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

Abstract

A terminal according to at least one embodiment disclosed in the present specification may: report, to a network, AI processing unit (APU) information on the number of artificial intelligence / machine learning (AI / ML) processes that can be processed simultaneously; receiving, from the network, information indicating operation of the terminal related to the at least one AI / ML model; and simultaneously performing at least some terminal operations.

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] In general, 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 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 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] In one aspect of the present disclosure, a method for operating a user equipment (UE) in a wireless communication system is provided herein. The method may include the following steps: reporting to a network artificial intelligence (AI) processing unit (APU) information related to the number of artificial intelligence / machine learning (AI / ML) processes that can be processed simultaneously in the UE; receiving from the network information indicating UE operations related to at least one AI / ML model; and performing at least a portion of the UE operations simultaneously. Based on the fact that, in a specific time resource, the UE operation indicated by the network exceeds the number of AI / ML processes reported to the network through the APU information, the UE may determine one or more UE operations to be excluded from the indicated UE operation and report to the network information about the one or more UE operations determined to be excluded.

[0008] The UE may discard, deactivate, or suspend one or more UE operations determined to be excluded.

[0009] The UE may perform remaining UE operations among the UE operations indicated by the network except for one or more UE operations determined to be excluded based on the APU information.

[0010] UE operations related to at least one AI / ML model indicated by the network may include at least one of AI / ML-based beam management, AI / ML-based positioning, or AI / ML-based channel state information (CSI) calculation.

[0011] The APU information may be configured separately from the channel state information (CSI) processing unit (CPU) information, which is related to the number of CSI calculations that can be processed simultaneously in the UE.

[0012] Alternatively, the APU information may be configured as part of the CPU information regarding the number of CSI calculations that can be processed simultaneously in the UE. One AI / ML-based CSI calculation may be counted as "X" non-AI / ML-based CSI calculations. In this case, "X" may be configured based on information received from the network. Alternatively, "X" may be determined based on at least one of the capabilities of the UE and an AI / ML model related to the relevant AI / ML-based CSI calculation.

[0013] The APU information may be configured for each component carrier (CC) or configured to cover a plurality of CCs.

[0014] One or more UE operations to be excluded may be determined based on a priority.

[0015] In another aspect of the present disclosure, there is provided a computer-readable recording medium having recorded thereon a program for executing the above-mentioned method.

[0016] In another aspect of the present disclosure, a UE configured to perform the above method is provided herein.

[0017] In another aspect of the present disclosure, a device configured to control a UE to execute the above method is provided herein.

[0018] In another aspect of the present disclosure, a method for operating a base station (BS) in a wireless communication system is provided herein. The method may include the following steps: receiving APU information related to the number of AI / ML processes that can be processed simultaneously in the UE from the UE; sending information indicating UE operations related to at least one AI / ML model to the UE; and receiving information about one or more UE operations to be excluded from the indicated UE operations based on the indicated UE operations exceeding the number of AI / ML processes obtained through the APU information in a specific time resource from the UE.

[0019] In another aspect of the present disclosure, a BS configured to perform the above method is provided herein.

[0020] Beneficial Effects

[0021] According to the present disclosure, wireless signal transmission and reception can be efficiently performed in a wireless communication system.

[0022] 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

[0023] Figure 1 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 are shown.

[0024] Figure 2 Shows the radio frame structure.

[0025] Figure 3 A resource grid showing time slots.

[0026] Figure 4 An exemplary mapping of physical channels in time slots is shown.

[0027] Figure 5 An exemplary physical downlink shared channel (PDSCH) and ACK / NACK transmission and reception process is shown.

[0028] Figure 6 An exemplary physical uplink shared channel (PUSCH) transmission process is shown.

[0029] Figure 7 An example of a channel state information (CSI) related process is shown.

[0030] Figure 8 A diagram for explaining the concept of artificial intelligence / machine learning (AI / ML) / deep learning.

[0031] Figures 9 to 12 Various AI / ML models for deep learning are shown.

[0032] Fig.13 is a diagram illustrating split AI reasoning.

[0033] Fig.14 is a diagram showing the framework of 3GPP Radio Access Network (RAN) intelligence.

[0034] Figures 15 to 17 Shows the AI ​​model training and reasoning environment.

[0035] Fig.18 It is a diagram used to illustrate AI-based CSI feedback.

[0036] Fig.19is a diagram for explaining operation of a user equipment (UE) according to an embodiment.

[0037] Fig. 20 is a diagram for explaining the operation of a base station (BS) according to an embodiment.

[0038] Figure 21 to Figure 24 An example of the communication system 1 and the wireless device applied to the present disclosure is shown.

[0039] Fig.25 An exemplary discontinuous reception (DRX) operation suitable for use with the present disclosure is shown. DETAILED DESCRIPTION

[0040] 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, and 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.

[0041] 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 for next-generation 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).

[0042] For the sake of brevity, 3GPP NR is mainly described, but the technical concept of the present disclosure is not limited thereto.

[0043] In the present disclosure, the term "setting" may be replaced by "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.

[0044] 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.

[0045] Figure 1 Physical channels used in the 3GPP NR system and a general signal transmission method using the same are shown.

[0046] 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.

[0047] 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.

[0048] The UE may perform a random access procedure to access the BS in steps S103 to S106. For random access, the UE may send 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 sending a PRACH (S105) and receiving a PDCCH and a PDSCH corresponding to the PDCCH (S106).

[0049] After the foregoing process, the UE may receive PDCCH / PDSCH (S107) and send 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 confirmation / negative determination (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 sent on PUCCH, UCI may be sent on PUSCH when control information and business data need to be sent simultaneously. In addition, UCI may be sent aperiodically via PUSCH according to the request / command of the network.

[0050] Figure 2 The 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.

[0051] 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.

[0052] [Table 1]

[0053] SCS(15*2∧u) <![CDATA[N slot symb ]]> <![CDATA[N frame,u slot ]]> <![CDATA[N subframe,u slot ]]> 15KHz(u=0) 14 10 1 30KHz(u=1) 14 20 2 60KHz(u=2) 14 40 4 120KHz(u=3) 14 80 8 240KHz(u=4) 14 160 16

[0054] *N slot symb : The number of symbols in a time slot

[0055] *N frame,u slot : Number of time slots in a frame

[0056] *N subframe,u slot : Number of time slots in a subframe

[0057] 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.

[0058] [Table 2]

[0059] SCS(15*2∧u) <![CDATA[N slot symb ]]> <![CDATA[N frame,u slot ]]> <![CDATA[N subframe,u slot ]]> 60KHz(u=2) 12 40 4

[0060] 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.

[0061] 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).

[0062] Figure 3 A 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 a UE. In a resource grid, each element is referred to as a resource element (RE), and a complex symbol may be mapped to each RE.

[0063] Figure 4An 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.

[0064] The individual physical channels are described in more detail below.

[0065] 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 using 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 used 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 a system information RNTI (SI-RNTI). When the PDCCH is for RAR, the CRC is masked by a random access-RNTI (RA-RNTI).

[0066] 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 set of parameters (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 a CORESET may be configured by high-level signaling.

[0067] 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 according to 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 MIB or high-level signaling. Each CORESET is associated with one or more SSs, and each SS is associated with a CORESET. The SS can be defined based on the following parameters.

[0068] -controlResourceSetId: CORESET related to SS.

[0069] -monitoringSlotPeriodicityAndOffset: PDCCH monitoring periodicity (time slot) and PDCCH monitoring offset (time slot).

[0070] - monitoringSymbolsWithinSlot: PDCCH monitoring symbols in a slot (eg, the first symbol of a CORESET).

[0071] -nrofCandidates: the number of PDCCH candidates for each AL={1,2,4,8,16} (one of 0, 1, 2, 3, 4, 5, 6 and 8).

[0072] *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.

[0073] Table 3 shows the characteristics of each SS.

[0074] [Table 3]

[0075]

[0076] Table 4 shows the DCI format transmitted on the PDCCH.

[0077] [Table 4]

[0078]

[0079] DCI format 0_0 can be used to schedule TB-based (or TB-level) PUSCH, and DCI format 0_1 ​​can be used to schedule TB-based (or TB-level) PUSCH or code block group (CBG)-based (or CBG-level) PUSCH. DCI format 1_0 can be used to schedule TB-based (or TB-level) PDSCH, and DCI format 1_1 can 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 can be called UL grant DCI or UL scheduling information, and DCI format 1_0 / 1_1 can be called 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 can be transmitted to a corresponding group of UEs on a group common PDCCH (a PDCCH pointing to a group of UEs).

[0080] 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.

[0081] PDSCH transmits DL data (e.g., DL shared channel transport block (DL-SCH TB)) and uses modulation schemes such as quadrature phase shift keying (QPSK), 16-ary quadrature amplitude modulation (16QAM), 64QAM, or 256QAM. TB is encoded as a codeword. PDSCH can transmit up to two codewords. Scrambling and modulation mapping can be performed on the basis of the codeword, and the 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.

[0082] PUCCH transmits uplink control information (UCI). UCI includes the following information.

[0083] -SR (Scheduling Request): Information used to request UL-SCH resources.

[0084] -HARQ (Hybrid Automatic Repeat Request) -ACK (Acknowledgement): A response to a DL data packet (e.g., a codeword) on the PDSCH. HARQ-ACK indicates whether the DL data packet was 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.

[0085] -CSI (Channel State Information): Feedback information of DL channels. Multiple Input Multiple Output (MIMO) related feedback information includes RI and PMI.

[0086] 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).

[0087] [Table 5]

[0088]

[0089] 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.

[0090] 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)).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 sent with a DFT-s-OFDM waveform, the UE sends the PUSCH with transform precoding. For example, when transform precoding is not possible (e.g., disabled), the UE may send the PUSCH with a CP-OFDM waveform, and when transform precoding is possible (e.g., enabled), the UE may send the PUSCH with a CP-OFDM or DFT-s-OFDM waveform. PUSCH transmission may be dynamically scheduled by a UL grant in the DCI, or semi-statically scheduled by higher layer (e.g., RRC) signaling (and / or layer 1 (L1) signaling such as PDCCH) (configured scheduling or configured grant). PUSCH transmission may be performed in a codebook-based or non-codebook-based manner.

[0096] 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 (e.g., 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.

[0097] - Frequency domain resource assignment: indicates the RB set assigned to PDSCH.

[0098] - 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.

[0099] -PDSCH-to-HARQ_feedback timing indicator: indicates K1.

[0100] -HARQ process number (4 bits): indicates the HARQ process ID of the data (eg, PDSCH or TB).

[0101] - PUCCH resource indicator (PRI): indicates a PUCCH resource to be used for UCI transmission among multiple PUCCH resources in a PUCCH resource set.

[0102] 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.

[0103] 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.

[0104] Whether the UE should perform spatial bundling for HARQ-ACK responses may be configured for each cell group (e.g., via RRC / high-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.

[0105] 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-by-bit) logical AND operation on the A / N bits of multiple TBs.

[0106] 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.

[0107] 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.

[0108] There are multiple parallel DL HARQ processes at the BS / UE for DL ​​transmissions. Multiple parallel HARQ processes allow continuous DL transmissions 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 media 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.

[0109] 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.

[0110] - Frequency domain resource assignment: indicates the RB set assigned to PUSCH.

[0111] -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 a start and length indicator value (SLIV) or separately.

[0112] 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.

[0113] CSI related operations

[0114] Figure 7 An example of a CSI-related process is shown.

[0115] 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.

[0116] -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.

[0117] - 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).

[0118] - 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.

[0119] -CSI-RS can be configured for one or more UEs. Different CSI-RS configurations can be provided for each UE, or the same CSI-RS configuration can be provided to multiple UEs. CSI-RS can support up to 32 antenna ports. CSI-RS corresponding to N (N is 1 or greater) antenna ports can be mapped to N RE positions within a time-frequency unit corresponding to one time slot and one RB. When N is 2 or greater, N-port CSI-RS can be multiplexed by CDM, FDM and / or TDM methods. CSI-RS can be mapped to the remaining REs except for REs mapped with CORESET, DMRS and SSB. In the frequency domain, CSI-RS can be configured for the entire bandwidth, a partial bandwidth part (BWP) or a partial bandwidth. CSI-RS can be sent in each RB within the bandwidth in which CSI-RS is configured (ie, density = 1), or CSI-RS can be sent in every two RBs (eg, even or odd RBs) (ie, 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.

[0120] -CSI report configuration may include configuration of feedback type, measurement resources, report type, etc. NZP-CSI-RS resource set may be used for CSI report configuration of corresponding UE. NZP-CSI-RS resource set may be associated with CSI-RS or SSB. Multiple periodic NZP-CSI-RS resource sets may be configured as TRS resource sets. (i) Feedback type includes 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 configuration of downlink signals and / or downlink resources on which UE performs measurements to determine feedback information. Measurement resources may be configured as ZP and / or NZP CSI-RS resource sets associated with CSI report configuration. NZP CSI-RS resource sets may include CSI-RS sets or SSB sets. 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.

[0121] 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).

[0122] 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.

[0123] 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 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.

[0124] 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 for single-user MIMO (SU-MIMO) supporting both high-order and low-order. Type II codebooks mainly support multi-user MIMO (MU-MIMO) that can handle 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 solve 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.

[0125] 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.

[0126] - 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.

[0127] - For type IICSI feedback, part 1 includes the RI (if reported), the CQI and an indication of the number of non-zero WB amplitude coefficients for each layer of type IICSI. Part 2 includes the PMI of type IICSI.

[0128] - For enhancement type IICSI feedback, part 1 includes the RI (if reported), CQI and an indication of the total number of non-zero WB amplitude coefficients of all layers of enhancement type IICSI. Part 2 includes the PMI of enhancement type IICSI.

[0129] 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 the Part 2 CSI.

[0130] Semi-persistent CSI reporting performed in PUCCH format 3 or 4 supports Type IICSI feedback, but only supports part 1 of Type IICSI feedback.

[0131] Quasi-isotope (QCL)

[0132] 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.

[0133] 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.

[0134] - "QCL-TypeA": {Doppler shift, Doppler spread, average delay, delay spread}

[0135] - "QCL-TypeB": {Doppler shift, Doppler spread}

[0136] - "QCL-TypeC": {Doppler shift, average delay}

[0137] - "QCL-TypeD": {spatial Rx parameters}

[0138] Beam Management (BM)

[0139] BM refers to a series of processes for acquiring and maintaining a BS beam set (transmit receive 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.

[0140] - Beam measurement: An operation in which a BS or UE measures characteristics of a received beamforming signal.

[0141] -Beam determination: The operation by which a BS or UE selects its Tx / Rx beam.

[0142] -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.

[0143] -Beam reporting: An operation in which the UE reports information about a beamformed signal based on beam measurements.

[0144] The BM process may be divided into (1) a DL BM process using SSB or CSI-RS and (2) a ULBM process using SRS. In addition, each BM process may include a Tx beam sweep for determining a Tx beam and an Rx beam sweep for determining an Rx beam.

[0145] The DLBM 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.

[0146] 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).

[0147] Artificial Intelligence / Machine Learning (AI / ML)

[0148] With the development of AI / ML technology, the nodes and UEs constituting the wireless communication network 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 the BS, the distribution / location / material of the buildings / furniture, the location / moving direction / speed of the 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.

[0149] 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.

[0150] - Artificial Intelligence: This can correspond to all automation where machines replace human work.

[0151] -Machine Learning: Machines learn decision-making patterns from data without explicit programming rules.

[0152] - 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 node network 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).

[0153] Classification of AI / ML types according to various references

[0154] 1. Offline vs. online

[0155] (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.

[0156] (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. Real-time learning is performed on specific data units (batches) collected online, allowing the system to quickly adapt to changing data.

[0157] 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 (online + offline learning) can be performed using subsequently generated real-time data.

[0158] 2. Classification based on AI / ML framework concepts

[0159] (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.

[0160] (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.

[0161] (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.

[0162] 3. Classification based on learning methods

[0163] (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 a known label or outcome. Examples of supervised learning may include: (i) regression: linear regression, logistic regression; (ii) instance-based algorithms: k-nearest neighbor (KNN); (iii) decision tree algorithms: CART; (iv) support vector machines: SVM; (v) Bayesian algorithms: Naive Bayes; and (vi) ensemble algorithms: extreme gradient boosting, packing: random forest. Due to regression and classification problems, supervised learning can be further grouped, and classification predicts labels and regression predicts quantities.

[0164] (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.

[0165] (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 probability between states using various dynamic states of the environment and an AI / ML model of the rewards caused by these states. 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 versus policy-based RL, policy-based RL versus non-policy RL, etc.

[0166] AI / ML Models

[0167] Fig. 9 An example of a feed-forward neural network (FFNN) AI / ML model is shown. Fig. 9 ,FFNN AI / ML model includes input layer, hidden layer and output layer.

[0168] Fig.10 An example of a recurrent neural network (RNN) AI / ML model is shown. 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.

[0169] 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 an 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 an 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 calculation of reducing the size of a feature map by downsampling it (e.g., max pooling or average pooling).

[0170] 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.

[0171] [Formula 1]

[0172] Where h = f(x) = Wx, x′ = g(h) = Vh

[0173] 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.

[0174] Fig.13 is a diagram illustrating split AI reasoning.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] The following describes the functional framework for AI operations.

[0179] Herein, the following terms may be defined to explain AI (or AI / ML) in more detail.

[0180] -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.

[0181] -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.

[0182] -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.

[0183] -AI / ML Inference: The process of using trained AI models to make predictions or draw decisions based on collected data and AI models.

[0184] 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.

[0185] For example, input data may include measurements from UEs or other network entities, feedback from actuators, and outputs from AI models.

[0186] 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 (e.g., 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 ​​algorithm.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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 transformation) based on the reasoning data 12 provided by the data collection function 10.

[0192] 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.

[0193] When available, model performance feedback 14 can be used to monitor the performance of the AI ​​model. However, the feedback can be omitted.

[0194] 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 (e.g., one or more UEs, one or more RAN nodes, one or more network nodes, etc.) or itself.

[0195] 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.

[0196] The definitions of training, validation, and testing in datasets used in AI / ML can be distinguished as follows:

[0197] -Training data: Training data refers to the dataset used to train the model.

[0198] - Validation data: Validation data refers to a dataset used to validate an already trained model. In other words, validation data is a dataset used to prevent overfitting of a typical training dataset.

[0199] 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.

[0200] -Test data: Test data refers to the dataset used for final evaluation. Test data is independent of training.

[0201] 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 test data is included, the training data, validation data, and test data can be divided in a ratio of 6:2:2 (training: validation: test).

[0202] 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.

[0203] Cat 0a) No collaborative framework: AI / ML algorithms are purely implementation-based and do not require changes to the wireless interface.

[0204] Cat 0b) This level involves wireless interfaces suitable for modification of AI / ML algorithms based on efficient implementations, but this level corresponds to a framework without collaboration.

[0205] 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.

[0206] Cat 2) can perform collaborative ML tasks between UE and gNB. This level requires the exchange of AI / ML model commands between network nodes.

[0207] Fig.14 The functions shown may be implemented in a RAN node (e.g., BS, TRP, Central Unit (CU) of a BS), a network node, Operation Administration and Maintenance (OAM) of a network operator, or a UE.

[0208] 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.14If 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.

[0209] Alternatively, Fig.14 Any 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.

[0210] 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).

[0211] 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.

[0212] Step 2: The network nodes train the AI ​​model using the received training data.

[0213] 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.

[0214] For ease of explanation, it is assumed that the AI ​​model is only deployed / updated to RAN node 1.

[0215] Step 4: RAN node 1 receives input data for AI model inference (i.e., inference data) from UE and RAN node 2.

[0216] Step 5: RAN node 1 uses the received inference data to perform AI model inference to generate output data (e.g., prediction or decision).

[0217] Step 6: If applicable, the RAN node 1 may send model performance feedback to the network node.

[0218] 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.

[0219] Step 8: RAN node 1 and RAN node 2 send feedback information to the network node.

[0220] 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).

[0221] Step 1: UE and RAN node 2 send input data (i.e., training data) for AI model training to RAN node 1.

[0222] Step 2: RAN node 1 uses the received training data to train the AI ​​model.

[0223] Step 3: RAN node 1 receives input data for AI model inference (i.e., inference data) from UE and RAN node 2.

[0224] Step 4: RAN node 1 uses the received inference data to perform AI model inference to generate output data (e.g., prediction or decision).

[0225] 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.

[0226] Step 6: RAN node 2 sends feedback information to RAN node 1.

[0227] 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.

[0228] 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.

[0229] Step 2: The RAN node trains the AI ​​model using the received training data.

[0230] 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.

[0231] Step 4: The UE receives input data for AI model inference (ie, inference data) from the RAN node (and / or from other UEs).

[0232] Step 5: The UE performs AI model inference using the received inference data to generate output data (e.g., prediction or decision).

[0233] Step 6: If applicable, the UE may send model performance feedback to the RAN node.

[0234] Step 7: The UE and RAN nodes perform actions based on the output data.

[0235] Step 8: The UE sends feedback information to the RAN node.

[0236] CPU usage rules for CSI reports based on AI / ML

[0237] With the advancement of computing technology and AI / ML technology, nodes and UEs included in wireless communication networks are becoming more and more intelligent and complex. In particular, due to the intelligence of the network, it is expected that various network decision parameter values ​​(e.g., the transmission and reception power of each BS, the transmission power of each UE, the precoder / beam of each BS / UE, the time / frequency resource allocation of each UE, the duplex mode of each BS, etc.) can be quickly optimized and applied based on various network environment parameters (e.g., the distribution / location of BS, the distribution / location / material of buildings / furniture, the location / moving direction / speed of UE, climate information, etc.).

[0238] Hereinafter, a proposal of a CSI processing unit (CPU) when CSI reporting is performed based on AI / ML in / by an evolved network / BS / UE will be described.

[0239] Fig.18 It is a diagram used to illustrate AI-based CSI feedback.

[0240] In a method for reducing CSI feedback payload overhead, a common AI model may be based on compression at the UE and / or BS. For example, the CSI encoder of the UE and the CSI decoder of the BS may be as follows: Fig.18 Assumptions shown. The encoder and decoder can be related to autoencoders, and convolutional neural networks (CNNs) can be used to efficiently reduce channel size / dimensionality.

[0241] In AI / ML based payload reduction, what to compress 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. Implicit feedback has the advantage of lower feedback overhead compared to explicit feedback. When AI / ML is used for CSI payload reduction, information about the compressed original channel can be fed back to the gNB, which may lead to explicit feedback.

[0242] As another example of AI / ML-based CSI feedback, compression of existing PMI, such as Type I and Type I ICSI, may be considered. In Rel-16 / 17 Type II CSI, linear-based vectors (e.g., DFT vectors) are used for compression in the frequency domain to reduce the payload of Rel-15 Type I ICSI. Therefore, AI / ML may be used to further reduce the payload of existing CSI and improve its performance.

[0243] As described above, AI / ML based CSI reporting may be considered. Assuming that both the BS and the UE have AI / ML capabilities (training and / or inference), autoencoder-like CSI reporting may be performed. Alternatively, one-sided AI / ML (at the network or UE) may be considered. For one-sided AI / ML, specific parameter optimizations (performance and / or payload) may be considered to optimize the existing legacy codebook based on AI / ML.

[0244] For the generation / calculation of CSI (based on AI / ML), in order to manage the CSI processor of the UE, a CPU was introduced in NR Rel. 17. Table 6 is an excerpt from TS 38.214 about the CPU.

[0245] [Table 6]

[0246]

[0247]

[0248]

[0249] Proposal 1

[0250] In the case of AI / ML-based CSI reporting, when the UE performs CSI generation through the reasoning operation of the AI / ML model, the CPU occupancy is assumed to be X, where the value of X is a value configured by the BS to the UE or a predefined value.

[0251] AI / ML-based CSI reporting can be roughly classified into two types: dual-sided AI / ML and single-sided AI / ML. In both cases, it can be assumed that the BS (or network AI / ML server) performs training / inference and signals the trained AI / ML model to the UE, and the UE operates only based on AI / ML reasoning. As the entity responsible for training, the BS (or network AI / ML server) can configure / indicate the corresponding value based on the computational complexity of the reasoning (based on the floating point operations (FLOPs) required for AI / ML reasoning) and / or the report of the UE capabilities related to CSI processing / memory. When indicating a value of X, the UE assumes the CPU occupancy configured / indicated by the BS as X (e.g., O CPU =X) and calculates the AI / ML based CSI after the UE's CPU occupation operation, which is specified in Table 6 extracted from TS 38.214.

[0252] In the case of AI / ML inference, the variable multiplication between nodes in the pre-trained model and the activation functions of each node (e.g., sigmoid, rectified linear unit (ReLU), hyperbolic tangent (tanh), etc.) are processed without the burden of training. Therefore, the complexity of the inference operation is much lower than that of training. However, for performance improvement, deeper and larger models are considered. In these cases, it is obvious that the computational complexity increases due to the increase in the number of variables. Therefore, in the AI / ML model, the CPU occupancy may vary depending on the amount of computation associated with the AI / ML model parameters (e.g., the number of variables, the number of nodes, the number of hidden layers, etc.) and / or the amount of computation involved in the pre-processing / post-processing of the input / output data. In addition, the AI / ML model is scalable, and different AI / ML models can be configured depending on the usage. In order to evenly reflect the diversity of AI / ML models, the following suggestions are made.

[0253] Proposal 1-1

[0254] The UE may report information about the AI / ML models that can be processed with one CPU occupancy to the BS (as UE capabilities).

[0255] The information about the AI / ML model that can be processed using one CPU occupancy in Proposal 1-1 may include some or all of the following: the number of variables, the number of nodes, and the number of hidden layers. Alternatively, a value based on a specific computing unit (e.g., FLOP) that can be processed using one CPU occupancy may be reported.

[0256] As another example, the BS may indicate / configure the UE with a threshold that can be handled using one CPU occupancy. Since this value may vary depending on the performance of the UE, a relatively loose value may be indicated. Based on the UE capabilities, the thresholds may be divided into multiple groups (e.g., high, medium, low, etc.), and specific thresholds may be indicated for each group. Alternatively, the BS may configure or predefine the CPU required for CSI reporting based on a specific AI / ML model, and then indicate / configure the CPUs of each group to the UE. For example, the value X in Proposal 1 may be configured or predefined as shown in Table 1. UE capabilities related to AI / ML may be reported by the UE.

[0257] Table 7 shows an example of configuring the value of X based on UE capabilities.

[0258] [Table 7]

[0259] Group id X 1- High-performance UE 1 2-Medium performance UE 2 3- Low performance UE 3

[0260] Alternatively, if a specific AI / ML model or version of a model is specified, the UE may report its capabilities for each model / version.

[0261] Proposal 1-2

[0262] In addition to the CPU, an AI / ML processing unit (APU) may be introduced to allow the UE to efficiently manage the processing of multiple AI / ML models.

[0263] In Proposal 1-2, the APU may allow reporting of the number of AI / ML modules / functions (A) that the UE can simultaneously compute per CC or across CCs. If B APUs are occupied, the UE may assume that the UE has AB unoccupied APUs. In this case, if the number of enabled or triggered AI / ML models exceeds AB, the selected AB AI / ML models may be enabled according to a predetermined priority rule, and the UE may report information about the unselected AI / ML models to the BS. The APU may be assumed to be occupied from the time signaling (e.g., MAC-CE / DCI) that triggers / enables a specific AI / ML model is received until the time deactivation, termination, or switching signaling is received.

[0264] Alternatively, the APUs may be integrally managed / applied via AI / ML models, and the number of APUs required for each AI / ML model may vary.

[0265] As another example, consider configuring and indicating the APU separately for AI / ML-based CSI reporting, so that the UE can report both the maximum supported APU and CPU values ​​as capabilities. In this case, the occupancy rules of the APU are the same as the occupancy rules of the CPU, but the occupancy rules of the APU are specifically used to manage AI / ML-based CSI reporting. The statement that the APU occupancy rules are the same as the occupancy rules of the CPU means that, as in traditional systems, it is assumed that one CSI report associated with one CSI-RS resource occupies K APUs (e.g., K=1). Alternatively, since the APU can be used across multiple CSI resources as in the case of UE's CSI prediction, the K value may have a value of 0.5. In this case, it can be assumed that one CSI report associated with two resources occupies one APU.

[0266] The main use case of the APU can be used for AI / ML reasoning. However, if the UE has training capabilities, a separate unit for managing training and reasoning can be introduced. For example, the APU can be managed separately as APU-training and APU-reasoning. In this case, if the APU-reasoning of the UE is all occupied and some or all of the APU-training is not occupied, the APU-training can be borrowed and used according to specific rules. Conversely, if the APU-training of the UE is all occupied and some or all of the APU-reasoning is not occupied, the APU-reasoning can be borrowed and used according to specific rules. Specific rules refer to rules for exchanging the uses of APU-training and APU-reasoning. For example, the occupancy of each APU can be described by a function (e.g., a linear equation or a polynomial equation). The following linear equation can be used: w*APU-reasoning+b=APU-training, where w is a coefficient for matching the capabilities of APU-training and APU-reasoning, and b is an offset. For example, if w=2 and b=0, it means that the APU required for processing APU-training is twice the APU required for APU-reasoning.

[0267] In addition, when APU-training and APU-inference are integrated and operated / applied with a single APU, if there is an APU shortage, training may take precedence over inference and occupy the APU (or inference may return the APU already occupied by inference for use by training) (the reverse operation is also possible). If inference is not allowed due to APU shortage, a default mode may be configured. For example, in the case of CSI reporting, it may fall back to type I CSI reporting as the default mode. Alternatively, if inference is not allowed due to APU shortage, AI / ML model inference may be discarded or delayed to apply inference. Here, "delay" means waiting until an unoccupied APU equal to or greater than the APU occupied by the AI / ML model is available. However, if the delay is too long, problems such as buffer size, memory size, etc. may arise. Therefore, a maximum window size or timer may be introduced to limit the delay. Waiting may be performed until the window or timer expires. If it is ensured that the APU is not occupied before expiration, AI / ML inference may be applied. Otherwise, AI / ML inference may be discarded. Information about the discard or delay of the AI / ML model may be reported to the BS or AI / ML server.

[0268] Proposals 1-3

[0269] If the current APU occupancy value of the UE is 0 (i.e., L=0) and the BS triggers training / update / inference of the AI / ML model, for faster training / update / inference of the AI / ML model, it is assumed that the UE occupies as many APUs (N) as the UE can process simultaneously per CC or across CCs. APU : The number of AI / ML model training / update / inference supported by the UE at the same time N APU ). In other words, for fast training / update / inference of specific AI / ML models, the APU occupancy is O APU =N APU .

[0270] In proposals 1-3, for fast training / update / inference, the BS can configure / instruct the UE to occupy all necessary APUs. Alternatively, if there is a priority for a specific AI / ML model, it can be agreed that proposals 1-3 are applied only when the training / update / inference of the AI / ML model with the highest priority is triggered.

[0271] Proposal 2

[0272] When AI / ML functionality is modularized and multiple AI / ML models (e.g., Y) participate in a single CSI report, the CPU occupancy is considered to be Y or Y+1.

[0273] Typically, CSI reporting is performed by measuring raw channel data through channel measurement (CMR) and / or interference measurement (IMR) based on a specific RS (e.g., CSI-RS or SSB) configured by the BS. The data is then preprocessed (e.g., eigenvalue decomposition) as needed, and based on the preprocessing, the preferred CRI / RI / CQI / PMI or SSBRI / CRI / L1-RSRP / L1-SINR is calculated. The entire process can be processed using a single AI / ML model. Alternatively, the process may be divided into multiple modules, and modules / functions with higher computational complexity or modules / functions that can be replaced with lower complexity modules / functions that provide medium performance may be replaced by the AI / ML model. Therefore, the number of modules (Y) participating in modular AI / ML-based CSI reporting is considered as CPU occupancy. If these modules are mixed with other traditional modules / functions, an additional count of 1 may be added and considered as Y+1. Even if a specific module (e.g., channel estimation) is implemented in the UE, the UE may report information (e.g., the number of modules or model information) to the BS, which supports scheduling flexibility of the BS.

[0274] Proposal 3

[0275] If the current CPU occupancy value of the UE is 0 (i.e., L=0) and the BS triggers AI / ML-based CSI reporting, then for faster AI / ML-based CSI reporting, it is assumed that the UE occupies as many CPUs as the UE can process simultaneously per CC or across CCs (N CPU : The UE indicates the number of supported simultaneous CSI calculations N using the parameters simultaneousCSI-ReportsPerCC in the component carrier and simultaneousCSI-ReportsAllCC across all component carriers CPU ). In other words, for AI / ML-based CSI reporting, the CPU usage is 0 CPU =N CPU .

[0276] In the case of AI / ML based CSI computation, if the size of the AI / ML model performing the inference is not significant, the computational effort may be low, thereby allowing for faster CSI reporting. In particular, in a two-sided model where both the BS and the UE perform inference using the AI / ML model, the UE may occupy all available CPU to perform fast AI / ML based CSI reporting, as described in Proposal 3.

[0277] As another example, in Proposal 3, N may be configured and managed separately for AI / ML-based CSI reporting and traditional CSI reporting. CPU For example, N can be introduced separately CPU_AI , and NCPU_AI Alternatively, if a specific number of AI / ML-based CSI reports are triggered (e.g., more than one report), then N CPU Can be replaced by N CPU_AI Here, N CPU_AI It can be a value determined based on the UE capability report. Alternatively, the BS can be N CPU Defines multiple values ​​and indicates to the UE which value to use. This is used to allow the BS to configure / indicate a specific N according to the CSI report scheduled by the BS. CPU value, thereby increasing the flexibility of both the BS and the UE. Alternatively, the UE may be configured with N CPU _ AI Multiple values ​​of and specifically applied to different AI / ML model groups (AI / ML model (group) specific). For example, if two values ​​are applied: N CPU_AI-1 and N CPU_AI-2 , and there are multiple AI / ML models, then multiple AI / ML models can be mapped to N CPU_AI-1 and N CPU_AI-2 In other words, for the AI / ML models belonging to group 1, each of N CPU_AI-1 The CPU usage is managed individually as an upper limit, and for AI / ML models in Group 2, N CPU_AI-2 The CPU occupancy is managed individually as an upper limit. Information about such grouping may be based on UE capability report or configured / indicated by the BS.

[0278] Proposal 4

[0279] To enhance scheduling flexibility, the BS may adjust the CSI priority of AI / ML based CSI reporting by additionally signaling a CSI priority indicator.

[0280] As described in Table 8 below, when multiple CSI reports collide in time and / or frequency resources, or when multiple PUCCHs and / or PUSCHs carrying CSI are multiplexed, a specific CSI report may be discarded based on the following priority rules. Regarding CSI priority rules, according to the rules in Table 8 extracted from the standard document, Pri iCSI(y,k,c,s)CSI reports with lower values ​​of have higher priority. In order to adjust the priority of AI / ML-based CSI reports based on this structure, the BS may add a 1-bit field to the DCI that schedules the CSI report and indicate whether the priority is high or low. For example, priority adjustment may be performed as follows: by setting k to 0 or 1 in the following formula 2, a higher or equal priority may be indicated compared to an existing normal CSI report (e.g., type 1 CSI, type 2 CSI, etc.). As another example, the range of k values ​​in formula 2 may be expanded to 0, 1, and 2, allowing for greater flexibility in priority. In this case, the field size of the priority may be increased to 2 bits. The indications in Proposal 4 may be specified differently for each AI / ML model.

[0281] [Formula 2]

[0282] Pri iCSI(y,k,c,s) =3·N cells *M s *y+N cells *M s *k+M s *c+s

[0283] Alternatively, by setting k=0 for AI / ML based CSI reporting, AI / ML based CSI reporting can be configured to have the same priority as beam reporting.

[0284] [Table 8]

[0285]

[0286]

[0287] Proposal 5

[0288] To support fast CSI reporting in AI / ML model inference-based CSI reporting of a UE, a value (eg, Z, Z' or CSI reference resource timing) that is smaller than a previously defined CSI calculation delay may be defined / configured.

[0289] Proposal 5 is intended to support situations where model training is completed and the UE needs to perform fast CSI reporting based on AI / ML model reasoning. As proposed below, values ​​smaller than those in Table 5.4-1 or Table 5.4-2 of TS 38.214 may be defined and used for AI / ML-based CSI reporting, as shown in Table 9 below. Alternatively, the Z / Z' values ​​from Table 5.4-1 may be used for AI / ML-based CSI reporting. Proposal 5 may be further extended by defining and using specific Z / Z' values ​​according to an AI / ML model or model group. A model group refers to a set of AI / ML models that can perform CSI calculations within a specific range of Z / Z' values. If the UE reports information related to the corresponding Z / Z' value as a capability, the BS may configure which Z / Z' value to use based on the information.

[0290] [Table 9]

[0291]

[0292]

[0293]

[0294] As described above, when a network or BS to which AI technology is applied or a network / BS with advanced performance (implemented by other means) is implemented, it is expected that integrated control and / or real-time collaboration between BSs will allow dynamic optimization of interference and services. The present disclosure proposes a resource control and CSI-related signaling method suitable for the above purpose. Although the present disclosure is described based on a 3GPP NR system within the current system architecture, it is obvious that the present disclosure is also applicable to other communication systems.

[0295] Fig.19 It is a diagram for explaining the operation of a UE according to an embodiment.

[0296] Reference Fig.19 , the UE may report to the network APU information (A05) related to the number of AI / ML processes that can be processed simultaneously in the UE.

[0297] The UE may receive information indicating UE operation related to at least one AI / ML model from the network (A10).

[0298] The UE may perform at least a portion of the UE operations simultaneously (A15).

[0299] Based on the fact that the UE operations indicated by the network exceed the number of AI / ML processes reported to the network through the APU information in a specific time resource, the UE may determine one or more UE operations to be excluded from the indicated UE operations and report information about the one or more UE operations determined to be excluded to the network.

[0300] The UE may discard, deactivate, or suspend one or more UE operations determined to be excluded.

[0301] The UE may perform remaining UE operations among the UE operations indicated by the network except for one or more UE operations determined to be excluded based on the APU information.

[0302] UE operations related to at least one AI / ML model indicated by the network may include at least one of AI / ML-based beam management, AI / ML-based positioning, or AI / ML-based CSI calculation.

[0303] The APU information may be configured separately from the CPU information regarding the number of CSI calculations that may be processed simultaneously in the UE.

[0304] Alternatively, the APU information may be configured as part of the CPU information regarding the number of CSI calculations that can be processed simultaneously in the UE. One AI / ML-based CSI calculation may be counted as "X" non-AI / ML-based CSI calculations. In this case, "X" may be configured based on information received from the network. Alternatively, "X" may be determined based on at least one of the capabilities of the UE and an AI / ML model related to the relevant AI / ML-based CSI calculation.

[0305] The APU information may be configured for each component carrier (CC), or may be configured to cover multiple CCs.

[0306] One or more UE operations to be excluded may be determined based on a priority.

[0307] Fig. 20 is a diagram for explaining the operation of a BS according to an embodiment.

[0308] Reference Fig. 20 The BS may receive APU information related to the number of AI / ML processes that can be simultaneously processed in the UE from the UE (B05).

[0309] The BS may transmit information indicating UE operation related to at least one AI / ML model to the UE ( B10 ).

[0310] Based on the indicated UE operation exceeding the number of AI / ML processes obtained through the APU information in a specific time resource, the BS may receive information about one or more UE operations to be excluded from the indicated UE operation from the UE ( B15 ).

[0311] Fig.21 A communication system 1 applied to the present disclosure is shown.

[0312] Reference Fig.21, the 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.

[0313] The wireless devices 100a to 100f may be connected to the network 300 via the BS 200. The 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, 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.

[0314] Wireless communication / connection 150a, 150b or 150c may be established between wireless devices 100a to 100f / BS200 or BS200 / BS200. 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 processes for sending / receiving radio signals, various signal processing processes (e.g., channel coding / decoding, modulation / demodulation, and resource mapping / demapping), and resource allocation processes may be performed based on various proposals of the present disclosure.

[0315] Fig. 22 A wireless device suitable for use with the present disclosure is shown.

[0316] Reference Fig. 22 , the first wireless device 100 and the second wireless device 200 may transmit radio signals via various RATs (e.g., LTE and NR). Herein, {the first wireless device 100 and the second wireless device 200} may correspond to Fig.21 {wireless device 100x and BS 200} and / or {wireless device 100x and wireless device 100x}.

[0317] 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.

[0318] 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.

[0319] In the following, 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.

[0320] 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 flow charts 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 flow charts 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.

[0321] 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.

[0322] 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.

[0323] Fig.23 Another example of a wireless device applicable to the present disclosure is shown. The wireless device can be used according to the usage / service (refer to Fig.21 ) are implemented in various forms.

[0324] Reference Fig.23 , the wireless devices 100 and 200 may correspond to Fig. 22The 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. 22 One or more processors 102 and 202 and / or one or more memories 104 and 204. For example, the transceiver 114 may include Fig. 22 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.

[0325] 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.21 100a), vehicles ( Fig.21 100b-1 and 100b-2), XR devices ( Fig.21 100c), handheld device ( Fig.21 100d), household appliances ( Fig.21 100e), IoT devices ( Fig.21 100f), digital broadcasting terminal, holographic device, public safety device, MTC device, medical device, fintech device (or financial device), security device, climate / environmental device, AI server / device ( Fig.21 400), BS( Fig.21 200), network nodes, etc. The wireless device can be used in a mobile or fixed location according to the usage example / service.

[0326] exist Fig.23In 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.

[0327] Fig.24 A vehicle or an autonomous vehicle applied to the present disclosure is shown. The vehicle or the autonomous vehicle can be implemented by a mobile robot, a car, a train, a manned / unmanned aerial vehicle (AV), a ship, etc.

[0328] Reference Fig.24 , 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.23 Block 110 / 130 / 140.

[0329] 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 vehicle 100. The control unit 120 may include an electronic control unit (ECU). The drive unit 140a may enable the vehicle or autonomous 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 vehicle 100, and include a wired / wireless charging circuit, a battery, etc. The sensor unit 140c may obtain 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 / backward 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.

[0330] 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 an external server. The external server may predict traffic information data based on information collected from the vehicle or autonomous driving vehicle using AI technology, etc., and provide the predicted traffic information data to the vehicle or autonomous driving vehicle.

[0331] Fig.25 is a diagram illustrating a DRX operation of a UE according to an embodiment of the present disclosure.

[0332] 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.

[0333] Reference Fig.25 , 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. On the contrary, 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. On the contrary, 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.

[0334] 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.

[0335] 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.

[0336] 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.

[0337] Industrial Applicability

[0338] The present disclosure is applicable to UE, BS or other devices in a wireless mobile communication system.

Claims

1. A method for operating a user equipment UE in a wireless communication system, the method comprising the following steps: Reporting to the network artificial intelligence processing unit (APU) information related to the number of artificial intelligence / machine learning (AI / ML) processes that can be processed simultaneously in the UE; receiving, from the network, information indicating UE operation related to at least one AI / ML model; as well as concurrently performing at least a portion of said UE operations, Wherein, based on the UE operation indicated by the network exceeding the number of the AI / ML processes reported to the network through the APU information in a specific time resource, the UE: determining one or more UE operations to be excluded from the indicated UE operations; and Information about the one or more UE operations determined to be excluded is reported to the network.

2. The method according to claim 1, wherein: The UE discards, deactivates or suspends the one or more UE operations determined to be excluded.

3. The method according to claim 1, wherein: The UE performs remaining UE operations among the UE operations instructed by the network except for the one or more UE operations determined to be excluded based on the APU information.

4. The method according to claim 1, wherein: The UE operation related to the at least one AI / ML model indicated by the network includes at least one of AI / ML-based beam management, AI / ML-based positioning, or AI / ML-based channel state information CSI calculation.

5. The method according to claim 1, wherein: The APU information is configured separately from the channel state information CSI processing unit CPU information, and the CPU information is related to the number of CSI calculations that can be processed simultaneously in the UE.

6. The method according to claim 1, wherein: The APU information is configured as part of a channel state information CSI processing unit CPU information, the CPU information being related to the number of CSI calculations that can be processed simultaneously in the UE, and Among them, one AI / ML-based CSI calculation is counted as "X" non-AI / ML-based CSI calculations.

7. The method according to claim 6, wherein: "X" is configured based on information received from the network.

8. The method according to claim 6, wherein: “X” is determined based on at least one of the capabilities of the UE and an AI / ML model related to the associated AI / ML-based CSI calculation.

9. The method according to claim 1, wherein: The APU information is configured for each component carrier CC, or the APU information is configured to cover multiple CCs.

10. The method according to claim 1, wherein: The one or more UE operations to be excluded are determined based on a priority. 11 . A computer-readable recording medium having recorded thereon a program for executing the method according to claim 1 .

12. 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: Reporting to the network Artificial Intelligence Processing Unit (APU) information related to the number of Artificial Intelligence (AI) / Machine Learning (AI) / ML processes that the device can process simultaneously; receiving information indicative of device operations related to at least one AI / ML model from the network; and performing at least a portion of said device operations simultaneously, Wherein, based on the device operation indicated by the network exceeding the number of the AI / ML processes reported to the network through the APU information in a specific time resource, the processor: determining one or more device operations to be excluded from the indicated device operations; and Information regarding the one or more device operations determined to be excluded is reported to the network.

13. The device according to claim 12, further comprising a transceiver, in, The device is a user equipment UE.

14. A method for operating a base station BS in a wireless communication system, the method comprising the following steps: Receiving, from a user equipment UE, artificial intelligence processing unit (AI) APU information related to the number of artificial intelligence / machine learning (AI / ML) processes that can be processed simultaneously in the UE; sending information indicating UE operation related to at least one AI / ML model to the UE; and Based on the indicated UE operation exceeding the number of the AI / ML processes obtained through the APU information in a specific time resource, information about one or more UE operations to be excluded from the indicated UE operation is received from the UE.

15. A base station BS for wireless communication, the BS comprising: Transceiver; as well as A processor configured to control the transceiver: Receiving, from a user equipment UE, artificial intelligence processing unit (AI) APU information related to the number of artificial intelligence / machine learning (AI / ML) processes that can be processed simultaneously in the UE; sending information indicating UE operation related to at least one AI / ML model to the UE; and Based on the indicated UE operation exceeding the number of the AI / ML processes obtained through the APU information in a specific time resource, information about one or more UE operations to be excluded from the indicated UE operation is received from the UE.

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