardware, firmware, software, or a combination thereof. For example, one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), one or more Digital Signal Processing Devices (DSPDs), one or more Programmable Logic Devices (PLDs), or one or more Field Programmable Gate Arrays (FPGAs) may be included in one or more processors (102, 202). The descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, etc. The descriptions, functions, procedures, suggestions, methods and / or operation flowcharts disclosed in this document may be implemented using firmware or software configured to perform one or more processors (102, 202) or stored in one or more memories (104, 204) and executed by one or more processors (102, 202). The descriptions, functions, procedures, suggestions, methods and / or operation flowcharts disclosed in this document may be implemented using firmware or software in the form of codes, instructions and / or sets of instructions.

[0359] One or more memories (104, 204) may be coupled to one or more processors (102, 202) and may store various forms of data, signals, messages, information, programs, codes, instructions, and / or commands. The one or more memories (104, 204) may be configured as ROM, RAM, EPROM, flash memory, hard drives, registers, cache memory, computer-readable storage media, and / or combinations thereof. The one or more memories (104, 204) may be located internally and / or externally to the one or more processors (102, 202). Additionally, the one or more memories (104, 204) may be coupled to the one or more processors (102, 202) via various technologies, such as wired or wireless connections.

[0360] One or more transceivers (106, 206) can transmit user data, control information, wireless signals / channels, etc., as mentioned in the methods and / or flowcharts of this document, to one or more other devices. One or more transceivers (106, 206) can receive user data, control information, wireless signals / channels, etc., as mentioned in the descriptions, functions, procedures, proposals, methods and / or flowcharts of this document, from one or more other devices. For example, one or more transceivers (106, 206) can be connected to one or more processors (102, 202) and can transmit and receive wireless signals. For example, one or more processors (102, 202) can control one or more transceivers (106, 206) to transmit user data, control information, or wireless signals to one or more other devices. Additionally, one or more processors (102, 202) may control one or more transceivers (106, 206) to receive user data, control information, or wireless signals from one or more other devices. Additionally, one or more transceivers (106, 206) may be coupled to one or more antennas (108, 208), and one or more transceivers (106, 206) may be configured to transmit and receive user data, control information, wireless signals / channels, or the like, as referred to in the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed herein, via one or more antennas (108, 208). In this document, one or more antennas may be multiple physical antennas or multiple logical antennas (e.g., antenna ports). One or more transceivers (106, 206) can convert received user data, control information, wireless signals / channels, etc. from RF band signals to baseband signals in order to process the received user data, control information, wireless signals / channels, etc. using one or more processors (102, 202).One or more transceivers (106, 206) may convert user data, control information, wireless signals / channels, etc. processed by one or more processors (102, 202) from baseband signals to RF band signals. For this purpose, one or more transceivers (106, 206) may include an (analog) oscillator and / or filter.

[0361] Figure 24 illustrates another example of a wireless device applicable to the present disclosure. The wireless device may be implemented in various forms depending on the use case / service (see Figure 22).

[0362] Referring to FIG. 24, the wireless device (100, 200) corresponds to the wireless device (100, 200) of FIG. 23 and may be composed of various elements, components, units / units, and / or modules. For example, the wireless device (100, 200) may include a communication unit (110), a control unit (120), a memory unit (130), and an additional element (140). The communication unit may include a communication circuit (112) and a transceiver(s) (114). For example, the communication circuit (112) may include one or more processors (102, 202) and / or one or more memories (104, 204) of FIG. 23. For example, the transceiver(s) (114) may include one or more transceivers (106, 206) and / or one or more antennas (108, 208) of FIG. 23. The control unit (120) is electrically connected to the communication unit (110), the memory unit (130), and the additional elements (140) and controls the overall operation of the wireless device. For example, the control unit (120) may control the electrical / mechanical operation of the wireless device based on the program / code / command / information stored in the memory unit (130). In addition, the control unit (120) may transmit information stored in the memory unit (130) to an external device (e.g., another communication device) via a wireless / wired interface through the communication unit (110), or store information received from an external device (e.g., another communication device) via a wireless / wired interface in the memory unit (130).

[0363] The additional element (140) may be configured in various ways depending on the type of the wireless device. For example, the additional element (140) may include at least one of a power unit / battery, an input / output (I / O) unit, a driving unit, and a computing unit. Although not limited thereto, the wireless device may be implemented in the form of a robot (Fig. 22, 100a), a vehicle (Fig. 22, 100b-1, 100b-2), an XR device (Fig. 22, 100c), a portable device (Fig. 22, 100d), a home appliance (Fig. 22, 100e), an IoT device (Fig. 22, 100f), a digital broadcasting terminal, a hologram device, a public safety device, an MTC device, a medical device, a fintech device (or a financial device), a security device, a climate / environmental device, an AI server / device (Fig. 22, 400), a base station (Fig. 22, 200), a network node, etc. Wireless devices may be mobile or stationary depending on the use / service.

[0364] In FIG. 24, various elements, components, units / parts, and / or modules within the wireless device (100, 200) may be entirely interconnected via a wired interface, or at least some may be wirelessly connected via a communication unit (110). For example, within the wireless device (100, 200), the control unit (120) and the communication unit (110) may be wired, and the control unit (120) and a first unit (e.g., 130, 140) may be wirelessly connected via the communication unit (110). In addition, each element, component, unit / part, and / or module within the wireless device (100, 200) may further include one or more elements. For example, the control unit (120) may be composed of a set of one or more processors. For example, the control unit (120) may be composed of a set of a communication control processor, an application processor, an electronic control unit (ECU), a graphics processing processor, a memory control processor, etc. As another example, the memory unit (130) may be composed of RAM (Random Access Memory), DRAM (Dynamic RAM), ROM (Read Only Memory), flash memory, volatile memory, non-volatile memory, and / or a combination thereof.

[0365] Figure 25 illustrates a vehicle or autonomous vehicle applicable to the present disclosure. The vehicle or autonomous vehicle may be implemented as a mobile robot, a car, a train, a manned or unmanned aerial vehicle (AV), a ship, or the like.

[0366] Referring to FIG. 25, a vehicle or autonomous vehicle (100) may include an antenna unit (108), a communication unit (110), a control unit (120), a driving unit (140a), a power supply unit (140b), a sensor unit (140c), and an autonomous driving unit (140d). The antenna unit (108) may be configured as a part of the communication unit (110). Blocks 110 / 130 / 140a to 140d correspond to blocks 110 / 130 / 140 of FIG. 24, respectively.

[0367] The communication unit (110) can transmit and receive signals (e.g., data, control signals, etc.) with external devices such as other vehicles, base stations (e.g., base stations, road side units, etc.), and servers. The control unit (120) can control elements of the vehicle or autonomous vehicle (100) to perform various operations. The control unit (120) can include an ECU (Electronic Control Unit). The drive unit (140a) can drive the vehicle or autonomous vehicle (100) on the ground. The drive unit (140a) can include an engine, a motor, a power train, wheels, brakes, a steering device, etc. The power supply unit (140b) supplies power to the vehicle or autonomous vehicle (100) and can include a wired / wireless charging circuit, a battery, etc. The sensor unit (140c) can obtain vehicle status, surrounding environment information, user information, etc. The sensor unit (140c) may include an IMU (inertial measurement unit) sensor, a collision sensor, a wheel sensor, a speed sensor, an incline sensor, a weight detection sensor, a heading sensor, a position module, a vehicle forward / backward sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor, a temperature sensor, a humidity sensor, an ultrasonic sensor, an illuminance sensor, a pedal position sensor, etc. The autonomous driving unit (140d) may implement a technology for maintaining a driving lane, a technology for automatically controlling speed such as adaptive cruise control, a technology for automatically driving along a set path, a technology for automatically setting a path and driving when a destination is set, etc.

[0368] For example, the communication unit (110) can receive map data, traffic information data, etc. from an external server. The autonomous driving unit (140d) can generate an autonomous driving route and driving plan based on the acquired data. The control unit (120) can control the drive unit (140a) so that the vehicle or autonomous vehicle (100) moves along the autonomous driving route according to the driving plan (e.g., speed / direction control). During autonomous driving, the communication unit (110) can irregularly / periodically acquire the latest traffic information data from an external server and can acquire surrounding traffic information data from surrounding vehicles. In addition, during autonomous driving, the sensor unit (140c) can acquire vehicle status and surrounding environment information. The autonomous driving unit (140d) can update the autonomous driving route and driving plan based on newly acquired data / information. The communication unit (110) can transmit information regarding the vehicle location, autonomous driving route, driving plan, etc. to the external server. External servers can predict traffic information data in advance using AI technology or other technologies based on information collected from vehicles or autonomous vehicles, and provide the predicted traffic information data to the vehicles or autonomous vehicles.

[0369] The embodiments described above are combinations of components and features of the present disclosure in a predetermined form. Each component or feature should be considered optional unless explicitly stated otherwise. Each component or feature may be implemented without being combined with other components or features. Furthermore, it is also possible to form embodiments of the present disclosure by combining some components and / or features. The order of operations described in the embodiments of the present disclosure may be changed. Some components or features of one embodiment may be included in another embodiment or may be replaced with corresponding components or features of another embodiment. It is self-evident that claims that do not have an explicit citation relationship in the patent claims may be combined to form embodiments or incorporated as new claims through post-application amendments.

[0370] It will be apparent to those skilled in the art that the present invention can be embodied in other specific forms without departing from the spirit or scope of the invention. Therefore, the above detailed description should not be construed as limiting in any respect, but rather as illustrative. The scope of the present invention should be determined by a reasonable interpretation of the appended claims, and all modifications within the scope of equivalents are intended to be included within the scope of the present invention.

[0371] The present disclosure may be used in a terminal, base station, or other equipment of a wireless mobile communication system.

Claims

1. In a method performed by a first device, Receive settings for data to be reported by the first device from the second device; Performing measurements to obtain the data based on the above settings; and Including reporting the data obtained through the above measurement to the second device, The data obtained through the above measurement is stored in the buffer of the first device, A method wherein the first device stops the measurement based on the buffer being used beyond a threshold and reports the data stored in the buffer to the second device.

2. In paragraph 1, The above data is a method for training, retraining or updating an AI / ML (Artificial intelligence / machine learning) model.

3. In paragraph 1, Further comprising requesting resource allocation to the second device based on the above buffer being used more than a threshold, A method in which said data is reported through allocated resources in response to said request.

4. In paragraph 1, The above settings include criteria related to data quality, A method in which the first device selects data samples that satisfy the above criteria from among all data samples included in the data acquired through the measurement and reports the data samples to the second device.

5. In paragraph 4, A method in which the report of the above data includes quality evaluation information of the selected data samples.

6. In paragraph 4, A method wherein the reporting of the above data includes a ratio between the entire data samples and the selected data samples.

7. In paragraph 1, A method wherein the above settings include information about a reference data set to be referenced by the first device for measuring and reporting the data.

8. In paragraph 7, Information about the above reference data set is the ID of the above reference data set, method.

9. In paragraph 1, A method wherein the above measurement comprises at least one of a channel measurement for channel state information (CSI), a reference signal received power (RSRP) measurement for beam management, or a positioning reference signal (PRS) measurement for positioning.

10. In paragraph 1, The above first device is a terminal, A method wherein the second device is a base station, a network node that collects the data for training an AI / ML (Artificial intelligence / machine learning) model, or a network node that trains the AI / ML model based on the data.

11. A non-transitory computer-readable recording medium having recorded thereon a program for performing the method described in paragraph 1.

12. In the first device, at least one processor; and At least one memory configured to store instructions that, when executed by said at least one processor, cause said at least one processor to perform operations; The operations of at least one processor are: In a method performed by a first device, Receive settings for data to be reported by the first device from the second device; Performing measurements to obtain the data based on the above settings; and Including reporting the data obtained through the above measurement to the second device, The data obtained through the above measurement is stored in a buffer configured in at least one memory, The first device, wherein the first device stops the measurement based on the buffer being used beyond a threshold and reports the data stored in the buffer to the second device.

13. In paragraph 12, A first device, wherein the first device is a terminal including a transceiver or a processing device configured to control the terminal.

14. In a method performed by a second device, Transmitting settings for data reporting of the first device to the first device; and Including receiving the data report based on the results of the measurement performed in the first device, A method in which the second device requests the first device to select data samples that satisfy criteria related to data quality among all data samples included in the result of the measurement through the above settings and to configure the data report.

15. In the second device, at least one processor; and At least one memory configured to store instructions that, when executed by said at least one processor, cause said at least one processor to perform operations; The operations of at least one processor are: Transmitting settings for data reporting of the first device to the first device; and Including receiving the data report based on the results of the measurement performed in the first device, The second device requests the first device to select data samples that satisfy criteria related to data quality among all data samples included in the results of the measurement through the above settings and to configure the data report.

Images