Method and apparatus for transmitting and receiving signal in wireless communication system

Abstract

According to at least one embodiment disclosed in the present specification, a method for reporting a channel state by a terminal in a wireless communication system may comprise: receiving a channel state information (CSI) related configuration; performing a plurality of interconnect CSI measurements based on the CSI-related configuration; and transmitting a CSI report based on the CSI measurements, in which the plurality of interconnected CSI measurements include a first CSI measurement performed based on an artificial intelligence / machine learning (AI / ML) model and a second CSI measurement performed without the AI / ML model, and the CSI report includes an output of the AI / ML model obtained by the first CSI measurement and channel quality indicator (CQI) information obtained by the second CSI measurement.

Description

Technical Field

[0001] The present disclosure relates to a wireless communication system, and more particularly, to a method and apparatus for transmitting / receiving uplink / downlink wireless signals in a wireless communication system. Background Art

[0002] Generally, wireless communication systems are developing to cover a wide range in various ways to provide communication services such as audio communication services, data communication services, etc. Wireless communication is a multiple access system that can support communication with multiple users by sharing available system resources (e.g., bandwidth, transmission power, etc.). For example, the multiple access system can be any one of a code division multiple access (CDMA) system, a frequency division multiple access (FDMA) system, a time division multiple access (TDMA) system, an orthogonal frequency division multiple access (OFDMA) system, and a single carrier frequency division multiple access (SC-FDMA) system. Summary of the invention

[0003] Technical issues

[0004] An object of the present disclosure is to provide a method and an apparatus for efficiently performing a wireless signal transmission / reception process.

[0005] Those skilled in the art will understand that the objectives that can be achieved using the present disclosure are not limited to those specifically described above, and the above and other objectives that can be achieved by the present disclosure will be more clearly understood from the following detailed description.

[0006] Technical Solution

[0007] According to an aspect, a method for reporting a channel state by a user equipment (UE) in a wireless communication system may include: receiving a channel state information (CSI) related configuration; based on the CSI related configuration, performing multiple CSI measurements associated with each other; and sending a CSI report based on the CSI measurement. The multiple associated CSI measurements may include a first CSI measurement performed based on an artificial intelligence / machine learning (AI / ML) model and a second CSI measurement performed without the AI / ML model, and the CSI report may include an output of the AI / ML model obtained from the first CSI measurement and channel quality indicator (CQI) information obtained from the second CSI measurement.

[0008] The CQI information may include a second CQI value obtained by applying a CQI offset to a first CQI value calculated from the second CSI measurement.

[0009] The CQI offset may be provided via network signaling.

[0010] Based on the first CSI measurement being triggered, the second CQI measurement associated with the first CSI measurement may be performed together with the first CSI measurement.

[0011] The first CSI measurement and the second CSI measurement may be performed respectively in a first CSI resource and a second CSI resource associated with each other.

[0012] The CSI configuration may include information for association of the first CSI resource and the second CSI resource.

[0013] A first CSI report including the output of the AI / ML model obtained from the first CSI measurement may be sent over a physical uplink shared channel (PUSCH).

[0014] A second CSI report including the CQI information obtained from the second CSI measurement may be sent through a physical uplink control channel (PUCCH).

[0015] The CSI report may be sent via a physical uplink channel.

[0016] The output of the AI / ML model configured for the UE may be provided as an input to an AI / ML model configured in a network.

[0017] The multiple associated CSI measurements may be counted as N CSI processes, and N may be a value equal to or greater than 1 and may be provided through network signaling.

[0018] According to another aspect, a processor-readable recording medium recording a program for executing the above-described method of reporting a channel status may be provided.

[0019] According to another aspect, a UE for performing the above method of reporting a channel status may be provided.

[0020] According to another aspect, a processing device for controlling a UE to perform the above method of reporting a channel status may be provided.

[0021] According to another aspect, a method for receiving a channel state report by a base station (BS) in a wireless communication system may include: sending a CSI-related configuration; and receiving multiple CSI measurements associated with each other based on the CSI-related configuration. The multiple associated CSI measurements may include a first CSI measurement performed based on an AI / ML model and a second CSI measurement performed without the AI / ML model, and the CSI report may include an output of the AI / ML model obtained from the first CSI measurement and a channel quality indicator CQI information obtained from the second CSI measurement.

[0022] According to another aspect, a processor-readable recording medium recording a program for executing the above-described method of receiving a channel status report may be provided.

[0023] According to another aspect, a BS for performing the above method of receiving a channel status report may be provided.

[0024] Beneficial Effects

[0025] According to the present disclosure, wireless signal transmission and reception can be efficiently performed in a wireless communication system.

[0026] Those skilled in the art will appreciate that the effects that can be achieved using the present disclosure are not limited to those specifically described above, and other advantages of the present disclosure will be more clearly understood from the following detailed description in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 Illustrated are physical channels used in a 3rd Generation Partnership Project (3GPP) system as an exemplary wireless communication system and a general signal transmission method using the same.

[0028] Figure 2 The radio frame structure is shown.

[0029] Figure 3 A resource grid of time slots is illustrated.

[0030] Figure 4 An exemplary mapping of physical channels in time slots is illustrated.

[0031] Figure 5 An exemplary physical downlink shared channel (PDSCH) and ACK / NACK transmission and reception process is illustrated.

[0032] Figure 6 An exemplary physical uplink shared channel (PUSCH) transmission process is illustrated.

[0033] Figure 7 An example of a channel state information (CSI) related process is shown.

[0034] Figure 8 A diagram used to illustrate the concepts of artificial intelligence / machine learning (AI / ML) / deep learning.

[0035] Figures 9 to 12 Various AI / ML models for deep learning are shown.

[0036] Fig.13 is a diagram illustrating split AI reasoning.

[0037] Fig.14is a diagram illustrating the framework of 3GPP Radio Access Network (RAN) intelligence.

[0038] Figures 15 to 17 Diagram of the AI ​​model training and inference environment.

[0039] Fig.18 is a diagram illustrating AI / ML based channel state information (CSI) feedback.

[0040] Fig.19 is a diagram illustrating a CSI process according to an embodiment.

[0041] Fig. 20 is a diagram illustrating operations of a user equipment (UE) according to an embodiment.

[0042] Fig.21 is a diagram illustrating an operation of a base station (BS) according to an embodiment.

[0043] Figure 22 to Figure 25 An example of the communication system 1 and the wireless device applied to the present disclosure is illustrated.

[0044] Fig.26 An exemplary discontinuous reception (DRX) operation suitable for use with the present disclosure is illustrated. DETAILED DESCRIPTION

[0045] Embodiments of the present disclosure are applicable to various wireless access technologies such as 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). CDMA can be implemented as a radio technology such as Universal Terrestrial Radio Access (UTRA) or CDMA2000. TDMA can be implemented as a radio technology such as Global System for Mobile Communications (GSM) / General Packet Radio Service (GPRS) / Enhanced Data Rates for GSM Evolution (EDGE). OFDMA can be implemented as a radio technology such as Institute of Electrical and Electronics Engineers (IEEE) 802.11 (Wireless Fidelity (Wi-Fi)), IEEE 802.16 (Worldwide Interoperability for Microwave Access (WiMAX)), IEEE 802.20, and Evolved UTRA (E-UTRA). UTRA is part of Universal Mobile Telecommunications System (UMTS). 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) is a part of Evolved UMTS (E-UMTS) using E-UTRA, LTE-Advanced (A) 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.

[0046] As more and more communication devices require greater communication capacity, enhanced mobile broadband communications relative to traditional radio access technologies (RATs) are needed. In addition, large-scale machine-type communications (MTC), which can provide various services anytime and anywhere by connecting multiple devices and objects, is another important issue to be considered in the next generation of communications. Discussions are also underway to design communication systems that take into account services / UEs that are sensitive to reliability and latency. Therefore, discussions are underway to introduce new radio access technologies that take into account enhanced mobile broadband communications (eMBB), massive MTC, and ultra-reliable low-latency communications (URLLC). In this disclosure, for simplicity, this technology will be referred to as NR (new radio or new RAT).

[0047] For the sake of brevity, 3GPP NR is mainly described, but the technical concept of the present disclosure is not limited thereto.

[0048] In the present disclosure, the term "setting" may be replaced with "configuration", and the two may be used interchangeably. In addition, conditional expressions (e.g., "if", "in the case of...", or "when") may be replaced by "based on..." or "under the state of...". In addition, the operation or software / hardware (SW / HW) configuration of the user equipment (UE) / base station (BS) may be derived / understood based on satisfying the corresponding conditions. When the processing of the receiving (or transmitting) side can be derived / understood from the processing of the transmitting (or receiving) side in the signal transmission / reception between the wireless communication device (e.g., BS and UE), its description may be omitted. For example, the signal determination / generation / encoding / transmission of the transmitting side may be understood as the signal monitoring reception / decoding / determination of the receiving side. In addition, when it is said that the UE performs (or does not perform) a specific operation, this may also be interpreted as the BS expecting / assuming (or not expecting / assuming) that the UE performs the specific operation. When it is said that the BS performs (or does not perform) a specific operation, this may also be interpreted as the UE expecting / assuming (or not expecting / assuming) that the BS performs the specific operation. In the following description, for the convenience of description, sections, implementations, examples, options, methods, schemes, etc. are distinguished and indexed from each other, which does not mean that each of them necessarily constitutes an independent invention or that each of them should only be implemented separately. Unless explicitly contradictory, it can be deduced / understood that at least part of the sections, implementations, examples, options, methods, schemes, etc. can be implemented in combination or can be omitted.

[0049] In a wireless communication system, a user equipment (UE) receives information from a base station (BS) through a downlink (DL) and transmits information to the BS through an uplink (UL). The information transmitted and received by the BS and the UE includes data and various control information, and includes various physical channels according to the type / purpose of the information transmitted and received by the UE and the BS.

[0050] Figure 1Physical channels used in the 3GPP NR system and a general signal transmission method using the same are shown.

[0051] When the UE is powered on again from a power-off state or enters a new cell, in step S101, the UE performs an initial cell search process (e.g., establishes synchronization with the BS). To this end, the UE receives a synchronization signal block (SSB) from the BS. The SSB includes a primary synchronization signal (PSS), a secondary synchronization signal (SSS), and a physical broadcast channel (PBCH). The UE establishes synchronization with the BS based on the PSS / SSS and obtains information such as a cell identity (ID). The UE can obtain broadcast information in the cell based on the PBCH. The UE can receive a DL reference signal (RS) during the initial cell search process to monitor the DL channel status.

[0052] After the initial cell search, the UE may acquire more specific system information by receiving a physical downlink control channel (PDCCH) and receiving a physical downlink shared channel (PDSCH) based on information of the PDCCH in step S102 .

[0053] The UE may perform a random access procedure to access the BS in steps S103 to S106. For random access, the UE may transmit a preamble to the BS on a physical random access channel (PRACH) (S103) and receive a response message to the preamble on a PDCCH and a PDSCH corresponding to the PDCCH (S104). In the case of contention-based random access, the UE may perform a contention resolution procedure by further transmitting a PRACH (S105) and receiving a PDCCH and a PDSCH corresponding to the PDCCH (S106).

[0054] After the aforementioned process, the UE may receive PDCCH / PDSCH (S107) and transmit a physical uplink shared channel (PUSCH) / physical uplink control channel (PUCCH) (S108) as a general downlink / uplink signal transmission process. The control information sent from the UE to the BS is called uplink control information (UCI). UCI includes hybrid automatic repeat and request acknowledgement / negative confirmation (HARQ-ACK / NACK), scheduling request (SR), channel state information (CSI), etc. CSI includes channel quality indicator (CQI), precoding matrix indicator (PMI), rank indicator (RI), etc. Although UCI is usually transmitted on PUCCH, UCI may be transmitted on PUSCH when control information and service data need to be transmitted simultaneously. In addition, UCI may be transmitted aperiodically through PUSCH according to the request / command of the network.

[0055] Figure 2The radio frame structure is shown. In NR, uplink transmission and downlink transmission are configured in frames. Each radio frame has a length of 10ms and is divided into two 5ms half frames (HF). Each half frame is divided into five 1ms subframes (SF). A subframe is divided into one or more time slots, and the number of time slots in a subframe depends on the subcarrier spacing (SCS). Depending on the cyclic prefix (CP), each time slot includes 12 or 14 orthogonal frequency division multiplexing (OFDM) symbols. When a normal CP is used, each time slot includes 14 OFDM symbols. When an extended CP is used, each time slot includes 12 OFDM symbols.

[0056] Table 1 exemplarily shows that the number of symbols per slot, the number of slots per frame, and the number of slots per subframe vary according to the SCS when a normal CP is used.

[0057] [Table 1]

[0058] *N slot symb : The number of symbols in a time slot

[0059] *N frame,u slot : Number of time slots in a frame

[0060] *N subframe,u slot : Number of time slots in a subframe

[0061] Table 2 shows that the number of symbols per slot, the number of slots per frame, and the number of slots per subframe vary according to the SCS when the extended CP is used.

[0062] [Table 2]

[0063]

[0064] The structure of the frame is only an example. The number of subframes, the number of time slots, and the number of symbols in a frame may vary.

[0065] In the NR system, OFDM parameter sets (e.g., SCS) may be configured differently for multiple cells aggregated for one UE. Therefore, the (absolute time) duration of a time resource (e.g., SF, time slot, or TTI) (referred to as a time unit (TU) for simplicity) consisting of the same number of symbols may be configured differently between aggregated cells. Here, the symbol may include an OFDM symbol (or CP-OFDM symbol) and an SC-FDMA symbol (or a discrete Fourier transform-spread-OFDM (DFT-s-OFDM) symbol).

[0066] Figure 3A resource grid showing a time slot. A time slot includes multiple symbols in the time domain. For example, when a normal CP is used, a time slot includes 14 symbols. However, when an extended CP is used, a time slot includes 12 symbols. A carrier includes multiple subcarriers in the frequency domain. A resource block (RB) is defined as a plurality of consecutive subcarriers (e.g., 12 consecutive subcarriers) in the frequency domain. A bandwidth part (BWP) may be defined as a plurality of consecutive physical RBs (PRBs) in the frequency domain and corresponds to a single parameter set (e.g., SCS, CP length, etc.). A carrier may include up to N (e.g., five) BWPs. Data communication may be performed via enabled BWPs, and only one BWP may be enabled for one UE. In a resource grid, each element is referred to as a resource element (RE), and one complex symbol may be mapped to each RE.

[0067] Figure 4 An exemplary mapping of physical channels in a time slot is shown. PDCCH may be sent in the DL control region, and PDSCH may be sent in the DL data region. PUCCH may be sent in the UL control region, and PUSCH may be sent in the UL data region. A guard period (GP) provides a time gap for a transmission mode to a reception mode switch or a reception mode to a transmission mode switch at the BS and the UE. Some symbols in a subframe at the time of a DL to UL switch may be configured as a GP.

[0068] The individual physical channels are described in more detail below.

[0069] PDCCH transmits DCI. For example, PDCCH (i.e., DCI) may carry information about the transmission format and resource allocation of the DL shared channel (DL-SCH), resource allocation information of the uplink shared channel (UL-SCH), paging information about the paging channel (PCH), system information about DL-SCH, information about resource allocation of high-level control messages (e.g., RAR sent on PDSCH), transmit power control commands, information about the activation / release of the configured scheduling, etc. DCI includes a cyclic redundancy check (CRC). The CRC is masked with various identifiers (IDs) (e.g., radio network temporary identifier (RNTI)) according to the owner or purpose of the PDCCH. For example, if the PDCCH is for a specific UE, the CRC is masked by the UE ID (e.g., cell-RNTI (C-RNTI)). If the PDCCH is used for a paging message, the CRC is masked by the paging-RNTI (P-RNTI). If the PDCCH is for system information (eg, system information block (SIB)), the CRC is masked by the system information RNTI (SI-RNTI). When the PDCCH is for RAR, the CRC is masked by the random access-RNTI (RA-RNTI).

[0070] The PDCCH includes 1, 2, 4, 8 or 16 control channel elements (CCEs) depending on its aggregation level (AL). CCE is a logical allocation unit for providing a specific code rate to the PDCCH according to the radio channel state. CCE includes 6 resource element groups (REGs), each REG being defined by one OFDM symbol × one (P)RB. The PDCCH is transmitted in a control resource set (CORESET). A CORESET is defined as a set of REGs with a given parameter set (e.g., SCS, CP length, etc.). Multiple CORESETs for one UE may overlap with each other in the time / frequency domain. The CORESET may be configured by system information (e.g., Master Information Block (MIB)) or UE-specific high-level signaling (e.g., Radio Resource Control (RRC) signaling). Specifically, the number of RBs and the number of symbols (up to 3) in the CORESET may be configured by high-level signaling.

[0071] For PDCCH reception / detection, the UE monitors PDCCH candidates. PDCCH candidates are CCEs that the UE should monitor to detect PDCCH. Each PDCCH candidate is defined as 1, 2, 4, 8 or 16 CCEs depending on the AL. Monitoring includes (blind) decoding of the PDCCH candidates. The set of PDCCH candidates decoded by the UE is defined as the PDCCH search space (SS). The SS can be a common search space (CSS) or a UE-specific search space (USS). The UE can obtain DCI by monitoring PDCCH candidates in one or more SSs configured by the MIB or higher-layer signaling. Each CORESET is associated with one or more SSs, and each SS is associated with one CORESET. The SS can be defined based on the following parameters.

[0072] - controlResourceSetId: CORESET related to SS.

[0073] - monitoringSlotPeriodicityAndOffset: PDCCH monitoring periodicity (time slot) and PDCCH monitoring offset (time slot).

[0074] - monitoringSymbolsWithinSlot: PDCCH monitoring symbols within a slot (e.g., the first symbol of a CORESET).

[0075] - nrofCandidates: the number of PDCCH candidates (one of 0, 1, 2, 3, 4, 5, 6 and 8) for each AL={1, 2, 4, 8, 16}.

[0076] * The timing (eg, time / frequency resources) at which the UE is to monitor a PDCCH candidate is defined as a PDCCH (monitoring) timing. One or more PDCCH (monitoring) timings may be configured in a time slot.

[0077] Table 3 shows the characteristics of each SS.

[0078] [Table 3]

[0079]

[0080] Table 4 shows the DCI format transmitted on the PDCCH.

[0081] [Table 4]

[0082]

[0083] DCI format 0_0 may be used to schedule TB-based (or TB-level) PUSCH, and DCI format 0_1 ​​may be used to schedule TB-based (or TB-level) PUSCH or code block group (CBG)-based (or CBG-level) PUSCH. DCI format 1_0 may be used to schedule TB-based (or TB-level) PDSCH, and DCI format 1_1 may be used to schedule TB-based (or TB-level) PDSCH or CBG-based (or CBG-level) PDSCH (or 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 transmit dynamic slot format information (e.g., dynamic slot format indicator (SFI)) to the UE, and DCI format 2_1 is used to transmit DL preemption information to the UE. DCI format 2_0 and / or DCI format 2_1 may be transmitted to a corresponding group of UEs on a group common PDCCH (PDCCH pointing to a group of UEs).

[0084] 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. Under the fallback DCI format, the DCI size / field configuration remains the same regardless of the UE configuration. In contrast, under the non-fallback DCI format, the DCI size / field configuration varies according to the UE configuration.

[0085] PDSCH transmits DL data (e.g., DL shared channel transport block (DL-SCH TB)) and uses a modulation scheme such as quadrature phase shift keying (QPSK), 16-ary quadrature amplitude modulation (16QAM), 64QAM, or 256QAM. TBs are encoded as codewords. PDSCH can transmit up to two codewords. Scrambling and modulation mapping can be performed on a codeword basis, and modulation symbols generated from each codeword can be mapped to one or more layers. Each layer is mapped to a resource together with a demodulation reference signal (DMRS), and an OFDM symbol signal is generated from the layer mapped with the DMRS and sent through the corresponding antenna port.

[0086] PUCCH transmits uplink control information (UCI). UCI includes the following information.

[0087] - SR (Scheduling Request): Information used to request UL-SCH resources.

[0088] - HARQ (Hybrid Automatic Repeat Request)-ACK (Acknowledgement): A response to a DL data packet (e.g., codeword) on the PDSCH. HARQ-ACK indicates whether the DL data packet is successfully received. In response to a single codeword, a 1-bit HARQ-ACK may be sent. In response to two codewords, a 2-bit HARQ-ACK may be sent. HARQ-ACK responses include positive ACK (abbreviated as ACK), negative ACK (NACK), discontinuous transmission (DTX), or NACK / DTX. The term HARQ-ACK may be used interchangeably with HARQ ACK / NACK and ACK / NACK.

[0089] - CSI (Channel State Information): Feedback information of DL channels. Multiple-input multiple-output (MIMO) related feedback information includes RI and PMI.

[0090] Table 5 shows an exemplary PUCCH format. Based on PUCCH transmission duration, the PUCCH format may be divided into short PUCCH (formats 0 and 2) and long PUCCH (formats 1, 3, and 4).

[0091] [Table 5]

[0092]

[0093] PUCCH format 0 transmits up to 2 bits of UCI and is mapped in a sequence-based manner for easy transmission. Specifically, the UE sends a specific UCI to the BS by sending one of multiple sequences on the PUCCH of PUCCH format 0. The UE sends the PUCCH of PUCCH format 0 in the PUCCH resources configured for the corresponding SR only when the UE sends a positive SR.

[0094] PUCCH format 1 transmits up to 2 bits of UCI, and the modulation symbol of UCI is spread in the time domain with an orthogonal cover code (OCC) (configured differently depending on whether frequency hopping is performed). DMRS is transmitted in symbols where modulation symbols are not transmitted (ie, transmitted in time division multiplexing (TDM)).

[0095] PUCCH format 2 transmits more than 2 bits of UCI, and the modulation symbols of DCI are sent with frequency division multiplexing (FDM) using DMRS. DMRS are located in symbols #1, #4, #7, and #10 of a given RB at a density of 1 / 3. Pseudo-noise (PN) sequences are used for DMRS sequences. For 2-symbol PUCCH format 2, frequency hopping can be enabled.

[0096] PUCCH format 3 does not support UE multiplexing in the same PRBS and transmits UCI of more than 2 bits. In other words, the PUCCH resource of PUCCH format 3 does not include OCC. The modulation symbol is transmitted in TDM using DMRS.

[0097] PUCCH format 4 supports multiplexing of up to 4 UEs in the same PRBS and transmits UCI of more than 2 bits. In other words, the PUCCH resource of PUCCH format 3 includes OCC. The modulation symbol is transmitted in TDM using DMRS.

[0098] At least one of the one or two or more cells configured in the UE may be configured for PUCCH transmission. At least the primary cell may be configured as a cell for PUCCH transmission. Based on at least one cell configured for PUCCH transmission, at least one PUCCH cell group may be configured in the UE, and each PUCCH cell group includes one or more cells. The PUCCH cell group may be referred to as a PUCCH group. PUCCH transmission may be configured for the SCell as well as the primary cell. The primary cell belongs to the primary PUCCH group, and the PUCCH-SCell configured for PUCCH transmission belongs to the secondary PUCCH group. The PUCCH on the primary cell can be used for cells belonging to the primary PUCCH group, and the PUCCH on the PUCCH-SCell can be used for cells belonging to the secondary PUCCH group.

[0099] The PUSCH transmits UL data (e.g., UL shared channel transport block (UL-SCH TB)) and / or UCI based on a CP-OFDM waveform or a DFT-s-OFDM waveform. When the PUSCH is transmitted in a DFT-s-OFDM waveform, the UE transmits the PUSCH with transform precoding. For example, when transform precoding is not possible (e.g., disabled), the UE may transmit the PUSCH in a CP-OFDM waveform, and when transform precoding is possible (e.g., enabled), the UE may transmit the PUSCH in a CP-OFDM or DFT-s-OFDM waveform. PUSCH transmission may be dynamically scheduled by a UL grant in a DCI, or semi-statically scheduled by higher layer (e.g., RRC) signaling (and / or layer 1 (L1) signaling such as a PDCCH) (configured scheduling or configured grant). PUSCH transmission may be performed in a codebook-based or non-codebook-based manner.

[0100] Figure 5 An exemplary ACK / NACK transmission process is shown. Figure 5 , the UE may detect the PDCCH in slot #n. The PDCCH includes DL scheduling information (eg, DCI format 1_0 or DCI format 1_1). The PDCCH indicates DL assignment with PDSCH offset K0 and PDSCH with HARQ-ACK report offset K1. For example, DCI format 1_0 and DCI format 1_1 may include the following information.

[0101] - Frequency domain resource assignment: Indicates the set of RBs assigned to PDSCH.

[0102] - Time domain resource assignment: Indicates K0 and the starting position (eg, OFDM symbol index) and length (eg, number of OFDM symbols) of the PDSCH in the slot.

[0103] - PDSCH-to-HARQ_feedback timing indicator: indicates K1.

[0104] - HARQ process number (4 bits): Indicates the HARQ process ID of the data (eg, PDSCH or TB).

[0105] - PUCCH resource indicator (PRI): indicates a PUCCH resource to be used for UCI transmission among multiple PUCCH resources in a PUCCH resource set.

[0106] After receiving PDSCH in slot #(n+K0) according to the scheduling information of slot #n, the UE may send UCI on PUCCH in slot #(n+K1). UCI may include a HARQ-ACK response to PDSCH. For convenience, Figure 5Based on the assumption that the SCS of PDSCH is equal to the SCS of PUCCH and slot #n1=slot #(n+K0), this should not be construed as limiting the present disclosure. When the SCSs are different, K1 may be indicated / interpreted based on the SCS of PUCCH.

[0107] In the case where the PDSCH is configured to carry up to one TB, the HARQ-ACK response may be configured in one bit. In the case where the PDSCH is configured to carry up to two TBs, if spatial bundling is not configured, the HARQ-ACK response may be configured in 2 bits, and if spatial bundling is configured, the HARQ-ACK response may be configured in 1 bit. When slot #(n+K1) is designated as the HARQ-ACK transmission timing of multiple PDSCHs, the UCI transmitted in slot #(n+K1) includes the HARQ-ACK responses to the multiple PDSCHs.

[0108] Whether the UE should perform spatial bundling for HARQ-ACK responses may be configured for each cell group (e.g., via RRC / higher layer signaling). For example, spatial bundling may be configured for each individual HARQ-ACK response sent on the PUCCH and / or a HARQ-ACK response sent on the PUSCH.

[0109] Spatial bundling can be supported when up to two (or two or more) TBs (or codewords) can be received at a time (can be or be scheduled by one DCI) in the corresponding serving cell (for example, when the high-level parameter maxNrofCodeWordsScheduledByDCI indicates 2 TBs). More than four layers can be used for 2TB transmission, and up to four layers can be used for 1TB transmission. As a result, when spatial bundling is configured for the corresponding cell group, spatial bundling can be performed for serving cells in the cell group where more than four layers can be scheduled. A UE that wants to send a HARQ-ACK response through spatial bundling can generate a HARQ-ACK response by performing a (bit-wise) logical AND operation on the A / N bits of multiple TBs.

[0110] For example, assuming that a UE receives a DCI that schedules two TBs and receives the two TBs on a PDSCH based on the DCI, the UE that performs spatial bundling can generate a single A / N bit by a logical AND operation between a first A / N bit of a first TB and a second A / N bit of a second TB. As a result, when both the first TB and the second TB are ACK, the UE reports an ACK bit value to the BS, and when at least one TB is NACK, the UE reports a NACK bit value to the BS.

[0111] For example, when only one TB is actually scheduled in a serving cell configured to receive two TBs, the UE may generate a single A / N bit by performing a logical AND operation on the A / N bits of the one TB and a bit value 1. As a result, the UE reports the A / N bits of the one TB to the BS.

[0112] There are multiple parallel DL HARQ processes at the BS / UE for DL ​​transmission. Multiple parallel HARQ processes allow continuous DL transmission while the BS waits for HARQ feedback indicating the success or failure of reception of the previous DL transmission. Each HARQ process is associated with a HARQ buffer in the medium access control (MAC) layer. Each DL HARQ process manages state variables such as the number of MAC physical data unit (PDU) transmissions, HARQ feedback for the MAC PDUs in the buffer, and the current redundancy version. Each HARQ process is identified by a HARQ process ID.

[0113] Figure 6 An exemplary PUSCH transmission process is shown. Figure 6 , the UE may detect the PDCCH in slot #n. The PDCCH includes DL scheduling information (eg, DCI format 1_0 or 1_1). The DCI format 1_0 or 1_1 may include the following information.

[0114] - Frequency domain resource assignment: Indicates the set of RBs assigned to PUSCH.

[0115] - Time domain resource assignment: Indicates the slot offset K2 and the starting position (e.g., OFDM symbol index) and duration (e.g., number of OFDM symbols) of the PUSCH in the slot. The starting symbol and length of the PUSCH may be indicated by the start and length indicator value (SLIV) or separately.

[0116] Then, the UE may transmit the PUSCH in slot #(n+K2) according to the scheduling information in slot #n. The PUSCH includes the UL-SCH TB.

[0117] CSI related operations

[0118] Figure 7 An example of a CSI-related process is shown.

[0119] The UE receives CSI-related configuration information from the BS via RRC signaling (710). The CSI-related configuration information may include at least one of channel state information-interference measurement (CSI-IM) related information, CSI measurement related information, CSI resource configuration related information, CSI-RS resource related information, or CSI report configuration related information.

[0120] - CSI-IM resources may be configured for interference measurement (IM) of the UE. In the time domain, the CSI-IM resource set may be configured as periodic, semi-persistent, or aperiodic. The CSI-IM resources may be configured as zero-power (ZP)-CSI-RS of the UE. The ZP-CSI-RS may be configured to be distinguished from the non-zero-power (NZP)-CSI-RS.

[0121] - The UE may assume that the CSI-RS resources for channel measurement and the CSI-IM / NZP CSI-RS resources for interference measurement configured for one CSI report have a QCL relationship with respect to the "QCL-TypeD" of each resource (when the NZP CSI-RS resources are used for interference measurement).

[0122] - 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 channel measurement resource (CMR) may be an NZP CSI-RS for CSI acquisition, and the interference measurement resource (IMR) may be an NZP CSI-RS for CSI-IM and IM.

[0123] - CSI-RS may be configured for one or more UEs. Different CSI-RS configurations may be provided for each UE, or the same CSI-RS configuration may be provided to multiple UEs. CSI-RS may support up to 32 antenna ports. CSI-RS corresponding to N (N is 1 or greater) antenna ports may be mapped to N RE positions within a time-frequency unit corresponding to one slot and one RB. When N is 2 or greater, the N-port CSI-RS may be multiplexed by CDM, FDM, and / or TDM methods. CSI-RS may be mapped to the remaining REs except for REs mapped with CORESET, DMRS, and SSB. In the frequency domain, CSI-RS may be configured for the entire bandwidth, a partial bandwidth part (BWP), or a partial bandwidth. CSI-RS may be transmitted in each RB within the bandwidth in which the CSI-RS is configured (i.e., density = 1), or CSI-RS may be transmitted in every two RBs (e.g., even or odd RBs) (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 as periodic, semi-persistent, or aperiodic.

[0124] - The CSI report configuration may include configurations of feedback type, measurement resources, report type, etc. The NZP-CSI-RS resource set may be used for the CSI report configuration of the corresponding UE. The NZP-CSI-RS resource set may be associated with a CSI-RS or an SSB. Multiple periodic NZP-CSI-RS resource sets may be configured as TRS resource sets. (i) Feedback types include channel quality indicator (CQI), precoding matrix indicator (PMI), CSI-RS resource indicator (CRI), SSB resource block indicator (SSBRI), layer indicator (LI), rank indicator (RI), first layer (L1)-reference signal received strength (RSRP), etc. (ii) Measurement resources may include configurations of downlink signals and / or downlink resources on which the UE performs measurements to determine feedback information. The measurement resources may be configured as ZP and / or NZP CSI-RS resource sets associated with the CSI report configuration. 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) The report type may include the time at which the UE performs the report and the configuration of the uplink channel. The reporting time may be configured as periodic, semi-persistent, or aperiodic. Periodic CSI reports may be sent on the PUCCH. Semi-persistent CSI reports may be sent on the PUCCH or PUSCH based on a MAC CE indicating enable / disable. Aperiodic CSI reports may be indicated by DCI signaling. For example, the CSI request field of an uplink grant may indicate one of various report trigger sizes. Aperiodic CSI reports may be sent on the PUSCH.

[0125] The UE measures the CSI based on the configuration information related to the CSI. The CSI measurement may include receiving a CSI-RS (720) and acquiring the CSI by calculating the received CSI-RS (730).

[0126] The UE may send a CSI report to the BS (740). For the CSI report, time resources and frequency resources available to the UE are controlled by the BS. The channel state information (CSI) includes at least one of a channel quality indicator (CQI), a precoding matrix indicator (PMI), a CSI-RS resource indicator (CRI), a SS / PBCH block resource indicator (SSBRI), a layer indicator (LI), a rank indicator (RI), L1-RSRP, and / or L-SINR.

[0127] The time domain behavior of CSI reporting supports periodic, semi-persistent, and aperiodic. i) Periodic CSI reporting is performed in short PUCCH and long PUCCH. The periodicity and slot offset of periodic CSI reporting can be configured by RRC, and refer to CSI-ReportConfig IE. ii) SP (semi-periodic) CSI reporting is performed in short PUCCH, long PUCCH or PUSCH. For SP CSI in short / long PUCCH, the periodicity and slot offset are configured by RRC, and CSI reporting is enabled / disabled through a separate MAC CE / DCI. For SP CSI in 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 enabled / disabled by DCI (format 0_1). For SP CSI reporting in PUSCH, a separate RNTI (SP-CSI C-RNTI) is used. The initial CSI report timing follows the PUSCH time domain allocation value indicated by DCI, and the subsequent CSI report timing follows the periodicity configured by RRC. DCI format 0_1 ​​may include a CSI request field and enable / disable a specific configured SP-CSI triggering state. SP CSI reporting has the same or similar enable / disable mechanism as with data transmission in SPS PUSCH. iii) Aperiodic CSI reporting is performed in PUSCH and triggered by DCI. In this case, information related to the triggering of aperiodic CSI reporting may 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 of the AP CSI report is dynamically controlled by DCI.

[0128] The CSI codebooks (e.g., PMI codebooks) defined in the NR specification can be roughly classified into Type I codebooks and Type II codebooks. Type I codebooks are mainly targeted at single-user MIMO (SU-MIMO) supporting both high-order and low-order. Type II codebooks mainly support multi-user MIMO (MU-MIMO) capable of processing up to two layers. Although Type II codebooks can provide more accurate CSI compared to Type I, Type II codebooks may also increase signaling overhead. On the other hand, an enhanced Type II codebook is introduced to address the CSI overhead problem associated with the existing Type II codebook. The enhanced Type II codebook can reduce the payload of the codebook by considering the correlation in the frequency domain.

[0129] The CSI report on PUSCH can be configured as part 1 and part 2. Part 1 has a fixed payload size that identifies the number of information bits in part 2. Part 1 is completely transmitted before part 2.

[0130] - For Type I CSI feedback, Part 1 includes RI (if reported), CRI (if reported) and CQI for the first codeword. Part 2 includes PMI and, when RI>4, Part 2 also includes CQI.

[0131] - For Type II CSI feedback, Part 1 includes the RI (if reported), CQI, and an indication of the number of non-zero WB amplitude coefficients for each layer of the Type II CSI. Part 2 includes the PMI for the Type II CSI.

[0132] - For enhanced Type II CSI feedback, Part 1 includes the RI (if reported), CQI, and an indication of the total number of non-zero WB amplitude coefficients for all layers of enhanced Type II CSI. Part 2 includes the PMI for enhanced Type II CSI.

[0133] For PUSCH, the CSI report consists of two parts. If the CSI payload to be reported is smaller than the payload size provided by the PUSCH resources allocated for CSI reporting, the UE may drop part of Part 2 CSI.

[0134] Semi-persistent CSI reporting performed in PUCCH format 3 or 4 supports Type II CSI feedback, but only part 1 of Type I CSI feedback.

[0135] Quasi-isotope (QCL)

[0136] Two antenna ports are quasi-colocated when the channel properties of an antenna port are to be inferred from the channel of another antenna port. The channel properties may include one or more of delay spread, Doppler spread, frequency / Doppler shift, average received power, receive timing / average delay, and spatial RX parameters.

[0137] A list of multiple TCI state configurations can be configured in the UE through the high-level parameter PDSCH-Config, and each TCI state is linked to the QCL configuration parameters between one or two DL reference signals and the DM-RS port of the PDSCH. The QCL may include qcl-Type1 for the first DL RS and qcl-Type2 for the second DL RS. The QCL type may correspond to one of the following.

[0138] - "QCL-TypeA": {Doppler shift, Doppler spread, average delay, delay spread}

[0139] - "QCL-TypeB": {Doppler shift, Doppler spread}

[0140] - "QCL-TypeC": {Doppler shift, average delay}

[0141] - "QCL-TypeD": {Spatial Rx parameters}

[0142] Beam Management (BM)

[0143] BM refers to a series of processes for acquiring and maintaining a BS beam set (transmit reception point (TRP) beam) and / or a UE beam set that can be used for DL ​​and UL transmission / reception. BM may include the following processes and terms.

[0144] - Beam measurement: An operation in which a BS or UE measures characteristics of a received beamforming signal.

[0145] -Beam determination: The operation by which a BS or UE selects its Tx / Rx beam.

[0146] -Beam sweeping: An operation of covering the spatial domain using Tx and / or Rx beams within a prescribed time interval according to a predetermined method.

[0147] -Beam reporting: An operation in which the UE reports information about a beamformed signal based on beam measurements.

[0148] The BM process may be divided into (1) a DL BM process using SSB or CSI-RS and (2) a UL BM process using SRS. In addition, each BM process may include Tx beam sweeping for determining a Tx beam and Rx beam sweeping for determining an Rx beam.

[0149] The DL BM process may include (1) transmission of a beamformed DL RS (eg, CSI-RS or SSB) from a BS and (2) beam reporting from a UE.

[0150] The beam report may include a preferred DL RS ID and a reference signal received power (RSRP) corresponding to the preferred DL RS ID. The DL RS ID may be a SSB resource indicator (SSBRI) or a CSI-RS resource indicator (CRI).

[0151] Artificial Intelligence / Machine Learning (AI / ML)

[0152] With the development of AI / ML technology, nodes and UEs constituting wireless communication networks are becoming more and more intelligent / advanced, especially due to the intelligence of the network / BS, it is expected that various network / BS-determined parameter values ​​(e.g., the transmission and reception power of each BS, the transmission power of each UE, the precoder / beam of the BS / UE, the time / frequency resource allocation of each UE, or the duplex method of the BS) will be quickly optimized and derived / applied according to various environmental parameters (e.g., the distribution / location of BS, the distribution / location / material of buildings / furniture, the location / moving direction / speed of UE, and climate information). In line with this trend, many standardization organizations (e.g., 3GPP or O-RAN) are considering the introduction of network / BS-determined parameter values, and research on this is also actively underway.

[0153] In a narrow sense, AI / ML can be easily referred to as artificial intelligence based on deep learning, but the concept is shown in Figure 8 middle.

[0154] - Artificial Intelligence: This can correspond to all automation where machines replace human work.

[0155] - Machine Learning: Machines learn decision-making patterns from data without explicit programming rules.

[0156] - Deep learning: This is an AI / ML model based on artificial neural networks, where the machine performs everything from unstructured data to feature extraction and determination at once, and the algorithm relies on biological neural systems, i.e., a multi-layer interconnected network of nodes for feature extraction and transformation inspired by neural networks. Common deep learning network architectures may include deep neural networks (DNNs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs).

[0157] Classification of AI / ML types according to various references

[0158] 1. Offline vs. Online

[0159] (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 and used for prediction. In most cases, this offline learning method is used. In offline learning, the system does not learn incrementally. Instead, all available collected data is used for learning, and the results are applied without further learning. If new data needs to be learned, learning can be started again using the new complete data.

[0160] (2) Online learning: Online learning is a method of improving performance little by little by incrementally learning with additionally generated data, based on the fact that data to be used for learning is continuously generated recently via the Internet. Learning is performed in real time on specific data units (batches) collected online, allowing the system to quickly adapt to changing data.

[0161] In order to build an AI system, learning can be performed only through online learning using only real-time generated data. Alternatively, after offline learning using a specific data set, additional learning can be performed using subsequently generated real-time data (online + offline learning).

[0162] 2. Classification based on AI / ML framework concepts

[0163] (1) Centralized learning: When training data collected from multiple different nodes are reported to a centralized node, all data resources / storage / learning (e.g., supervised, unsupervised, and reinforcement learning) are performed by one central node.

[0164] (2) Federated Learning: A collective AI / ML model is configured based on data across decentralized data owners. Instead of using the data in the AI / ML model, local nodes / devices collect data and train their copies of the AI / ML model, so there is no need to report the source data to the central node. In federated learning, the parameters / weights of the AI / ML model can be sent back to the centralized node to support general AI / ML model training. The advantages of federated learning include increased computing speed and advantages in information security. That is, there is no need to upload personal data to a central server for processing, which can prevent the leakage and abuse of personal information.

[0165] (3) Distributed learning: Machine learning processing represents the concept of scaling and deployment across a cluster of nodes. Splitting and sharing AI / ML model training across multiple nodes operating simultaneously accelerates AI / ML model training.

[0166] 3. Classification by learning method

[0167] (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 may include: (i) regression: linear regression, logistic regression; (ii) instance-based algorithms: k-nearest neighbors (KNN); (iii) decision tree algorithms: CART; (iv) support vector machines: SVM; (v) Bayesian algorithms: Naive Bayes; and (vi) ensemble algorithms: extreme gradient boosting, packing: random forests. Supervised learning can be further grouped as regression and classification problems, with classification predicting labels and regression predicting quantities.

[0168] (2) Unsupervised learning: This is a machine learning task that aims to learn a function that describes the hidden structure in unlabeled data. The input data is unlabeled and there is no known outcome. Some examples of unsupervised learning include K-means clustering, principal component analysis (PCA), nonlinear independent component analysis (ICA), and LSTM.

[0169] (3) Reinforcement Learning: In reinforcement learning (RL), an agent aims to optimize a long-term goal by interacting with the environment based on a trial-and-error process, which is goal-oriented learning based on interaction with the environment. Examples of RL algorithms may include (i) Q-learning, (ii) multi-armed bandit learning, (iii) deep Q-networks, state-action-reward-state-action (SARSA), (iv) temporal difference learning, (v) actuator-evaluator reinforcement learning, (vi) deep deterministic policy gradient, and (vii) Monte Carlo tree search. RL can be further grouped into AI / ML model-based RL and AI / ML model-free RL. Model-based RL is an RL algorithm that uses a predictive AI / ML model to obtain the transition probabilities between states using various dynamic states of the environment and an AI / ML model of the rewards these states cause. Model-free RL is a value-based or policy-based RL algorithm that achieves maximum future rewards, which has low computational complexity in terms of multi-agent environments / states and does not require an accurate representation of the environment. RL algorithms can also be classified into value-based RL vs. policy-based RL, policy-based RL vs. non-policy RL, etc.

[0170] AI / ML Models

[0171] Fig. 9 Shows an example of a feed-forward neural network (FFNN) AI / ML model. Fig. 9 ,FFNN AI / ML model includes input layer, hidden layer and output layer.

[0172] Fig.10 Shows an example of a recurrent neural network (RNN) AI / ML model. Fig.10 , the RNN AI / ML model is an artificial neural network in which hidden nodes are connected to directed edges to form a directed cycle. It is an AI / ML model suitable for processing data that appears sequentially (for example, speech or text). One type of RNN is the long short-term memory (LSTM), which is a structure that adds the cell state to the hidden state of the RNN. In detail, in LSTM, an input gate, a forget gate, and an output gate are added to the RNN cell, and the cell state is added. Fig.10 In this example, A represents a neural network, and x t Indicates the input value, h t Indicates the output value. Here, h t It can represent the current state value based on time, and h t-1 Can represent the previous state value.

[0173] Fig.11A convolutional neural network (CNN) AI / ML model is shown. CNN is used for two purposes, including reducing the complexity of the AI / ML model and extracting good features by applying convolution calculations commonly used in the fields of video processing or image processing. Fig.11 , a kernel or filter means a unit / structure that applies weights to the input within a specific range / unit. A kernel (or filter) can be modified by learning. The stride is the range of movement of the kernel within the input. A feature map is the result of applying a kernel to the input. Padding refers to a value added to adjust the size of a feature map. To increase robustness to distortion and variation, multiple feature maps can be extracted. Pooling refers to the computation of reducing the size of a feature map by downsampling it (e.g., max pooling or average pooling).

[0174] Fig.12 Figure 2 shows an autoencoder AI / ML model. Fig.12 , an autoencoder is a neural network that receives a feature vector x and outputs the same or similar vector x' and an unsupervised learning where the input node and the output node have the same features. The autoencoder reconstructs the input, so the output can be called a reconstruction. The loss function can be expressed according to the following formula 1.

[0175] [Formula 1]

[0176] ,in

[0177] In Equation 12, the loss function of the autoencoder is calculated based on the difference between the input and the output. Based on the loss function of the autoencoder, the degree of loss in the input is evaluated, and an optimization process is applied to the autoencoder to minimize the loss.

[0178] Fig.13 is a diagram illustrating split AI reasoning.

[0179] Fig.13 It shows a case where the model inference function is performed collaboratively between a terminal device such as a UE and a network AI / ML endpoint during split AI operation.

[0180] In addition to the model inference function, the model training function, executor, and data collection function can each be split into multiple parts based on the current task and environment. These functions can be performed through the collaboration of multiple entities.

[0181] For example, the computationally intensive part and the energy intensive part can be executed at the network endpoint, while the privacy-sensitive part and the delay-sensitive part can be executed on the terminal device. In this case, the terminal device executes the task / model based on the input data up to a specific part / layer, and then sends the intermediate data to the network endpoint. The network endpoint executes the remaining parts / layers and provides the reasoning output to one or more devices that perform the operation / task.

[0182] The following describes the functional framework for AI operations.

[0183] In this article, the following terms are defined to explain AI (or AI / ML) in more detail.

[0184] - Data Collection: Data collected from network nodes, management entities or UEs, which is used as the basis for AI model training, data analysis and inference.

[0185] - AI Model: A data-driven algorithm in which AI techniques are applied to generate a set of outputs based on a set of inputs, including prediction information and / or decision parameters.

[0186] - AI / ML Training: The online or offline process of training an AI model by learning features and patterns to best represent the data and obtaining an AI / ML model trained for inference.

[0187] - AI / ML Inference: The process of using trained AI models to make predictions or draw decisions based on collected data and AI models.

[0188] Reference Fig.14 , the data collection function 10 collects input data and provides the processed input data to the model training function 20 and the model reasoning function 30.

[0189] For example, input data may include measurements from UEs or other network entities, feedback from actuators, and outputs from AI models.

[0190] The data collection function 10 performs data preparation based on the input data and provides the input data processed by the data preparation. Here, the data collection function 10 does not perform specific data preparation (eg, data preprocessing and cleaning, forming and transforming) for each AI algorithm, but the data collection function 10 can perform data preparation common to the AI ​​algorithms.

[0191] After completing the data preparation process, the data collection function 10 provides the training data 11 to the model training function 20 and provides the inference data 12 to the model inference function 30. Here, the training data 11 is the input data required by the AI ​​model training function 20. The inference data 12 is the input data required by the AI ​​model inference function 30.

[0192] The data collection function 10 may be performed by a single entity (eg, UE, RAN node, network node, etc.) or by multiple entities. In this case, the training data 11 and the inference data 12 may be provided to the model training function 20 and the model inference function 30, respectively, from the multiple entities.

[0193] As part of the AI ​​model testing process, the model training function 20 is responsible for performing the AI ​​model training, validation, and testing required to generate model performance metrics. If necessary, the model training function 20 can handle data preparation (e.g., data preprocessing and cleaning, forming, and transformation) based on the training data 11 provided by the data collection function 10.

[0194] Here, model deployment / update 13 is used to initially deploy the trained, verified and tested AI model to the model reasoning function 30 or to provide an updated model to the model reasoning function 30.

[0195] The model reasoning function 30 is responsible for providing AI model reasoning output 16 (e.g., prediction or decision). If applicable, the model reasoning function 30 may also provide model performance feedback 14 to the model training function 20. In addition, if necessary, the model reasoning function 30 may handle data preparation (e.g., data preprocessing and cleaning, forming and transforming) based on the reasoning data 12 provided by the data collection function 10.

[0196] 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.

[0197] When available, model performance feedback 14 can be used to monitor the performance of the AI ​​model. However, the feedback can be omitted.

[0198] The executor function 40 receives the output 16 from the model reasoning function 30 and triggers or performs relevant tasks / operations. The executor function 40 may trigger tasks / operations of other entities (eg, one or more UEs, one or more RAN nodes, one or more network nodes, etc.) or itself.

[0199] Feedback 15 can be used to derive training data 11 and inference data 12 or monitor the performance of the AI ​​model, its impact on the network, etc.

[0200] The definitions of training, validation, and testing in datasets used in AI / ML can be distinguished as follows:

[0201] - Training data: Training data refers to the dataset used to train the model.

[0202] - Validation data: Validation data refers to a dataset used to validate a trained model. In other words, validation data is a dataset used to prevent overfitting of a typical training dataset.

[0203] In addition, validation data refers to a data set used to select the best model among multiple trained models during the training process. Therefore, validation data can also be considered as a kind of learning.

[0204] - Test data: Test data refers to the dataset used for final evaluation. Test data is independent of training.

[0205] In the case of the above datasets, it is common to split the training set so that the training data and validation data are divided in a ratio of 8:2 or 7:3. When including test data, the training data, validation data, and test data can be divided in a ratio of 6:2:2 (train:validation:test).

[0206] According to the capabilities of the AI / ML functions between the BS and the UE, the cooperation level can be defined as follows. It is also possible to combine multiple levels or modify the level by separating any one level.

[0207] Cat 0a) No collaborative framework: AI / ML algorithms are purely implementation-based and do not require changes to the wireless interface.

[0208] Cat 0b) This level involves wireless interfaces suitable for modification based on efficiently implemented AI / ML algorithms, but this level corresponds to a framework without collaboration.

[0209] Cat 1) provides support between nodes to enhance the AI / ML algorithms of individual nodes. This level is applied when the UE receives support (for training, adaptation, etc.) from the gNB and vice versa. At this level, model exchange between network nodes is not required.

[0210] Cat 2) can perform collaborative ML tasks between UE and gNB. This level requires the exchange of AI / ML model commands between network nodes.

[0211] Fig.14 The functions shown may be implemented in a RAN node (eg, a BS, a TRP, a Central Unit (CU) of a BS), a network node, an Operation Administration and Maintenance (OAM) of a network operator, or a UE.

[0212] Alternatively, two or more entities among the RAN node, the network node, the network operator's OAM or the UE may collaborate to implement Fig.14 For example, an entity can perform Fig.14 Some of the functions in the same entity can be performed by another entity, while the remaining functions can be performed by another entity. Fig.14 If some of the functions shown are performed by a single entity (e.g., UE, RAN node, or network node), the transmission / provision of data / information between the functions 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 the model deployment / update 13 and the model performance feedback 14 may be omitted.

[0213] Alternatively, Fig.14Any of the functions shown may be performed by collaboration between two or more entities in a RAN node, a network node, a network operator's OAM, or a UE. This may be referred to as split AI operation.

[0214] Fig.15 A scenario is shown in which the AI ​​model training function is performed by a network node (e.g., a core network node, a network operator's OAM, etc.), while the AI ​​model reasoning function is performed by a RAN node (e.g., a BS, a TRP, or a CU of a BS).

[0215] Step 1: RAN Node 1 and RAN Node 2 send input data (i.e., training data) for AI model training to the network node. Here, RAN Node 1 and RAN Node 2 may send data collected from UEs (e.g., UE measurements related to RSRP, RSRQ or SINR of serving cells and neighboring cells, UE location, speed, etc.) to the network node.

[0216] Step 2: The network nodes train the AI ​​model using the received training data.

[0217] Step 3: The network node deploys / updates the AI ​​model to RAN node 1 and / or RAN node 2. RAN node 1 (and / or RAN node 2) may continue to perform model training based on the received AI model.

[0218] For ease of explanation, it is assumed that the AI ​​model is only deployed / updated to RAN node 1.

[0219] Step 4: RAN node 1 receives input data for AI model inference (i.e., inference data) from UE and RAN node 2.

[0220] Step 5: RAN node 1 uses the received inference data to perform AI model inference to generate output data (e.g., prediction or decision).

[0221] Step 6: If applicable, the RAN node 1 may send model performance feedback to the network node.

[0222] Step 7: RAN node 1, RAN node 2 and UE (or RAN node 1 and UE or RAN node 1 and 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.

[0223] Step 8: RAN node 1 and RAN node 2 send feedback information to the network node.

[0224] Fig.16 A scenario is shown in which both the AI ​​model training function and the AI ​​model inference function are performed by a RAN node (e.g., a BS, a TRP, or a CU of a BS).

[0225] Step 1: UE and RAN node 2 send input data (i.e., training data) for AI model training to RAN node 1.

[0226] Step 2: RAN node 1 uses the received training data to train the AI ​​model.

[0227] Step 3: RAN node 1 receives input data for AI model inference (i.e., inference data) from UE and RAN node 2.

[0228] Step 4: RAN node 1 uses the received inference data to perform AI model inference to generate output data (e.g., prediction or decision).

[0229] Step 5: RAN node 1, RAN node 2 and UE (or RAN node 1 and UE or RAN node 1 and 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.

[0230] Step 6: RAN node 2 sends feedback information to RAN node 1.

[0231] Fig.17 A scenario is shown in which the AI ​​model training function is performed by a RAN node (e.g., a BS, a TRP, or a CU of a BS), while the AI ​​model inference function is performed by a UE.

[0232] Step 1: The UE sends input data (i.e., training data) for AI model training to the RAN node. Here, the RAN node may collect data (e.g., UE measurements related to RSRP, RSRQ or SINR of the serving cell and neighboring cells, UE location, speed, etc.) from various UEs and / or other RAN nodes.

[0233] Step 2: The RAN node trains the AI ​​model using the received training data.

[0234] Step 3: The RAN node deploys / updates the AI ​​model to the UE. The UE may continue to perform model training based on the received AI model.

[0235] Step 4: The UE receives input data for AI model inference (ie, inference data) from the RAN node (and / or from other UEs).

[0236] Step 5: The UE performs AI model inference using the received inference data to generate output data (e.g., prediction or decision).

[0237] Step 6: If applicable, the UE may send model performance feedback to the RAN node.

[0238] Step 7: The UE and RAN nodes perform actions based on the output data.

[0239] Step 8: The UE sends feedback information to the RAN node.

[0240] CQI / RI determination for AI / ML-based CSI reporting

[0241] With the advancement of computing technology and AI / ML technology, nodes and UEs included in wireless communication networks are becoming increasingly intelligent and complex. In particular, due to the intelligence of the network, it is expected that various network decision parameter values ​​(e.g., transmission and reception power of each BS, transmission power of each UE, precoder / beam of each BS / UE, time / frequency resource allocation of each UE, duplex mode of each BS, etc.) can be quickly optimized and applied based on various network environment parameters (e.g., distribution / location of BS, distribution / location / material of buildings / furniture, location / moving direction / speed of UE, climate information, etc.).

[0242] A CQI / RI determination method for performing AI / ML-based CSI reporting in an evolved network / gNB / UE is proposed below.

[0243] Fig.18 It is a diagram used to illustrate AI-based CSI feedback.

[0244] In a method for reducing CSI feedback payload overhead, a common AI model may be based on compression at the UE and / or BS. Fig.18 As shown in , a CSI encoder for the UE and a CSI decoder for the BS may be assumed. The encoder and decoder may be associated with an autoencoder, and a convolutional neural network (CNN) may be used for efficient reduction of channel size / dimensionality.

[0245] In AI / ML based payload reduction, compression is a key factor. There are two main types of channel feedback: explicit feedback and implicit feedback. LTE and NR use implicit feedback, where RI / CQI / PMI is reported to the BS instead of reporting the original channel matrix or channel covariance matrix. Compared with explicit feedback, implicit feedback has the advantage of lower feedback overhead. When AI / ML is used for CSI payload reduction, information about the compressed original channel can be fed back to the gNB, which can lead to explicit feedback.

[0246] As another example of AI / ML-based CSI feedback, compression of existing PMI, such as Type I and Type II CSI, can be considered. In Rel-16 / 17 Type II CSI, linear-based vectors (such as DFT vectors) are used for compression in the frequency domain to reduce the payload of Rel-15 Type II CSI. Accordingly, AI / ML can be used to further reduce the payload of existing CSI and improve its performance.

[0247] For example, bilateral (at the network and UE) AI / ML can be considered. In the bilateral (at the network and UE) case, autoencoder-like CSI reporting can be performed on the assumption that both the AI / ML gNB and the UE have the capabilities (training and / or inference) for AI / ML.

[0248] Alternatively, one-sided (at the network or UE) AI / ML can be considered. In the one-sided case, specific parameter optimization (performance and / or payload) based on AI / ML can be considered for the traditional codebook.

[0249] For CSI compression using bilateral AI / ML model based CSI reporting, the following needs to be considered.

[0250] - CSI generation model output / CSI reconstruction model input (e.g., configuration size / format), potential post-processing / pre-processing of CSI generation model output / CSI reconstruction model input

[0251] -CQI determination

[0252] -RI OK

[0253] That is, it is necessary to discuss how to determine CQI and RI in AI / ML-based CSI reporting (especially when spatial frequency domain compression is performed based on a bilateral AI / ML model).

[0254] As described above, the bilateral AI / ML model includes an AI / ML model which is deployed / configured in each of the UE and the gNB (or network) and performs inference. For example, the AI / ML model can be configured in the form of an autoencoder. The UE (CSI encoder) calculates the AI / ML model inference output by using channel information (e.g., channel matrix / channel covariance matrix / channel eigenvector) or information obtained by preprocessing the channel information as input, and feeds the output information back to the gNB with or without post-processing. The gNB (CSI decoder) calculates the inference output by inputting the feedback information to the AI / ML model with or without pre-processing, and finally decodes / obtains the CSI with or without post-processing of the inference output.

[0255] In this bilateral model, the UE may not directly determine the PMI from the information output by the AI / ML model (encoder), and thus has difficulty calculating other CSI (e.g., CQI / RI) based on the PMI. In other words, in the bilateral model, after receiving the AI / ML-based CSI report from the UE, the gNB determines the PMI through the AI / ML model, and (if there is no separate PMI signaling from the gNB later), the UE may not identify the PMI to which its reported AI / ML-based CSI corresponds. In addition, when the UE cannot identify the PMI, the UE has difficulty calculating other CSI (e.g., CQI / RI).

[0256] In the present disclosure, a method for solving this problem is proposed.

[0257] Table 6 is an excerpt of the CQI calculation specified in the legacy NR standard (TS 38.214).

[0258] [Table 6]

[0259]

[0260] As described in Table 6, for CSI, there should be a UE assumption for PMI. However, in the case of bilateral AI / ML models, the UE's encoder output may not be assumed to be PMI as described above, which leads to ambiguity in CQI and / or RI calculations. In addition, when the feature vector is used as AI / ML input, CQI / RI needs to be calculated.

[0261] (1) Proposal 1

[0262] In AI / ML based (bilateral) CSI reporting, AI / ML based CSI reporting and its linked or paired traditional CSI reporting should be triggered together to obtain CSI and / or RI information (at the gNB).

[0263] In Proposal 1, conventional CSI reporting may include codebook-based (not AI / ML-based) CSI reporting for PMI acquisition. Codebook-based CSI reporting may include Type I CSI and Type II CSI (e.g., Rel-15 / 16 / 17 / 18) described above. Linked or paired CSI reporting may refer to CSI reports that share the same CSI-RS id, or when a new link id or pairing id is introduced, to CSI reports with the same id. AI / ML-based CSI reporting includes the AI / ML-based bilateral spatial frequency domain compressed CSI described above.

[0264] When using Proposal 1, the number of reports configuration for AI / ML based CSI reporting may include only the AI / ML generated CSI. The remaining CQI and / or RI may then be reported in the paired or linked CSI report. The codebook for paired / linked CSI reporting may be pre-configured by the gNB in ​​the CSI reporting configuration for the UE, or a default PMI (e.g., Rel-16 Type II CSI or eigenvector) may be configured / indicated by the gNB. Whether to report PMI, or in the case of CQI whether it is wideband CQI or subband CQI, may also be configured separately by the gNB.

[0265] For example, an AI / ML-based CSI report and at least one traditional CSI report may be linked or paired with each other. The AI / ML-based CSI report configuration may include information about the linked or paired traditional CSI report (configuration). Traditional CSI reporting is a CSI report that does not use the AI / ML model and may be referred to as a non-AI / ML-based CSI report. When the UE performs AI / ML-based CSI reporting, it may also perform linked or paired traditional CSI reporting. Alternatively, when the network triggers AI / ML-based CSI reporting, it may also indicate to the UE the traditional CSI report linked or paired with the AI / ML-based CSI report.

[0266] In case of paired / linked CSI, it may be assumed that the occupancy of the CSI processing unit (CPU) is doubled, or the gNB may (based on the UE’s capability report) set / indicate a new CPU value.

[0267] AI / ML based CSI reports and linked or paired traditional CSI reports may be sent together (through the same time resources / signal), which should not be construed as limiting.

[0268] For example, in the case of a periodic or semi-periodic configuration for two CSI reports (e.g., AI / ML CSI and traditional CSI), CSI can be reported with different periodicities and / or offsets for network flexibility. In this case, the payload of the PMI can be determined based on the value corresponding to the CQI / RI. Therefore, the payload of the PMI is determined based on the most recently reported / transmitted CQI / RI of the UE. Linked or paired CSI reports can be sent through different channels. For example, CQI and / or RI can be sent through PUCCH, and the remaining AI / ML generated CSI can be sent through PUSCH. When two CSI reports are configured to have the same periodicity / time resources, they can be sent at one time through one signal (e.g., PUSCH). Alternatively, even if the two CSI reports (e.g., AI / ML CSI and traditional CSI) have different periodicities, they can be sent at one time through one signal (e.g., PUSCH) in the time resources where the two CSI reports overlap.

[0269] For example, in Proposal 1, two CSI reports (e.g., a legacy report and an AI / ML-based CSI report) may be triggered independently. As described above, linkage is only additionally configured in the configuration for the two CSI reports, and the UE calculates the CSI with mutual dependence as in Proposal 1. In this case, configuration flexibility may be increased by leaving it to the gNB to choose whether to trigger the two CSI reports simultaneously or at different times. Alternatively, the two CSI reports may belong to one report set, and the corresponding CSI reports may be triggered simultaneously by a higher layer such as MAC-CE / DCI. Alternatively, a method of linking multiple CSI reports to specific code points of a field indicated by MAC-CE / DCI and triggering them by MAC-CE / DCI may also be considered.

[0270] Although the above description is mainly about CSI reporting, it can also be extended to CSI resource configuration so that two CSI resources (e.g., AI / ML CSI resources and traditional CSI resources) are configured to be linked, and thus the two resources have mutual dependence. For example, one resource can be configured for AI / ML-based CSI reporting, and the other resource can be configured for traditional CSI reporting, so that the two resources can be configured to have a link with one report.

[0271] 1) Proposal 1-1. Another example of Proposal 1 is a method of receiving configurations for AI / ML-based CSI reporting and conventional CSI reporting in one reporting configuration and performing reporting at one time.

[0272] In Proposal 1-1, the CSI reports configured separately in Proposal 1 are configured in one CSI report configuration and performed at one time. For example, CQI and / or RI may be calculated / reported based on the traditional codebook, and CSI generated based on AI / ML may be reported. CSI generation based on AI / ML may be based on RI. Therefore, CSI and / or CQI and / or RI and / or CRI and / or LI generated by AI / ML may be reported as CSI content in Proposal 1-1.

[0273] (2) Proposal 2

[0274] In AI / ML based (bilateral) CSI reporting, other CSI contents (e.g., SINR) are reported instead of CQI, and the gNB schedules DL data for the UE based on the reported SINR.

[0275] Currently, for CQI, the UE reports a CQI index corresponding to the modulation order, code rate, and spectral efficiency of the SINR of each CW mapped to the UE. However, as described above, the UE should assume the PMI to calculate the SINR of each CW. Proposal 2 proposes that SINR is reported as a new content instead of such CQI, and SINR is a value calculated based on a vector (e.g., eigenvector) representing the channel measured by the UE based on the CSI-RS. By using the reported SINR information and / or decoding the CSI based on AI / ML generated CSI and correcting the SINR value according to the gNB implementation, the gNB can configure and indicate the MCS value used in DL data transmission to the UE. The UE can report the eigenvalue corresponding to the eigenvector instead of SINR. For SINR reporting, the conventional L1-SINR table configured for beam management can be used, or a new SINR table can be configured and indicated. Alternatively, in order to improve the flexibility of configuring the SINR table, the gNB can set the upper / lower limit, step size, and / or number of bits of the SINR range so that reporting can be performed with the increased flexibility of SINR table configuration. For example, by setting the SINR upper limit to 10 dBm, the lower limit to 0 dBm, and the step size to 0.5, the table can be configured to have a total of 20 states. Alternatively, in Proposal 2, the CQI can be reported, and the gNB can recognize that the CQI is a value calculated based on the eigenvector, correct the CQI, and use it in DL scheduling for the UE.

[0276] (3) Proposal 3

[0277] For AI / ML based (bilateral) CSI reporting, the gNB may indicate / configure a CQI offset value to the UE, and the UE may report to the gNB a CQI value calculated based on the CQI offset value indicated by the gNB, in addition to the CSI generated based on AI / ML.

[0278] For example, the gNB may signal the UE a CQI offset value to be applied to the CQI obtained by the legacy CSI method. The UE may apply the CQI offset to the result of calculating the CQI by the legacy CSI method. When the UE sends an AI / ML-based CSI report, the UE may also report the legacy CQI to which the CQI offset is applied to the network. In this case, Proposal 3 may be understood as a more specific implementation example of Proposal 1. Alternatively, the legacy CQI to which the CQI offset is applied may be sent as part of the AI / ML-based CSI report.

[0279] In Proposal 3, CQI offset refers to a value used to correct CQI when UE calculates CQI, and has a + or - value. The PMI assumed for calculating CQI may be, for example, a feature vector of a channel measured by the UE or may be a specific codebook, which has been pre-agreed between the gNB and the UE or pre-indicated by the gNB. In Proposal 3, when the CQI calculated by the UE is 10 and the offset value configured by the gNB for the UE is -3, the CQI value actually reported by the UE is 7. The offset value is applied to the CQI reported by the UE or content corresponding to the CQI (e.g., SINR).

[0280] In another example of Proposal 3, the UE may calculate the CQI based on the PMI assumption information for CQI calculation, which has been configured or pre-agreed by the gNB, and report it together with the offset value preferred or calculated by the UE.

[0281] The CQI and / or offset value is reported to the gNB together with the CSI generated based on AI / ML.

[0282] Fig.19 is a diagram illustrating an implementation example of CSI reporting based on Proposal 3.

[0283] refer to Fig.19 , the CSI reporting process can be roughly divided into CSI configuration (1905) through network signaling, CSI measurement (1910) based on CSI configuration, and CSI reporting (1915) of the measurement. In the case of aperiodic CSI reporting, a separate dynamic signaling (not shown) that triggers it can be added.

[0284] The CSI configuration (1905) may include a first configuration for AI / ML based CSI reporting and a second configuration for non-AI / ML based CSI reporting, and the first configuration and the second configuration may be associated with each other.

[0285] The CSI measurement (1910) may include a first CSI process based on AI / ML performed on AI / ML according to a first configuration, and a second CSI process (as in conventional CSI methods) in which CQI / RI and / or PMI are calculated according to a second configuration (as conventionally done). The first CSI process and the second CSI process may be performed in parallel (simultaneously) or based on the same CSI reference resource (set).

[0286] The CSI report (1915) may include the output from the AI / ML model based on the first CSI process and the CQI ( / RI) based on the second CSI process. For example, based on reporting the CQI ( / RI) based on the second CSI process together with the output from the AI / ML model based on the first CSI process, a CQI offset may be applied to the CQI based on the second CSI process.

[0287] (4) Proposal 4

[0288] For AI / ML based (bilateral) CSI reporting, the gNB or NW also delivers the AI / ML model corresponding to the AI / ML decoder side to the UE, and the UE calculates the CQI / RI using the decoder output and reports it to the gNB.

[0289] In Proposal 4, although it is a bilateral model, the AI / ML model of the gNB is also delivered to the UE for the UE's CQI report so that the UE can calculate the final AI / ML model output (decoder-side output). The applicability of Proposal 4 can be based on the capabilities of the UE. In addition, since the complexity of inference in Proposal 4 is doubled, it can be included in the AI ​​processor complexity or CSI complexity. For example, in the case of CPU occupancy, inference only on the existing encoder is counted as 1, and inference on the decoder is counted as 2.

[0290] (5) Proposal 5

[0291] For AI / ML-based (bilateral) CSI reporting, the gNB or NW configures the target RI / CQI (or SINR range), and the AI / ML-based CSI value is generated based on the value indicated by the gNB / NW and reported to the gNB.

[0292] In Proposition 5, CQI / RI reporting may be skipped. Alternatively, a recommended or preferred value may be reported to the gNB as a suggestion for a change in the CQI / RI indicated by the gNB. However, in this case, the reported AI / ML-based CSI is based on the value of RI / CQI preconfigured by the gNB. Alternatively, it may be agreed to report CSI based on the CQI / RI preferred by the UE.

[0293] (6) Proposal 6

[0294] The size of the AI / ML based (bilateral) CSI varies based on the preferred RI value.

[0295] In Proposal 6, the size of AI / ML-based CSI changes according to the preferred RI value reported by the UE. For example, the CSI size for RI=2 is different from the CSI size for RI=1. Therefore, the gNB can implicitly identify the RI according to the size of the CSI reported by the UE. However, in this case, because the gNB does not know the exact payload, multiple blind detections are required.

[0296] The above proposal applies not only to STRP but also to MTRP.

[0297] Fig. 20 is a diagram illustrating the operation of a UE according to an embodiment.

[0298] refer to Fig. 20 , the UE can receive CSI related configuration (A05).

[0299] The UE may perform a plurality of CSI measurements associated with each other based on the CSI-related configuration (A10). The plurality of associated CSI measurements may include a first CSI measurement performed based on an AI / ML model and a second CSI measurement performed without an AI / ML model.

[0300] The UE may send a CSI report based on the CSI measurement (A15). The CSI report may include the output of the AI / ML model obtained through the first CSI measurement and the CQI information obtained through the second CSI measurement.

[0301] The CQI information may include a second CQI value obtained by applying a CQI offset to a first CQI value calculated from the second CSI measurement.

[0302] The CQI offset may be provided via network signaling.

[0303] Based on the first CSI measurement being triggered, the second CQI measurement linked to the first CSI measurement may be performed together with the first CSI measurement.

[0304] The first CSI measurement and the second CSI measurement may be performed respectively in the first CSI resource and the second CSI resource associated with each other.

[0305] The CSI configuration may include information for association of the first CSI resource and the second CSI resource.

[0306] For example, a first CSI report including an output of an AI / ML model obtained through a first CSI measurement may be transmitted through a PUSCH. A second CSI report including CQI information obtained through a second CSI measurement may be transmitted through a PUCCH.

[0307] In another example, the CSI report may be sent via a physical uplink channel.

[0308] The output of the AI / ML model configured for the UE may be provided as input to the AI / ML model configured in the network.

[0309] Multiple associated CSI measurements may be counted as N CSI processes, and N may be a value equal to or greater than 1 and may be provided through network signaling.

[0310] Fig.21 is a diagram illustrating a BS operation according to an embodiment.

[0311] refer to Fig.21 , BS can send CSI related configuration (B05).

[0312] The BS may receive a CSI report for a plurality of CSI measurements associated with each other based on the CSI-related configuration (B10). The plurality of interconnected CSI measurements may include a first CSI measurement performed based on an AI / ML model and a second CSI measurement performed without an AI / ML model. The CSI report may include an output of the AI / ML model obtained through the first CSI measurement and CQI information obtained through the second CSI measurement.

[0313] The CQI information may include a second CQI value obtained by applying a CQI offset to a first CQI value calculated from the second CSI measurement.

[0314] The CQI offset may be provided through network signaling.

[0315] Based on the first CSI measurement being triggered, the second CQI measurement associated with the first CSI measurement may be performed together with the first CSI measurement.

[0316] The first CSI measurement and the second CSI measurement may be performed respectively in the first CSI resource and the second CSI resource associated with each other.

[0317] The CSI configuration may include information for association of the first CSI resource and the second CSI resource.

[0318] In an example, a first CSI report including an output of an AI / ML model obtained through a first CSI measurement may be received through a PUSCH. A second CSI report including CQI information obtained through a second CSI measurement may be received through a PUCCH.

[0319] In another example, the CSI reports may be received over a single physical uplink channel.

[0320] The output of the AI / ML model configured in the UE may be provided as the input of the AI / ML model configured in the BS.

[0321] Multiple associated CSI measurements may be counted as N CSI processes, and N may be a value equal to or greater than 1 and may be provided through network signaling.

[0322] Fig. 22 A communication system 1 applied to the present disclosure is shown.

[0323] refer to Fig. 22 , a communication system 1 applied to the present disclosure includes a wireless device, a base station (BS), and a network. Herein, a wireless device refers to a device that performs communication using a radio access technology (RAT) (e.g., 5G new RAT (NR) or long term evolution (LTE)), and may be referred to as a communication / radio / 5G device. The wireless device may include, but is not limited to, a robot 100a, vehicles 100b-1 and 100b-2, an extended reality (XR) device 100c, a handheld device 100d, a home appliance 100e, an Internet of Things (IoT) device 100f, and an artificial intelligence (AI) device / server 400. For example, a vehicle may include a vehicle with a wireless communication function, an autonomous driving vehicle, and a vehicle capable of performing communication between vehicles. Herein, a vehicle may include an unmanned aerial vehicle (UAV) (e.g., a drone). XR devices may include augmented reality (AR) / virtual reality (VR) / mixed reality (MR) devices, and may be implemented in the form of a head mounted device (HMD), a head up display (HUD) installed in a vehicle, a television, a smart phone, a computer, a wearable device, a home appliance device, a digital sign, a vehicle, a robot, etc. Handheld devices may include smart phones, smart boards, wearable devices (e.g., smart watches or smart glasses), and computers (e.g., notebooks). Home appliances may include TVs, refrigerators, and washing machines. IoT devices may include sensors and smart meters. For example, a BS and a network may be implemented as wireless devices, and a specific wireless device 200a may operate as a BS / network node relative to other wireless devices.

[0324] The wireless devices 100a to 100f may be connected to the network 300 via the BS 200. AI technology may be applied to the wireless devices 100a to 100f, and the wireless devices 100a to 100f may be connected to the AI ​​server 400 via the network 300. The network 300 may be configured using a 3G network, a 4G (e.g., LTE) network, or a 5G (e.g., NR) network. Although the wireless devices 100a to 100f may communicate with each other via the BS 200 / network 300, the wireless devices 100a to 100f may perform direct communication (e.g., side link communication) with each other without passing through the BS / network. For example, the vehicles 100b-1 and 100b-2 may perform direct communication (e.g., vehicle-to-vehicle (V2V) / vehicle-to-everything (V2X) communication). An IoT device (e.g., a sensor) may perform direct communication with other IoT devices (e.g., a sensor) or other wireless devices 100a to 100f.

[0325] Wireless communication / connection 150a, 150b or 150c may be established between wireless devices 100a to 100f / BS 200 or BS 200 / BS 200. Herein, wireless communication / connection may be established through various RATs (e.g., 5G NR) such as uplink / downlink communication 150a, sidelink communication 150b (or D2D communication) or inter-BS communication (e.g., relay, integrated access backhaul (IAB)). The wireless device and the BS / wireless device may send / receive radio signals to / from each other through wireless communication / connection 150a and 150b. For example, wireless communication / connection 150a and 150b may send / receive signals through various physical channels. To this end, at least a portion of various configuration information for configuring a process for sending / receiving a radio signal, various signal processing processes (e.g., channel coding / decoding, modulation / demodulation, and resource mapping / demapping), and a resource allocation process may be performed based on various proposals of the present disclosure.

[0326] Fig.23 A wireless device suitable for use with the present disclosure is shown.

[0327] refer to Fig.23 , the first wireless device 100 and the second wireless device 200 may transmit radio signals via various RATs (eg, LTE and NR). Herein, {the first wireless device 100 and the second wireless device 200} may correspond to Fig. 22 {wireless device 100x and BS 200} and / or {wireless device 100x and wireless device 100x}.

[0328] The first wireless device 100 may include one or more processors 102 and one or more memories 104, and further include one or more transceivers 106 and / or one or more antennas 108. The processor 102 may control the memory 104 and / or the transceiver 106, and may be configured to implement the description, functions, processes, proposals, methods, and / or operation flowcharts disclosed in this document. For example, the processor 102 may process the information in the memory 104 to generate first information / signals, and then transmit a radio signal including the first information / signals through the transceiver 106. The processor 102 may receive a radio signal including second information / signals through the transceiver 106, and then store information obtained by processing the second information / signals 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 store software codes including commands for executing part or all of the processes controlled by the processor 102 or for executing the description, functions, processes, proposals, methods, and / or operation flowcharts disclosed in this document. Herein, the processor 102 and the memory 104 may be part of a communication modem / circuit / chip designed to implement a RAT (e.g., LTE or NR). The transceiver 106 may be connected to the processor 102 and transmit and / or receive radio signals through one or more antennas 108. Each transceiver 106 may include a transmitter and / or a receiver. The transceiver 106 may be used interchangeably with a radio frequency (RF) unit. In the present disclosure, a wireless device may represent a communication modem / circuit / chip.

[0329] The second wireless device 200 may include one or more processors 202 and one or more memories 204, and further include one or more transceivers 206 and / or one or more antennas 208. The processor 202 may control the memory 204 and / or the transceiver 206, and may be configured to implement the description, functions, processes, proposals, methods, and / or operation flowcharts disclosed in this document. For example, the processor 202 may process the information in the memory 204 to generate third information / signals, and then transmit a radio signal including the third information / signals through the transceiver 206. The processor 202 may receive a radio signal including fourth information / signals through the transceiver 206, and then store information obtained by processing 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 store software codes including commands for executing part or all of the processes controlled by the processor 202 or for executing the description, functions, processes, proposals, methods, and / or operation flowcharts disclosed in this document. Herein, the processor 202 and the memory 204 may be part of a communication modem / circuit / chip designed to implement a RAT (e.g., LTE or NR). The transceiver 206 may be connected to the processor 202 and transmit and / or receive radio signals through one or more antennas 208. Each 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 represent a communication modem / circuit / chip.

[0330] Hereinafter, the hardware elements of the wireless devices 100 and 200 will be described in more detail. One or more protocol layers may be implemented by (but not limited to) one or more processors 102 and 202. For example, one or more processors 102 and 202 may implement one or more layers (e.g., functional layers such as PHY, MAC, RLC, PDCP, RRC, and SDAP). One or more processors 102 and 202 may generate one or more protocol data units (PDUs) and / or one or more service data units (SDUs) according to the descriptions, functions, processes, proposals, methods, and / or operational flowcharts disclosed in this document. One or more processors 102 and 202 may generate messages, control information, data, or information according to the descriptions, functions, processes, proposals, methods, and / or operational flowcharts disclosed in this document. One or more processors 102 and 202 may generate a signal (e.g., a baseband signal) including a PDU, SDU, message, control information, data, or information according to the description, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document, and provide the generated signal to one or more transceivers 106 and 206. One or more processors 102 and 202 may receive a signal (e.g., a baseband signal) from one or more transceivers 106 and 206 and obtain the PDU, SDU, message, control information, data, or information according to the description, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document.

[0331] One or more processors 102 and 202 may be referred to as controllers, microcontrollers, microprocessors, or microcomputers. One or more processors 102 and 202 may be implemented by hardware, firmware, software, or a combination thereof. As an 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 and 202. The descriptions, functions, processes, proposals, methods, and / or operational flowcharts disclosed in this document may be implemented using firmware or software, and the firmware or software may be configured to include modules, processes, or functions. Firmware or software configured to execute the descriptions, functions, processes, proposals, methods, and / or operational flowcharts disclosed in this document may be included in one or more processors 102 and 202 or stored in one or more memories 104 and 204 to be driven by one or more processors 102 and 202. The descriptions, functions, processes, proposals, methods and / or operational flow charts disclosed in this document may be implemented in the form of codes, commands and / or command sets using firmware or software.

[0332] One or more memories 104 and 204 may be connected to one or more processors 102 and 202 and store various types of data, signals, messages, information, programs, codes, instructions and / or commands. One or more memories 104 and 204 may be configured by read-only memory (ROM), random access memory (RAM), electrically erasable programmable read-only memory (EPROM), flash memory, hard disk drive, register, cache memory, computer-readable storage medium and / or a combination thereof. One or more memories 104 and 204 may be located inside and / or outside of one or more processors 102 and 202. One or more memories 104 and 204 may be connected to one or more processors 102 and 202 by various technologies such as wired or wireless connections.

[0333] One or more transceivers 106 and 206 may send user data, control information and / or radio signals / channels mentioned in the method and / or operation flow chart of this document to one or more other devices. One or more transceivers 106 and 206 may receive user data, control information and / or radio signals / channels mentioned in the description, function, process, proposal, method and / or operation flow chart disclosed in this document from one or more other devices. For example, one or more transceivers 106 and 206 may be connected to one or more processors 102 and 202 and send and receive radio signals. For example, one or more processors 102 and 202 may perform control so that one or more transceivers 106 and 206 may send user data, control information or radio signals to one or more other devices. One or more processors 102 and 202 may perform control so that one or more transceivers 106 and 206 may receive user data, control information or radio signals from one or more other devices. One or more transceivers 106 and 206 may be connected to one or more antennas 108 and 208, and one or more transceivers 106 and 206 may be configured to send and receive user data, control information and / or radio signals / channels mentioned in the description, functions, processes, proposals, methods and / or operation flow charts disclosed in this document through one or more antennas 108 and 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 and 206 may convert received radio signals / channels, etc. from RF band signals to baseband signals so as to process received user data, control information, radio signals / channels, etc. using one or more processors 102 and 202. One or more transceivers 106 and 206 may convert user data, control information, radio signals / channels, etc. processed using one or more processors 102 and 202 from baseband signals to RF band signals. To this end, one or more of the transceivers 106 and 206 may include (analog) oscillators and / or filters.

[0334] Fig.24 Another example of a wireless device applied to the present disclosure is shown. The wireless device can be used according to the usage / service (reference Fig. 22 ) are implemented in various forms.

[0335] refer to Fig.24 , the wireless devices 100 and 200 may correspond to Fig.23The wireless devices 100 and 200 of the present invention may be configured by various elements, components, units / parts and / or modules. For example, each of the wireless devices 100 and 200 may include a communication unit 110, a control unit 120, a memory unit 130 and an additional component 140. The communication unit may include a communication circuit 112 and a transceiver 114. For example, the communication circuit 112 may include Fig.23 One or more processors 102 and 202 and / or one or more memories 104 and 204. For example, the transceiver 114 may include Fig.23 The control unit 120 is electrically connected to the communication unit 110, the memory unit 130, and the additional components 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. The control unit 120 may transmit information stored in the memory unit 130 to the outside (e.g., other communication devices) via the communication unit 110 through a wireless / wired interface, or store information received from the outside (e.g., other communication devices) via the communication unit 110 in the memory unit 130 through a wireless / wired interface.

[0336] The additional components 140 may be configured differently depending on the type of wireless device. For example, the additional components 140 may include at least one of a power supply unit / battery, an input / output (I / O) unit, a drive unit, and a computing unit. The wireless device may be configured in the form of, but not limited to, a robot ( Fig. 22 100a), vehicles ( Fig. 22 100b-1 and 100b-2), XR devices ( Fig. 22 100c), handheld device ( Fig. 22 100d), household appliances ( Fig. 22 100e), IoT devices ( Fig. 22 100f), digital broadcasting terminals, holographic devices, public safety devices, MTC devices, medical devices, fintech devices (or financial devices), security devices, climate / environmental devices, AI servers / devices ( Fig. 22 400), BS ( Fig. 22 200), network node, etc. The wireless device can be used in a mobile or fixed location according to the use case / service.

[0337] exist Fig.24In the wireless devices 100 and 200, various elements, components, units / parts and / or modules in the wireless devices 100 and 200 may all be connected to each other through a wired interface, or at least a part thereof may be wirelessly connected through the communication unit 110. For example, in each of the wireless devices 100 and 200, the control unit 120 and the communication unit 110 may be wired, and the control unit 120 and the first unit (e.g., 130 and 140) may be wirelessly connected through the communication unit 110. The various elements, components, units / parts and / or modules within the wireless devices 100 and 200 may also include one or more elements. For example, the control unit 120 may be configured by a collection of one or more processors. As an example, the control unit 120 may be configured by a collection of a communication control processor, an application processor, an electronic control unit (ECU), a graphics processing unit, and a memory control processor. As another example, the memory unit 130 may be configured by a random access memory (RAM), a dynamic RAM (DRAM), a read-only memory (ROM), a flash memory, a volatile memory, a non-volatile memory, and / or a combination thereof.

[0338] Fig.25 A vehicle or an autonomous vehicle applied to the present disclosure is shown. The vehicle or the autonomous vehicle may be implemented by a mobile robot, a car, a train, a manned / unmanned aerial vehicle (AV), a ship, etc.

[0339] refer to Fig.25 , the vehicle or autonomous driving vehicle 100 may include an antenna unit 108, a communication unit 110, a control unit 120, a drive 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 Fig.24 Block 110 / 130 / 140.

[0340] The communication unit 110 may send and receive signals (e.g., data and control signals) to and from external devices such as other vehicles, BSs (e.g., gNBs and roadside units), and servers. The control unit 120 may perform various operations by controlling elements of the vehicle or autonomous driving vehicle 100. The control unit 120 may include an electronic control unit (ECU). The drive unit 140a may enable the vehicle or autonomous driving vehicle 100 to travel on a road. The drive unit 140a may include an engine, a motor, a power system, wheels, brakes, a steering device, etc. The power supply unit 140b may supply power to the vehicle or autonomous driving vehicle 100, and include a wired / wireless charging circuit, a battery, etc. The sensor unit 140c may acquire vehicle status, surrounding environment information, user information, etc. The sensor unit 140c may include an inertial measurement unit (IMU) sensor, a collision sensor, a wheel sensor, a speed sensor, a slope sensor, a weight sensor, a heading sensor, a position module, a vehicle forward / reverse sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor, a temperature sensor, a depth sensor, an ultrasonic sensor, a lighting sensor, a pedal position sensor, etc. The autonomous driving unit 140d may implement a technology for maintaining a lane in which the vehicle is traveling, a technology for automatically adjusting a speed (e.g., adaptive cruise control), a technology for autonomously traveling along a determined path, a technology for traveling by automatically setting a path if a destination is set, and the like.

[0341] For example, the communication unit 110 may receive map data, traffic information data, etc. from an external server. The autonomous driving unit 140d may generate an autonomous driving path and a driving plan from the obtained data. The control unit 120 may control the drive unit 140a so that the vehicle or the autonomous driving vehicle 100 may move along the autonomous driving path according to the driving plan (e.g., speed / direction control). In the middle of autonomous driving, the communication unit 110 may aperiodically / periodically obtain the latest traffic information data from the external server and obtain surrounding traffic information data from neighboring vehicles. In the middle of autonomous driving, the sensor unit 140c may obtain vehicle status and / or surrounding environment information. The autonomous driving unit 140d may update the autonomous driving path and driving plan based on the newly obtained data / information. The communication unit 110 may transmit information about the vehicle position, autonomous driving path, and / or driving plan to the external server. The external server may predict traffic information data using AI technology, etc. based on information collected from the vehicle or autonomous driving vehicle, and provide the predicted traffic information data to the vehicle or autonomous driving vehicle.

[0342] Fig.26 is a diagram illustrating a DRX operation of a UE according to an embodiment of the present disclosure.

[0343] The UE may perform DRX operation in the process and / or method described / proposed above. A UE configured with DRX may reduce power consumption by discontinuously receiving DL signals. DRX may be performed in the RRC_IDLE state, the RRC_INACTIVE state, and the RRC_CONNECTED state. The UE performs DRX in the RRC_IDLE state and the RRC_INACTIVE state to discontinuously receive paging signals. DRX in the RRC_CONNECTED state (RRC_CONNECTED DRX) will be described below.

[0344] refer to Fig.26 , the DRX cycle includes an on-duration and a DRX opportunity. The DRX cycle defines the time interval between periodic repetitions of the on-duration. The on-duration is the time period during which the UE monitors the PDCCH. When the UE is configured with DRX, the UE performs PDCCH monitoring during the on-duration. When the UE successfully detects the PDCCH during the PDCCH monitoring, the UE starts the inactivity timer and remains awake. Conversely, when the UE fails to detect any PDCCH during the PDCCH monitoring, the UE transitions to a sleep state after the on-duration. Therefore, when DRX is configured, PDCCH monitoring / reception may be performed discontinuously in the time domain in the process and / or method described / proposed above. For example, when DRX is configured, the PDCCH reception timing (e.g., a time slot with a PDCCH SS) may be configured discontinuously according to the DRX configuration in the present disclosure. Conversely, when DRX is not configured, PDCCH monitoring / reception may be performed continuously in the time domain. For example, when DRX is not configured, the PDCCH reception timing (e.g., a time slot with a PDCCH SS) may be configured continuously in the present disclosure. Regardless of whether DRX is configured, PDCCH monitoring may be restricted during time periods configured as measurement gaps.

[0345] The DRX configuration information is received through high-layer signaling (eg, RRC signaling), and DRX on / off is controlled by a DRX command from the MAC layer. Once DRX is configured, the UE may perform PDCCH monitoring discontinuously while executing the above-described / proposed procedures and / or methods.

[0346] The above-mentioned embodiments correspond to the combination of elements and features of the present disclosure in a prescribed form. And, unless explicitly mentioned, each element or feature may be regarded as selective. Each element or feature may be implemented in a form that is not combined with other elements or features. In addition, it is possible to implement the embodiments of the present disclosure by partially combining elements and / or features together. The order of operations described for each embodiment of the present disclosure may be modified. Some configurations or features of an embodiment may be included in another embodiment, or may replace the corresponding configuration or features of another embodiment. And, it is obvious to understand that the embodiment is configured by combining claims that do not have a clear reference relationship in the attached claims, or may be included as a new claim by modification after submitting the application.

[0347] Those skilled in the art will appreciate that the present disclosure may be implemented in other specific forms other than those described herein without departing from the spirit and essential characteristics of the present disclosure. Therefore, the above-described embodiments should be interpreted as being illustrative in all respects, rather than restrictive. The scope of the present disclosure should be determined by the appended claims and their legal equivalents, rather than by the above description, and all changes falling within the meaning and equivalent scope of the appended claims are intended to be covered therein.

[0348] Industrial Applicability

[0349] The present disclosure is applicable to UE, BS or other devices in a wireless mobile communication system.

Claims

1. A method for reporting a channel state by a user equipment (UE) in a wireless communication system, the method comprising: Receive channel state information (CSI) related configuration; Based on the CSI related configuration, perform a plurality of CSI measurements associated with each other; as well as sending a CSI report based on the CSI measurement, wherein the plurality of associated CSI measurements include a first CSI measurement performed based on an artificial intelligence / machine learning (AI / ML) model and a second CSI measurement performed without the AI / ML model, and The CSI report includes the output of the AI / ML model obtained from the first CSI measurement and channel quality indicator (CQI) information obtained from the second CSI measurement.

2. The method according to claim 1, wherein: The CQI information includes a second CQI value obtained by applying a CQI offset to a first CQI value calculated from the second CSI measurement.

3. The method according to claim 2, wherein: The CQI offset is provided through network signaling.

4. The method according to claim 1, wherein: Based on the first CSI measurement being triggered, the second CQI measurement associated with the first CSI measurement is performed together with the first CSI measurement.

5. The method according to claim 1, wherein: The first CSI measurement and the second CSI measurement are respectively performed in a first CSI resource and a second CSI resource associated with each other, and The CSI configuration includes information for associating the first CSI resource with the second CSI resource.

6. The method according to claim 1, wherein: a first CSI report including the output of the AI / ML model obtained from the first CSI measurement is sent over a physical uplink shared channel (PUSCH), and Wherein, a second CSI report including the CQI information obtained from the second CSI measurement is sent via a physical uplink control channel (PUCCH).

7. The method according to claim 1, wherein: The CSI report is sent via a physical uplink channel.

8. The method according to claim 1, wherein: The output of the AI / ML model configured for the UE is provided as an input to the AI / ML model configured in the network.

9. The method according to claim 1, wherein: The multiple associated CSI measurements are counted as N CSI processes, and Here, N is a value equal to or greater than 1 and is provided through network signaling.

10. A computer-readable recording medium recording a program for executing the method according to claim 1.

11. An apparatus for wireless communication, the apparatus comprising: a memory configured to store instructions; as well as a processor configured to perform operations by executing the instructions, The operation of the processor includes: Receive channel state information (CSI) related configuration; Based on the CSI related configuration, performing a plurality of CSI measurements associated with each other; and sending a CSI report based on the CSI measurement, wherein the plurality of associated CSI measurements include a first CSI measurement performed based on an artificial intelligence / machine learning (AI / ML) model and a second CSI measurement performed without the AI / ML model, and The CSI report includes the output of the AI / ML model obtained from the first CSI measurement and channel quality indicator (CQI) information obtained from the second CSI measurement.

12. The apparatus of claim 11, further comprising: Transceiver, The device is a user equipment (UE) operating in a wireless communication system.

13. The device of claim 11, wherein: The apparatus is a processing device configured to control a user equipment (UE) operating in a wireless communication system.

14. A method for receiving a channel state report by a base station (BS) in a wireless communication system, the method comprising: Send channel state information (CSI) related configuration; as well as receiving a plurality of CSI measurements associated with each other based on the CSI-related configuration, wherein the plurality of associated CSI measurements include a first CSI measurement performed based on an artificial intelligence / machine learning (AI / ML) model and a second CSI measurement performed without the AI / ML model, and The CSI report includes the output of the AI / ML model obtained from the first CSI measurement and channel quality indicator (CQI) information obtained from the second CSI measurement.

15. A base station (BS) for wireless communication, the BS comprising: a memory configured to store instructions; as well as a processor configured to perform operations by executing the instructions, The operation of the processor includes: Send channel state information (CSI) related configuration; and receiving a plurality of CSI measurements associated with each other based on the CSI-related configuration, wherein the plurality of associated CSI measurements include a first CSI measurement performed based on an artificial intelligence / machine learning (AI / ML) model and a second CSI measurement performed without the AI / ML model, and The CSI report includes the output of the AI / ML model obtained from the first CSI measurement and channel quality indicator (CQI) information obtained from the second CSI measurement.

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