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

Abstract

A method performed by a terminal, according to at least one of embodiments disclosed in the present specification, may comprise: receiving configuration information on resources of a downlink reference signal; measuring the downlink reference signal via some of all antenna ports of the downlink reference signal, on the basis of the configuration information; acquiring channel information for all antenna ports via an artificial intelligence / machine learning (AI / ML) model of the terminal, on the basis of a result of the measurement; and transmitting a channel state information (CSI) report on the basis of the acquired channel information.

Description

Method and device for transmitting and receiving signals in a wireless communication system

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

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

[0003] In 6G, extreme massive MIMO systems utilizing more base station / terminal antenna ports are being considered. Accordingly, the number of reference signal (e.g., CSI-RS, DM-RS) ports is expected to increase, and an increase in signaling overhead for RS transmission is anticipated.

[0004] In the case of CSI-RS, the complexity of channel estimation increases significantly as the dimensionality increases with the increase in the number of ports.

[0005] The object of the present disclosure is to provide a method for efficiently performing a wireless signal transmission and reception process and an apparatus for doing so. As an example, a more efficient AI / ML-based channel estimation technique for a large number of antenna ports is provided.

[0006] The technical problems to be solved in this disclosure are not limited to the above technical problems, and other technical problems not mentioned will be clearly understood by those skilled in the art to which this disclosure belongs from the description below.

[0007] A method performed by a terminal according to one aspect of the present disclosure may include receiving configuration information for resources of a downlink reference signal; measuring the downlink reference signal through some of the total antenna ports of the downlink reference signal based on the configuration information; obtaining channel information for the total antenna ports through an AI / ML (artificial intelligence / machine learning) model of the terminal based on the result of the measurement; and transmitting a CSI (channel state information) report based on the obtained channel information.

[0008] The above resources may include first resources for all antenna ports and second resources for some antenna ports.

[0009] The above setting information may include linkage information that links each second resource with at least one of the first resources.

[0010] The above linkage information can be used as input data for at least one of the training or inference of the above AI / ML model.

[0011] Channel information for all antenna ports measured in the first resources can be used as a Ground Truth Label for training the AI / ML model.

[0012] The AI / ML model can be monitored based on performance indicators for each resource configured on the above-mentioned antenna ports.

[0013] The above performance indicator may be determined based on at least one of information regarding channel estimation accuracy evaluated for each resource and information regarding the time interval during which the channel estimation accuracy is valid.

[0014] Based on the above performance indicators, at least one of the resources to which the downlink reference signal is transmitted and the transmission frequency of the downlink reference signal at each resource can be determined.

[0015] The terminal can transmit at least one of information regarding antenna ports preferred by the terminal among the entire antenna ports and information regarding resources preferred by the terminal for some of the antenna ports.

[0016] The terminal may transmit a terminal capability report. The terminal capability report may include information regarding a combination of the number of maximum antenna ports and the number of minimum antenna ports supported by the terminal for the downlink reference signal.

[0017] The above downlink reference signal may be a CSI-RS (channel state information-reference signal).

[0018] According to another aspect of the present disclosure, a computer-readable non-transitory recording medium may be provided that records a program for performing the method described above.

[0019] An apparatus according to another aspect of the present disclosure comprises: a memory for storing instructions; and a processor that operates by executing said instructions, wherein the operation of the processor may include receiving configuration information for resources of a downlink reference signal; measuring the downlink reference signal through some of the total antenna ports of the downlink reference signal based on said configuration information; obtaining channel information for said total antenna ports through an AI / ML (artificial intelligence / machine learning) model of the apparatus based on the result of said measurement; and transmitting a CSI (channel state information) report based on said obtained channel information.

[0020] The above device may further include a transceiver that transmits or receives a wireless signal under the control of the processor.

[0021] The above device may be a terminal operating in a wireless communication system.

[0022] The above device may be a processing device configured to control a terminal operating in a wireless communication system.

[0023] According to another aspect of the present disclosure, a method performed by a base station comprises: transmitting configuration information regarding resources of a downlink reference signal to a terminal; transmitting the downlink reference signal to the terminal through some of the entire antenna ports of the downlink reference signal based on the configuration information; and receiving a channel state information (CSI) report from the terminal, wherein the CSI report includes channel information for all the antenna ports, and the channel information for all the antenna ports may be estimated by an artificial intelligence / machine learning (AI / ML) model of the terminal based on the downlink reference signal transmitted through some of the antenna ports.

[0024] A base station according to another aspect of the present disclosure comprises: a memory for storing instructions; and a processor that operates by executing said instructions, wherein the operation of said processor comprises: transmitting configuration information for resources of a downlink reference signal to a terminal; transmitting said downlink reference signal to the terminal through some of the total antenna ports of said downlink reference signal based on said configuration information; and receiving a CSI (channel state information) report from said terminal, said CSI report comprising channel information for said total antenna ports, said channel information for said total antenna ports may be estimated by an AI / ML (artificial intelligence / machine learning) model of said terminal based on said downlink reference signal transmitted through said partial antenna ports.

[0025] According to one embodiment, wireless signal transmission and reception can be performed efficiently in a wireless communication system. For example, channel estimation can be performed with low complexity and overhead by performing measurements on a partial port and then estimating the channel for a full port through an AI / ML model.

[0026] The effects obtainable from the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art to which the present disclosure belongs from the description below.

[0027] FIG. 1 illustrates physical channels used in a 3GPP system, which is an example of a wireless communication system, and a general signal transmission method using them.

[0028] Figure 2 illustrates the structure of a radio frame.

[0029] Figure 3 illustrates a resource grid of slots.

[0030] Figure 4 illustrates an example where a physical channel is mapped within a slot.

[0031] Figure 5 illustrates the PDSCH and ACK / NACK transmission process.

[0032] Figure 6 illustrates the PUSCH transmission process.

[0033] Figure 7 shows an example of a CSI-related procedure.

[0034] Figure 8 is a diagram illustrating the concept of AI / ML / Deep learning.

[0035] FIGS. 9 to 12 illustrate various AI / ML models of deep learning.

[0036] Figure 13 is a diagram illustrating segmented AI inference.

[0037] Figure 14 is a diagram illustrating the framework for 3GPP RAN Intelligence.

[0038] Figures 15 to 17 illustrate AI Model Training and Inference environments.

[0039] FIG. 18 illustrates the flow of a method performed at a terminal according to one embodiment.

[0040] FIG. 19 illustrates the flow of a method performed at a base station according to one embodiment.

[0041] FIGS. 20 to 23 illustrate a communication system (1) and a wireless device applicable to the present disclosure.

[0042] The following technologies can be used in various wireless access systems such as CDMA (code division multiple access), FDMA (frequency division multiple access), TDMA (time division multiple access), OFDMA (orthogonal frequency division multiple access), and SC-FDMA (single carrier frequency division multiple access). CDMA can be implemented using radio technologies such as UTRA (Universal Terrestrial Radio Access) or CDMA2000. TDMA can be implemented using radio technologies such as GSM (Global System for Mobile Communications), GPRS (General Packet Radio Service), and EDGE (Enhanced Data Rates for GSM Evolution). OFDMA can be implemented using radio technologies such as IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802-20, and E-UTRA (Evolved UTRA). UTRA is part of the UMTS (Universal Mobile Telecommunications System). 3GPP (3rd Generation Partnership Project) LTE (long term evolution) is part of E-UMTS (Evolved UMTS) using E-UTRA, and LTE-A (Advanced) is an evolved version of 3GPP LTE. 3GPP NR (New Radio or New Radio Access Technology) is an evolved version of 3GPP LTE / LTE-A.

[0043] As more communication devices require larger communication capacities, the need for enhanced mobile broadband communication compared to existing Radio Access Technology (RAT) is emerging. Additionally, massive Machine Type Communications (MTC), which connects multiple devices and objects to provide various services anytime and anywhere, is one of the major issues to be considered in next-generation communication. Furthermore, communication system designs that consider services / terminals sensitive to reliability and latency are being discussed. As such, the introduction of next-generation RAT considering enhanced Mobile Broadband Communication (eMBB), massive MTC, and Ultra-Reliable and Low Latency Communication (URLLC) is being discussed, and for convenience, this technology is referred to as NR (New Radio or New RAT) in this invention.

[0044] For clarity of explanation, the description is based primarily on 3GPP NR, but the technical concept of the present invention is not limited thereto.

[0045] In this specification, the expression "setting" may be replaced with the expression "configure / configuration," and the two may be used interchangeably. Additionally, conditional expressions (e.g., "if," "in a case," or "when") may be replaced with expressions such as "based on that" or "in a state / status." Furthermore, the operation of a terminal / base station or SW / HW configuration based on the fulfillment of the corresponding conditions may be inferred or understood. Moreover, regarding signal transmission and reception between wireless communication devices (e.g., base station, terminal), if the process of the receiving (or transmitting) side can be inferred or understood from the process of the transmitting (or receiving) side, such description may be omitted. For example, signal determination / generation / encoding / transmission by the transmitting side may be understood as signal monitoring reception / decoding / determination by the receiving side. Furthermore, the expression that the terminal performs (or does not perform) a specific operation can also be interpreted as the base station operating under the expectation / assumption (or expectation / assumption that the terminal does not perform) the specific operation. The expression that the base station performs (or does not perform) a specific operation can also be interpreted as the terminal operating under the expectation / assumption (or expectation / assumption that the base station does not perform) the specific operation. Additionally, the classification and indexing of each section, embodiment, example, option, method, or plan in the following description are for the convenience of explanation and should not be interpreted as implying that each necessarily constitutes an independent invention or that each must necessarily be implemented individually. Furthermore, in describing each section, embodiment, example, option, method, or plan, if there are no explicitly conflicting or opposing technologies, it can be inferred / interpreted that at least some of them may be combined and implemented together, or that at least some may be omitted.

[0046] In a wireless communication system, a terminal receives information from a base station via a downlink (DL) and transmits information to the base station via an uplink (UL). The information transmitted and received by the base station and the terminal includes data and various control information, and various physical channels exist depending on the type and purpose of the information they transmit and receive.

[0047] FIG. 1 is a diagram illustrating physical channels used in 3GPP NR systems and a general signal transmission method using them.

[0048] When a terminal is turned on again after being turned off, or when it newly enters a cell, it performs an initial cell search operation, such as synchronizing with a base station in step S101. To do this, the terminal receives a Synchronization Signal Block (SSB) from the base station. The SSB includes a Primary Synchronization Signal (PSS), a Secondary Synchronization Signal (SSS), and a Physical Broadcast Channel (PBCH). Based on the PSS / SSS, the terminal synchronizes with the base station and obtains information such as the cell identity. Additionally, the terminal can obtain in-cell broadcast information based on the PBCH. Meanwhile, during the initial cell search phase, the terminal can receive a Downlink Reference Signal (DL RS) to check the downlink channel status.

[0049] After completing the initial cell search, the terminal can obtain more specific system information by receiving the Physical Downlink Control Channel (PDCCH) and the Physical Downlink Shared Channel (PDSCH) based on the Physical Downlink Control Channel information in step S102.

[0050] Subsequently, the terminal may perform a Random Access Procedure, such as steps S103 through S106, to complete the connection to the base station. To this end, the terminal may transmit a preamble through a Physical Random Access Channel (PRACH) (S103) and receive a response message for the preamble through a Physical Downlink Control Channel and a corresponding Physical Downlink Shared Channel (S104). In the case of contention-based random access, a Contention Resolution Procedure may be performed, such as transmitting an additional Physical Random Access Channel (S105) and receiving a Physical Downlink Control Channel and a corresponding Physical Downlink Shared Channel (S106).

[0051] A terminal that has performed the procedure described above may subsequently perform the reception of a physical downlink control channel / physical downlink shared channel (S107) and the transmission of a physical uplink shared channel (PUSCH) / physical uplink control channel (PUCCH) (S108) as a general uplink / downlink signal transmission procedure. The control information transmitted by the terminal to the base station is collectively referred to as Uplink Control Information (UCI). UCI includes HARQ ACK / NACK (Hybrid Automatic Repeat and reQuest Acknowledgement / Negative-ACK), SR (Scheduling Request), CSI (Channel State Information), etc. CSI includes CQI (Channel Quality Indicator), PMI (Precoding Matrix Indicator), RI (Rank Indication), etc. UCI is generally transmitted via PUCCH, but if control information and traffic data need to be transmitted simultaneously, it may be transmitted via PUSCH. In addition, UCI can be transmitted non-periodically via PUSCH in response to network requests / instructions.

[0052] Figure 2 illustrates the structure of a radio frame. In NR, uplink and downlink transmissions consist of frames. Each radio frame has a length of 10 ms and is divided into two 5 ms half-frames (HF). Each half-frame is divided into five 1 ms subframes (SF). Subframes are divided into one or more slots, and the number of slots within a subframe depends on the subcarrier spacing (SCS). Each slot contains 12 or 14 Orthogonal Frequency Division Multiplexing (OFDM) symbols depending on the cyclic prefix (CP). When a normal CP is used, each slot contains 14 OFDM symbols. When an extended CP is used, each slot contains 12 OFDM symbols.

[0053] Table 1 illustrates how the number of symbols per slot, the number of slots per frame, and the number of slots per subframe vary depending on the SCS when a standard CP is used.

[0054] SCS (15*2^u)N slot symb N frame,u slot N subframe,u slot 15KHz (u=0)1410130KHz (u=1)1420260KHz (u=2)14404120KHz (u=3)14808240KHz (u=4)1416016

[0055] * N slot symb : Number of symbols in the slot

[0056] * N frame,u slot : Number of slots in the frame

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

[0058] Table 2 illustrates how the number of symbols per slot, the number of slots per frame, and the number of slots per subframe vary depending on the SCS when an extended CP is used.

[0059] SCS (15*2^u)N slot symb N frame,u slot N subframe,u slot 60KHz (u=2)12404

[0060] The frame structure is merely an example, and the number of subframes, slots, and symbols within the frame can be varied.

[0061] In an NR system, the OFDM numerology (e.g., SCS) can be configured differently among multiple cells merged into a single terminal. Accordingly, the (absolute time) intervals of a time resource (e.g., SF, slot, or TTI) (collectively referred to as TU (Time Unit) for convenience) composed of the same number of symbols can be configured differently among the merged cells. Here, the symbols may include OFDM symbols (or CP-OFDM symbols) and SC-FDMA symbols (or Discrete Fourier Transform-spread-OFDM, DFT-s-OFDM symbols).

[0062] FIG. 3 illustrates a resource grid of slots. A slot contains multiple symbols in the time domain. For example, in the case of a standard CP, one slot contains 14 symbols, whereas in the case of an extended CP, one slot contains 12 symbols. A carrier contains multiple subcarriers in the frequency domain. A Resource Block (RB) is defined as multiple (e.g., 12) consecutive subcarriers in the frequency domain. A Bandwidth Part (BWP) is defined as multiple consecutive Physical Blocks (PRB) in the frequency domain and can correspond to a single numerology (e.g., SCS, CP length, etc.). A carrier can contain up to N (e.g., 5) BWPs. Data communication is performed through the active BWPs, and only one BWP can be active for a single terminal. Each element in the resource grid is referred to as a Resource Element (RE), and a single complex symbol can be mapped to it.

[0063] FIG. 4 illustrates an example where physical channels are mapped within a slot. PDCCH can be transmitted in the DL control area, and PDSCH can be transmitted in the DL data area. PUCCH can be transmitted in the UL control area, and PUSCH can be transmitted in the UL data area. GP provides a time gap during the process of the base station and the terminal switching from transmit mode to receive mode or from receive mode to transmit mode. Some symbols at the time of transition from DL to UL within a subframe can be set as GP.

[0064] Below, each physical channel is explained in more detail.

[0065] The PDCCH carries Downlink Control Information (DCI). For example, the PCCCH (i.e., DCI) carries the transmission format and resource allocation for the DL-SCH (downlink shared channel), resource allocation information for the UL-SCH (uplink shared channel), paging information for the PCH (paging channel), system information on the DL-SCH, resource allocation information for higher-layer control messages such as random connection acknowledgments transmitted over the PDSCH, transmission power control commands, and the activation / deactivation of the CS (Configured Scheduling). The DCI includes a Cyclic Redundancy Check (CRC), which is masked or scrambled with various identifiers (e.g., Radio Network Temporary Identifier, RNTI) depending on the owner or use of the PDCCH. For example, if the PDCCH is for a specific terminal, the CRC is masked with the terminal identifier (e.g., Cell-RNTI, C-RNTI). If PDCCH is for paging, the CRC is masked by P-RNTI (Paging-RNTI). If PDCCH is for system information (e.g., System Information Block, SIB), the CRC is masked by SI-RNTI (System Information RNTI). If PDCCH is for a random access response, the CRC is masked by RA-RNTI (Random Access-RNTI).

[0066] A PDCCH consists of 1, 2, 4, 8, or 16 Control Channel Elements (CCEs) depending on the Aggregation Level (AL). A CCE is a logical allocation unit used to provide a PDCCH of a specified code rate according to the radio channel conditions. A CCE consists of 6 Resource Element Groups (REGs). A REG is defined by one OFDM symbol and one (P)RB. A PDCCH is transmitted via a Control Resource Set (CORESET). A CORESET is defined as a set of REGs with a given pneumonology (e.g., SCS, CP length, etc.). Multiple CORESETs for a single terminal may overlap in the time / frequency domain. A CORESET can be configured via system information (e.g., Master Information Block, MIB) or terminal-specific (UE-specific) upper-layer signaling (e.g., Radio Resource Control, RRC layer). Specifically, the number of RBs and the number of OFDM symbols (up to 3) constituting the CORESET can be set by the upper layer signaling.

[0067] To receive / detect a PDCCH, the terminal monitors PDCCH candidates. A PDCCH candidate represents the CCE(s) that the terminal must monitor for PDCCH detection. Each PDCCH candidate is defined by 1, 2, 4, 8, or 16 CCEs according to AL. Monitoring involves (blind) decoding the PDCCH candidates. The set of PDCCH candidates monitored by the terminal is defined as the PDCCH Search Space (SS). The Search Space includes a Common Search Space (CSS) or a Terminal-specific Search Space (UE-specific search space, USS). The terminal can acquire a DCI by monitoring PDCCH candidates in one or more Search Spaces configured by the MIB or upper-layer signaling. Each CORESET is associated with one or more Search Spaces, and each Search Space is associated with one CORESET. A Search Space can be defined based on the following parameters.

[0068] - controlResourceSetId: Indicates the CORESET associated with the search space.

[0069] - monitoringSlotPeriodicityAndOffset: Indicates the PDCCH monitoring period (in slots) and the PDCCH monitoring interval offset (in slots).

[0070] - monitoringSymbolsWithinSlot: Represents PDCCH monitoring symbols within the slot (e.g., represents the first symbol(s) of the CORESET)

[0071] - nrofCandidates: AL={1, 2, 4, 8, 16} represents the number of star PDCCH candidates (one of the values ​​0, 1, 2, 3, 4, 5, 6, or 8)

[0072] An opportunity (e.g., time / frequency resources) to monitor PDCCH candidates is defined as a PDCCH (monitoring) opportunity. One or more PDCCH (monitoring) opportunities may be configured within a slot.

[0073] Table 3 illustrates the characteristics of each search space type.

[0074] TypeSearch SpaceRNTIUse CaseType0-PDCCHCommonSI-RNTI on a primary cellSIB DecodingType0A-PDCCHCommonSI-RNTI on a primary cellSIB DecodingType1-PDCCHCommonRA-RNTI or TC-RNTI on a primary cellMsg2, Msg4 decoding in RACHType2-PDCCHCommonP-RNTI on a primary cellPaging DecodingType3-PDCCHCommonINT-RNTI, SFI-RNTI, TPC-PUSCH-RNTI, TPC-PUCCH-RNTI, TPC-SRS-RNTI, C-RNTI, MCS-C-RNTI, or CS-RNTI(s)UE SpecificUE SpecificC-RNTI, or MCS-C-RNTI, or CS-RNTI(s)User specific PDSCH decoding

[0075] Table 4 shows examples of DCI formats transmitted via PDCCH.

[0076] DCI formatUsage0_0Scheduling of PUSCH in one cell0_1Scheduling of PUSCH in one cell1_0Scheduling of PDSCH in one cell1_1Scheduling of PDSCH in one cell2_0Notifying a group of UEs of the slot format2_1Notifying a group of UEs of the PRB(s) and OFDM symbol(s) where UE may assume no transmission is intended for the UE2_2Transmission of TPC commands for PUCCH and PUSCH2_3Transmission of a group of TPC commands for SRS transmissions by one or more UEs

[0077] DCI format 0_0 is 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 CBG (Code Block Group)-based (or CBG-level) PUSCH. DCI format 1_0 is 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 (DL grant DCI). DCI format 0_0 / 0_1 is referred to as UL grant DCI or UL scheduling information, and DCI format 1_0 / 1_1 can be referred to as DL grant DCI or DL ​​scheduling information. DCI format 2_0 is used to transmit dynamic slot format information (e.g., dynamic SFI) to terminals, and DCI format 2_1 is used to transmit downlink pre-Emption information to terminals. DCI format 2_0 and / or DCI format 2_1 may be transmitted to terminals within a group through a group common PDCCH, which is a PDCCH transmitted to terminals defined as a group.

[0078] DCI format 0_0 and DCI format 1_0 are referred to as fallback DCI formats, and DCI format 0_1 ​​and DCI format 1_1 may be referred to as non-fallback DCI formats. The DCI size / field configuration of the fallback DCI format remains the same regardless of terminal settings. On the other hand, the DCI size / field configuration of the non-fallback DCI format varies depending on the terminal settings.

[0079] PDSCH carries downlink data (e.g., DL-SCH transport block, DL-SCH TB), and modulation methods such as QPSK (Quadrature Phase Shift Keying), 16 QAM (Quadrature Amplitude Modulation), 64 QAM, and 256 QAM are applied. A codeword is generated by encoding the TB. PDSCH can carry up to two codewords. Scrambling and modulation mapping are performed for each codeword, and the modulation symbols generated from each codeword can be mapped to one or more layers. Each layer is mapped to a resource along with the DMRS (Demodulation Reference Signal) to generate an OFDM symbol signal, which is then transmitted through the corresponding antenna port.

[0080] PUCCH carries UCI (Uplink Control Information). UCI includes the following:

[0081] - SR(Scheduling Request): Information used to request UL-SCH resources.

[0082] - HARQ (Hybrid Automatic Repeat reQuest)-ACK (Acknowledgement): This is an acknowledgment for a downlink data packet (e.g., codeword) on the PDSCH. It indicates whether the downlink data packet was successfully received. A 1-bit HARQ-ACK is transmitted in response to a single codeword, and a 2-bit HARQ-ACK can be transmitted in response to two codewords. The HARQ-ACK response includes a positive ACK (simply ACK), a negative ACK (NACK), a DTX, or a NACK / DTX. Here, HARQ-ACK is used interchangeably with HARQ ACK / NACK and ACK / NACK.

[0083] - CSI (Channel State Information): Feedback information for the downlink channel. MIMO (Multiple Input Multiple Output) related feedback information includes RI (Rank Indicator) and PMI (Precoding Matrix Indicator).

[0084] Table 5 provides examples of PUCCH formats. Depending on the PUCCH transmission length, they can be classified into Short PUCCH (formats 0, 2) and Long PUCCH (formats 1, 3, 4).

[0085] PUCCH formatLength in OFDM symbols N PUCCH symb Number of bitsUsageEtc01 - 2≤2HARQ, SRSequence selection14 - 14≤2HARQ, [SR]Sequence modulation21 - 2>2HARQ, CSI, [SR]CP-OFDM34 - 14>2HARQ, CSI, [SR]DFT-s-OFDM(no UE multiplexing)44 - 14>2HARQ, CSI, [SR]DFT-s-OFDM(Pre DFT OCC)

[0086] PUCCH format 0 carries a UCI of up to 2 bits in size and is transmitted by mapping based on the sequence. Specifically, the terminal transmits a specific UCI to the base station by transmitting one of multiple sequences through a PUCCH that is PUCCH format 0. The terminal transmits a PUCCH that is PUCCH format 0 within the PUCCH resource for the corresponding SR setting only when transmitting a positive SR.

[0087] PUCCH format 1 carries a UCI of up to 2 bits, and modulation symbols are spread by an orthogonal cover code (OCC) in the time domain (configured differently depending on frequency hopping). DMRS is transmitted at symbols where modulation symbols are not transmitted (i.e., transmitted via Time Division Multiplexing (TDM)).

[0088] PUCCH Format 2 carries a UCI with a bit size greater than 2 bits, and modulated symbols are transmitted via DMRS and Frequency Division Multiplexing (FDM). DMRS is located at symbol indices #1, #4, #7, and #10 within a given resource block at a density of 1 / 3. A Pseudo Noise (PN) sequence is used for the DMRS sequence. Frequency hopping can be enabled for 2-symbol PUCCH Format 2.

[0089] PUCCH format 3 does not perform terminal multiplexing within the same physical resource blocks and carries a UCI with a bit size greater than 2 bits. In other words, PUCCH resources in PUCCH format 3 do not include orthogonal cover codes. Modulation symbols are transmitted via DMRS and TDM (Time Division Multiplexing).

[0090] PUCCH format 4 supports multiplexing of up to 4 terminals within the same physical resource blocks and carries a UCI with a bit size greater than 2 bits. In other words, the PUCCH resources of PUCCH format 3 contain an orthogonal cover code. Modulation symbols are transmitted via DMRS and TDM (Time Division Multiplexing).

[0091] At least one of the one or more cells configured in the terminal may be configured for PUCCH transmission. At least one 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 terminal, and each PUCCH cell group includes one or more cells. A PUCCH cell group may be briefly referred to as a PUCCH group. PUCCH transmission may be configured in SCells as well as Primary Cells; the Primary Cell belongs to the Primary PUCCH group, and the PUCCH-SCell configured for PUCCH transmission belongs to the secondary PUCCH group. For the Cells belonging to the Primary PUCCH group, the PUCCH on the Primary Cell may be used, and for the Cells belonging to the Secondary PUCCH group, the PUCCH on the PUCCH-SCell may be used.

[0092] PUSCH carries uplink data (e.g., UL-SCH transport block, UL-SCH TB) and / or uplink control information (UCI) and is transmitted based on a CP-OFDM (Cyclic Prefix - Orthogonal Frequency Division Multiplexing) waveform or a DFT-s-OFDM (Discrete Fourier Transform - spread - Orthogonal Frequency Division Multiplexing) waveform. When PUSCH is transmitted based on a DFT-s-OFDM waveform, the terminal transmits PUSCH by applying transform precoding. For example, if transform precoding is disabled, the terminal transmits PUSCH based on a CP-OFDM waveform, and if transform precoding is enabled, the terminal can transmit PUSCH based on a CP-OFDM waveform or a DFT-s-OFDM waveform. PUSCH transmissions can be dynamically scheduled by UL grants within DCI or semi-statically scheduled based on upper layer (e.g., RRC) signaling (and / or Layer 1 (L1) signaling (e.g., PDCCH)) configured grants. PUSCH transmissions can be performed in a codebook-based or non-codebook-based manner.

[0093] FIG. 5 illustrates the ACK / NACK transmission process. Referring to FIG. 5, the terminal can detect PDCCH in slot #n. Here, PDCCH includes downlink scheduling information (e.g., DCI format 1_0, 1_1), and PDCCH represents the DL assignment-to-PDSCH offset (K0) and the PDSCH-HARQ-ACK reporting offset (K1). For example, DCI format 1_0, 1_1 may include the following information.

[0094] - Frequency domain resource assignment: Represents the set of RBs assigned to PDSCH

[0095] - Time domain resource assignment: Indicates K0 (e.g., slot offset), the starting position of the PDSCH within slot #n+K0 (e.g., OFDM symbol index), and the length of the PDSCH (e.g., number of OFDM symbols).

[0096] - PDSCH-to-HARQ_feedback timing indicator: Indicates K1

[0097] - HARQ process number (4 bits): Represents the HARQ process ID (Identity) for data (e.g., PDSCH, TB)

[0098] - PUCCH resource indicator (PRI): Indicates the PUCCH resource to be used for UCI transmission among multiple PUCCH resources within the PUCCH resource set.

[0099] Subsequently, the terminal receives a PDSCH from slot #(n+K0) according to the scheduling information of slot #n, and when the reception of the PDSCH ends in slot #n1 (where, n+K0 ≤ n1), it can transmit a UCI via a PUCCH in slot #(n1+K1). Here, the UCI may include a HARQ-ACK response to the PDSCH. In FIG. 5, for convenience, it was assumed that the SCS for the PDSCH and the SCS for the PUCCH are identical and that slot #n1 = slot #n+K0, but the present invention is not limited thereto. If the SCSs are different, K1 may be indicated / interpreted based on the SCS of the PUCCH.

[0100] If the PDSCH is configured to transmit up to 1 TB, the HARQ-ACK response may consist of 1 bit. If the PDSCH is configured to transmit up to 2 TB, the HARQ-ACK response may consist of 2 bits if spatial bundling is not configured, and 1 bit if spatial bundling is configured. If the time for transmitting HARQ-ACKs for multiple PDSCHs is specified as slot #(n+K1), the UCI transmitted at slot #(n+K1) includes HARQ-ACK responses for multiple PDSCHs.

[0101] Whether a terminal must perform spatial bundling for a HARQ-ACK response can be configured per cell group (e.g., RRC / upper layer signaling). For example, spatial bundling can be configured individually for each HARQ-ACK response transmitted via PUCCH and / or HARQ-ACK response transmitted via PUSCH.

[0102] Spatial bundling may be supported when the maximum number of TBs (or codewords) that can be received (or scheduled via 1 DCI) at once in the corresponding serving cell is 2 (or more than 2) (e.g., when the upper layer parameter maxNrofCodeWordsScheduledByDCI corresponds to 2-TB). Meanwhile, more than 4 layers may be used for 2-TB transmission, and up to 4 layers may be used for 1-TB transmission. Consequently, if spatial bundling is configured in the corresponding cell group, spatial bundling may be performed on serving cells within the cell group where more than 4 layers are scheduleable. On the corresponding serving cell, a terminal that intends to transmit a HARQ-ACK response via spatial bundling may generate a HARQ-ACK response by performing a (bit-wise) logical AND operation on the A / N bits of multiple TBs.

[0103] For example, assuming that a terminal receives a DCI scheduling 2-TB and receives 2-TB via PDSCH based on the said DCI, the terminal performing spatial bundling can generate a single A / N bit by performing a logical AND operation on the first A / N bit for the first TB and the second A / N bit for the second TB. Consequently, if both the first TB and the second TB are ACK, the terminal reports the ACK bit value to the base station, and if either TB is NACK, the terminal reports the NACK bit value to the base station.

[0104] For example, if only 1-TB is actually scheduled on a serving cell configured to receive 2-TB, the terminal can generate a single A / N bit by performing a logical AND operation between the A / N bit for the 1-TB and the bit value 1. As a result, the terminal reports the A / N bit for the 1-TB to the base station as is.

[0105] Multiple parallel DL HARQ processes exist in the base station / terminal for DL ​​transmission. These multiple parallel HARQ processes enable DL transmission to be performed continuously while waiting for HARQ feedback regarding the successful or unsuccessful reception of the previous DL transmission. Each HARQ process is associated with a HARQ buffer of the MAC (Medium Access Control) layer. Each DL HARQ process manages state variables regarding the number of transmissions of MAC PDUs (Physical Data Blocks) in the buffer, HARQ feedback for MAC PDUs in the buffer, and the current redundancy version. Each HARQ process is distinguished by its HARQ process ID.

[0106] FIG. 6 illustrates a PUSCH transmission process. Referring to FIG. 6, the terminal can detect PDCCH in slot #n. Here, PDCCH includes uplink scheduling information (e.g., DCI format 0_0, 0_1). DCI format 0_0, 0_1 may include the following information.

[0107] - Frequency domain resource assignment: Indicates the set of RBs assigned to PUSCH

[0108] - Time domain resource assignment: Indicates slot offset K2, the starting position (e.g., symbol index) and length (e.g., number of OFDM symbols) of PUSCH within the slot. The starting symbol and length may be indicated via SLIV (Start and Length Indicator Value) or individually.

[0109] Subsequently, the terminal can transmit PUSCH at slot #(n+K2) according to the scheduling information of slot #n. Here, PUSCH includes UL-SCH TB.

[0110] CSI-related operations

[0111] Figure 7 shows an example of a CSI-related procedure.

[0112] The terminal receives configuration information related to CSI from the base station via RRC signaling (710). The configuration information related to CSI may include at least one of CSI-IM (interference management) resource information, CSI measurement configuration information, CSI resource configuration information, CSI-RS resource information, or CSI report configuration information.

[0113] - A CSI-IM resource may be configured for interference measurement (IM) of a terminal. In the time domain, the CSI-IM resource set may be configured periodic, semi-permanent, or non-periodic. The CSI-IM resource may be configured as Zero Power (ZP)-CSI-RS for the terminal. ZP-CSI-RS may be configured separately from Non-Zero Power (NZP)-CSI-RS.

[0114] - UE can assume that the CSI-RS resource(s) for channel measurement set for one CSI reporting and the CSI-IM / NZP CSI-RS resource(s) for interference measurement (when NZP CSI-RS resource(s) are used for interference measurement) have a QCL relationship with respect to 'QCL-TypeD' on a resource-by-resource basis.

[0115] - The CSI resource configuration may include at least one of a CSI-IM resource for interference measurement, an NZP CSI-RS resource for interference measurement, and an NZP CSI-RS resource for channel measurement. The CMR (channel measurement resource) may be an NZP CSI-RS for CSI acquisition, and the IMR (Interference measurement resource) may be an NZP CSI-RS for CSI-IM and IM.

[0116] - CSI-RS may be configured for one or more terminals. Different CSI-RS configurations may be provided for each terminal, or the same CSI-RS configuration may be provided for multiple terminals. CSI-RS may support up to 32 antenna ports. CSI-RS corresponding to N (N is 1 or more) antenna ports may be mapped to N RE positions within a time-frequency unit corresponding to one slot and one RB. If N is 2 or more, N-port CSI-RS may be multiplexed using CDM, FDM, and / or TDM methods. CSI-RS may be mapped to the remaining REs, excluding the REs to which CORESET, DMRS, and SSB are mapped. In the frequency domain, CSI-RS may be configured for the entire bandwidth, a portion of the bandwidth (BWP), or a portion of the bandwidth. CSI-RS may be transmitted at each RB within the configured bandwidth (i.e., density=1), or at every second RB (e.g., even or odd RB) (i.e., density=1 / 2). When CSI-RS is used as a Tracking Reference Signal (TRS), a single-port CSI-RS may be mapped to three subcarriers in each resource block (i.e., density=3). In the time domain, one or more CSI-RS resource sets may be configured for the terminal. Each CSI-RS resource set may include one or more CSI-RS configurations. Each CSI-RS resource set may be configured periodicly, semipersistently, or non-periodically.

[0117] - CSI report configuration may include settings for feedback type, measurement resources, report type, etc. NZP-CSI-RS resource sets may be used for the CSI report configuration of the terminal. NZP-CSI-RS resource sets may be associated with CSI-RS or SSB. Additionally, multiple periodic NZP-CSI-RS resource sets may be configured as TRS resource sets. (i) Feedback types may include Channel Quality Indicator (CQI), Precoding Matrix Indicator (PMI), CRI (CSI-RS Resource Indicator), SSBRI (SSB Resource block Indicator), LI (Layer Indicator), Rank Indicator (RI), Layer 1-Reference Signal Received Strength (RSRP), etc. (ii) Measurement resources may include settings for downlink signals and / or downlink resources for which the terminal performs measurements to determine feedback information. Measurement resources may be set as ZP and / or NZP CSI-RS resource sets associated with CSI reporting settings. NZP CSI-RS resource sets may include CSI-RS sets or SSB sets. For example, L1-RSRP may be measured against CSI-RS sets or against SSB sets. (iii) Report type may include settings for the timing and uplink channel, etc. for which the terminal performs reporting. Report timing may be set to periodic, semi-persistent, or non-periodic. Periodic CSI reporting may be transmitted over PUCCH. Semi-persistent CSI reporting may be transmitted over PUCCH or PUSCH based on MAC CE indicating activation / deactivation. Non-periodic CSI reporting may be indicated by DCI signaling.For example, the CSI request field of an uplink grant can specify one of various report trigger sizes. Non-periodic CSI reports can be transmitted over PUSCH.

[0118] The terminal measures the CSI based on configuration information related to the CSI. The CSI measurement may include a procedure for receiving the CSI-RS (720) and acquiring the CSI (730) by computationing the received CSI-RS.

[0119] The terminal can transmit CSI reports to the base station (740). For CSI reporting, the time and frequency resources available to the UE are controlled by the base station. The channel state information (CSI) may include at least one of a channel quality indicator (CQI), a precoding matrix indicator (PMI), a CSI-RS resource indicator (CRI), an SS / PBCH block resource indicator (SSBRI), a layer indicator (LI), a rank indicator (RI), an L1-RSRP, and / or an L-SINR.

[0120] The time domain behavior of CSI reporting supports periodic, semi-persistent, and aperiodic. i) Periodic CSI reporting is performed on short PUCCH or long PUCCH. The periodicity and slot offset of periodic CSI reporting can be set to RRC; refer to CSI-ReportConfig IE. ii) Semi-periodic (SP) CSI reporting is performed on short PUCCH, long PUCCH, or PUSCH. In the case of SP CSI on short / long PUCCH, the periodicity and slot offset are set to RRC, and CSI reporting is activated / deactivated by separate MAC CE / DCI. In the case of SP CSI on PUSCH, the periodicity of SP CSI reporting is set to RRC, but the slot offset is not set to RRC, and SP CSI reporting is activated / deactivated by DCI (format 0_1). For SP CSI reporting over PUSCH, a separate RNTI (SP-CSI C-RNTI) is used. The timing of the initial CSI report follows the PUSCH time domain allocation value specified in the DCI, while subsequent CSI reporting timing follows the period set by the RRC. DCI format 0_1 ​​includes a CSI request field and can activate / deactivate specific configured SP-CSI trigger states. SP CSI reporting has the same or similar activation / deactivation mechanisms as those used for data transmission over SPS PUSCH.iii) aperiodic CSI reporting is performed on PUSCH and triggered by DCI. In this case, information related to the trigger of aperiodic CSI reporting can be transmitted / instructed / set via MAC-CE. For an AP CSI with AP CSI-RS, the AP CSI-RS timing is set by RRC, and the timing for AP CSI reporting is dynamically controlled by DCI.

[0121] CSI codebooks (e.g., PMI codebooks) defined in NR standards can be broadly classified into Type I codebooks and Type II codebooks. Type I codebooks are primarily targeted at Single User (SU)-MIMO, which supports both high and low orders. Type II codebooks can primarily support MI-MIMO, which supports up to two layers. While Type II codebooks can provide more accurate CSI compared to Type I, this may result in increased signaling overhead. Meanwhile, the Enhanced Type II codebook was introduced to address the CSI overhead drawbacks associated with the existing Type II codebook; it reduces the codebook payload by considering the correlation along the frequency axis.

[0122] CSI reporting via PUSCH can be configured into Part 1 and Part 2. Part 1 has a fixed payload size and is used to identify the number of information bits in Part 2. Part 1 is transmitted in its entirety prior to Part 2.

[0123] - For Type I CSI feedback, Part 1 includes RI (if reported), CRI (if reported), and the CQI of the first code word. Part 2 includes PMI, and when RI > 4, Part 2 includes CQI.

[0124] - For Type II CSI feedback, Part 1 includes indications for the number of non-zero WB amplitude coefficients per layer of RI (if reported), CQI, and Type II CSI. Part 2 includes the PMI of Type II CSI.

[0125] - For Enhanced Type II CSI feedback, Part 1 includes indications of the total number of non-zero WB amplitude coefficients for the RI (if reported), CQI, and total layers of Enhanced Type II CSI. Part 2 includes the PMI of Enhanced Type II CSI.

[0126] In PUSCH, CSI reporting includes two parts, and if the CSI payload to be reported is less than the payload size provided by the PUSCH resources allocated for CSI reporting, the terminal may omit part of Part 2 CSI.

[0127] Meanwhile, semi-persistent CSI reporting performed in PUCCH format 3 or 4 supports Type II CSI feedback, but only Part 1 of Type II CSI feedback.

[0128] QCL (quasi-co location)

[0129] Two antenna ports are quasi-co-located if the channel properties of one antenna port can be inferred from the channel of another antenna port. Channel properties may include one or more of Delay spread, Doppler spread, Frequency / Doppler shift, Average received power, Received Timing / average delay, and Spatial RX parameters.

[0130] A list of multiple TCI-State configurations can be set in the terminal through the upper layer parameter PDSCH-Config. Each TCI-State is associated with a QCL configuration parameter 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.

[0131] - 'QCL-TypeA': {Doppler shift, Doppler spread, average delay, delay spread}

[0132] - 'QCL-TypeB': {Doppler shift, Doppler spread}

[0133] - 'QCL-TypeC': {Doppler shift, average delay}

[0134] - 'QCL-TypeD': {Spatial Rx parameter}

[0135] Beam Management (BM)

[0136] The BM process is a process for acquiring and maintaining a set of BS (or transmission and reception point (TRP)) and / or UE beams that can be used for downlink (DL) and uplink (UL) transmission / reception, and may include the following processes and terms.

[0137] - Beam measurement: An operation in which a BS or UE measures the characteristics of a received beamforming signal.

[0138] - Beam determination: The operation in which a BS or UE selects its transmit beam (Tx beam) / receive beam (Rx beam).

[0139] - Beam sweeping: An operation that covers a spatial domain using a transmitted and / or received beam for a set time interval in a predetermined manner.

[0140] - Beam report: An operation in which the UE reports information about the beamformed signal based on beam measurements.

[0141] The BM process can be divided into (1) a DL BM process using SSB or CSI-RS and (2) a UL BM process using SRS (sounding reference signal). Additionally, each BM process may include Tx beam sweeping to determine the Tx beam and Rx beam sweeping to determine the Rx beam.

[0142] At this time, the DL BM process may include (1) transmission of beam-formed DL RSs (e.g., CSI-RS or SSB) by the BS and (2) beam reporting by the UE.

[0143] Here, the beam report may include preferred DL RS ID(s) and the corresponding reference signal received power (RSRP). The DL RS ID may be an SSBRI (SSB Resource Indicator) or a CRI (CSI-RS Resource Indicator).

[0144] Positioning

[0145] Positioning may refer to determining the geographical location and / or velocity of a UE by measuring radio signals. Location information may be requested by a client associated with the UE (e.g., an application) and reported to said client. Additionally, said location information may be included within a core network or requested by a client connected to said core network. The location information may be reported in a standard format, such as cell-based or geographical coordinates, and may also report the estimated error value for the UE's location and velocity and / or the positioning method used for positioning.

[0146] LPP can be used as a point-to-point connection between a location server (E-SMLC and / or SLP and / or LMF) and a target device to position a target device (UE and / or SET) using position-related measurements obtained from one or more reference sources. Through LPP, the target device and the location server can exchange measurement and / or location information based on signals A and / or signals B.

[0147] NRPPa can be used for information exchange between reference sources (ACCESS NODE and / or BS and / or TP and / or NG-RAN nodes) and location servers.

[0148] The functions provided by the NRPPa protocol may include the following:

[0149] - E-CID Location Information Transfer. Through this function, location information can be exchanged between the reference source and the LMF for E-CID positioning purposes.

[0150] - OTDOA Information Transfer. Through this function, information can be exchanged between the reference source and the LMF for the purpose of OTDOA positioning.

[0151] - Reporting of General Error Situations. Through this function, general error situations where function-specific error messages are not defined can be reported.

[0152] Positioning methods supported by NG-RAN may include GNSS (Global Navigation Satellite System), OTDOA, E-CID (enhanced cell ID), barometric sensor positioning, WLAN positioning, Bluetooth positioning and TBS (terrestrial beacon system), UTDOA (Uplink Time Difference of Arrival), etc. Among the above positioning methods, the position of a UE may be measured using any one of the positioning methods, but the position of a UE may also be measured using two or more positioning methods.

[0153] OTDOA (Observed Time Difference Of Arrival)

[0154] The OTDOA positioning method utilizes the measured timing of downlink signals received by a UE from multiple TPs, including eNBs, ng-eNBs, and PRS-dedicated TPs. The UE measures the timing of the received downlink signals using location auxiliary data received from a location server. Based on these measurement results and the geographical coordinates of neighboring TPs, the UE's location can be determined.

[0155] A UE connected to the gNB may request a measurement gap from the TP for OTDOA measurement. If the UE does not recognize an SFN for at least one TP within the OTDOA auxiliary data, the UE may use an autonomous gap to obtain the SFN of the OTDOA reference cell before requesting a measurement gap to perform an RSTD (Reference Signal Time Difference) measurement.

[0156] Here, RSTD can be defined based on the smallest relative time difference between the boundaries of two subframes received from the reference cell and the measurement cell, respectively. That is, it can be calculated based on the relative time difference between the start time of the reference cell's subframe closest to the start time of the subframe received from the measurement cell. Meanwhile, the reference cell can be selected by the UE.

[0157] For accurate OTDOA measurement, it is necessary to measure the time of arrival (TOA) of signals received from three or more geographically distributed TPs or base stations. For example, the TOA for each of TP 1, TP 2, and TP 3 is measured, and based on the three TOAs, the RSTD for TP 1-TP 2, RSTD for TP 2-TP 3, and RSTD for TP 3-TP 1 are calculated. Based on this, a geometric hyperbola is determined, and the point where this hyperbola intersects can be estimated as the location of the UE. At this time, since there may be accuracy and / or uncertainty regarding each TOA measurement, the estimated location of the UE may be known as a specific range due to measurement uncertainty.

[0158] E-CID (Enhanced Cell ID)

[0159] In a cell ID (CID) positioning method, the location of a UE can be determined through geographic information of the UE's serving ng-eNB, serving gNB, and / or serving cell. For example, geographic information of the serving ng-eNB, serving gNB, and / or serving cell can be obtained through paging, registration, etc.

[0160] Meanwhile, the E-CID positioning method may utilize additional UE measurements and / or NG-RAN radio resources to improve UE location estimates in addition to the CID positioning method. In the E-CID positioning method, some of the same measurement methods as the measurement control system of the RRC protocol may be used, but generally, no additional measurements are performed solely for the purpose of measuring the UE's location. In other words, a separate measurement configuration or measurement control message may not be provided to measure the UE's location, and the UE may report measurements obtained through generally measurable measurement methods without expecting that additional measurement actions for location measurement alone will be requested.

[0161] For example, the serving gNB can implement an E-CID positioning method using E-UTRA measurements provided by the UE.

[0162] AI / ML (Artificial intelligence / machine learning)

[0163] With the advancement of AI / ML technology, the node(s) and terminal(s) constituting wireless communication networks are becoming more intelligent and sophisticated. In particular, due to the intelligence of networks and base stations, it is expected that various network / base station decision parameter values ​​(e.g., transmit / receive power of each base station, transmit power of each terminal, precoder / beam of base stations / terminals, time / frequency resource allocation for each terminal, duplex method of each base station, etc.) can be rapidly optimized, derived, and applied according to various environmental parameters (e.g., distribution / location of base stations, distribution / location / material of buildings / furniture, location / movement direction / speed of terminals, weather information, etc.). In line with this trend, many standardization organizations (e.g., 3GPP, O-RAN) are considering its adoption, and research on this is actively underway.

[0164] AI / ML can be easily referred to as deep learning-based artificial intelligence in a narrow sense, but conceptually it is as shown in Fig. 8.

[0165] - Artificial Intelligence: This can refer to any automation where machines can take over tasks that humans would otherwise have to perform.

[0166] - Machine Learning: Machines can learn patterns for decision-making from data on their own without explicitly programming rules.

[0167] - Deep Learning: An AI / ML model based on artificial neural networks in which machines perform everything from feature extraction to judgment from unstructured data in a single step. The algorithm relies on multilayer networks composed of interconnected nodes for feature extraction and transformation, inspired by biological nervous systems, or neural networks. Common deep learning network architectures may include deep neural networks (DNN), recurrent neural networks (RNN), and convolutional neural networks (CNN).

[0168] Classification of AI / ML types based on various criteria

[0169] 1. Offline vs. Online

[0170] (1) Offline Learning: This follows a sequential procedure 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 utilized for prediction tasks. This offline learning method is used in most situations. In offline learning, the system does not learn incrementally; instead, learning is performed using all available collected data, and the data is applied to the system without further learning. If learning on new data becomes necessary, learning can be restarted using the entire new dataset.

[0171] (2) Online Learning: Online learning is a method of gradually improving performance by incrementally learning with additional data, utilizing the fact that data available for recent learning is continuously generated through the internet. Learning is performed in real-time in units of specific data (batch) collected online, and as a result, the system can quickly adapt to changing data.

[0172] To build an AI system, only online learning may be used to perform learning using only real-time data, or offline learning may be performed using a predetermined dataset, followed by additional learning using real-time data that is subsequently generated (online + offline learning).

[0173] 2. Classification based on AI / ML Framework concepts

[0174] (1) Centralized Learning: Training data collected from multiple different nodes is reported to a centralized node, and all data resources, storage, and learning (e.g., supervised, unsupervised, reinforcement learning) are performed at a single centralized node.

[0175] (2) Federated Learning: A collective AI / ML model is constructed based on data distributed across data owners. Instead of bringing data into the AI / ML model, the AI / ML model is brought as a data source, allowing local nodes / individual devices to collect data and train their own copies of the AI / ML model, thus eliminating the need to report source data to a central node. In Federated learning, the parameters / weights of the AI / ML model simply need to be sent back to a centralized node to support general AI / ML model training. The advantages of Federated learning include increased computational speed and superiority in terms of information security. In other words, the process of uploading personal data to a central server is unnecessary, which can prevent the leakage and misuse of personal information.

[0176] (3) Distributed Learning: This refers to the concept where the machine learning process is extended and distributed across a node cluster. To accelerate the training of AI / ML models, the models are partitioned and shared across multiple nodes operating simultaneously.

[0177] 3. Classification by Learning Method

[0178] (1) Supervised Learning: Supervised learning is a machine learning task that aims to learn a mapping function from input to output given a labeled dataset. The input data is called training data and has known labels or outcomes. Examples of supervised learning include (i) Regression: Linear Regression, Logistic Regression, (ii) Instance-based Algorithms: k-Nearest Neighbor (KNN), (iii) Decision Tree Algorithms: CART, (iv) Support Vector Machines: SVM, (v) Bayesian Algorithms: Naive Bayes, and (vi) Ensemble Algorithms: Extreme Gradient Boosting, Bagging: Random Forest. Supervised learning can be further grouped into regression and classification problems; classification involves predicting labels, while regression involves predicting quantities.

[0179] (2) Unsupervised Learning: A machine learning task that aims to learn a function that explains a hidden structure in unlabeled data. The input data is unlabeled and has no known results. Some examples of unsupervised learning include K-means clustering, principal component analysis (PCA), nonlinear independent component analysis (ICA), and LSTM.

[0180] (3) Reinforcement Learning: In reinforcement learning (RL), agents aim to optimize long-term goals by interacting with the environment through a trial-and-error process; it is goal-oriented learning based on interaction with the environment. Examples of RL algorithms include (i) Q-learning, (ii) Multi-armed bandit learning, (iii) Deep Q Network, State-Action-Reward-State-Action (SARSA), (iv) Temporal Difference Learning, (v) Actor-critic reinforcement learning, (vi) Deep deterministic policy gradient, and (vii) Monte-Carlo tree search. Reinforcement learning can be further grouped into AI / ML model-based reinforcement learning and AI / ML model-free reinforcement learning. Model-based reinforcement learning is an RL algorithm that uses predictive AI / ML models to obtain transition probabilities between states by utilizing various dynamic states of the environment and AI / ML models where these states lead to rewards. Model-free reinforcement learning is an RL algorithm based on values ​​or policies to achieve maximum future rewards; in multi-agent environments / states, it is computationally less complex and does not require an accurate representation of the environment. Meanwhile, RL algorithms can also be classified into value-based RL versus policy-based RL, policy-based RL versus non-policy RL, etc.

[0181] AI / ML models

[0182] Figure 9 illustrates a Feed-Forward Neural Network (FFNN) AI / ML model. Referring to Figure 9, the FFNN AI / ML model includes an input layer, a hidden layer, and an output layer.

[0183] Figure 10 illustrates a Recurrent Neural Network (RNN) AI / ML model. Referring to Figure 10, the RNN AI / ML model is a type of artificial neural network in which hidden nodes are connected by directed edges to form a directed cycle structure; it is an AI / ML model suitable for processing sequentially appearing data such as speech and text. One type of RNN is the Long Short-Term Memory (LSTM), which is a structure in which a cell state is added to the hidden state of the RNN. Specifically, in the LSTM, an input gate, a forget gate, and an output gate are added to the RNN cell, and a cell state is added. In Figure 10, A is the neural network, x t is the input value, h t represents the output value. Here, h t can mean a state value representing the current based on time, and h t-1 can represent the previous state value.

[0184] Figure 11 illustrates a Convolutional Neural Network (CNN) AI / ML model. CNNs are used for two purposes: to reduce the complexity of AI / ML models and to extract useful features, by applying convolution operations commonly used in image processing. Referring to Figure 11, a kernel or filter refers to a unit or structure that applies weights to inputs within a specific range or unit. The kernel (or filter) can be changed through training. A stride refers to the range of movement of the kernel within the input. A feature map refers to the result of applying the kernel to the input. Padding refers to values ​​added to adjust the size of the feature map. Multiple feature maps may be extracted to induce robustness against distortion, alteration, etc. Pooling refers to operations (e.g., max pooling, average pooling) to reduce the size of the feature map by downsampling it.

[0185] Figure 12 illustrates an auto-encoder AI / ML model. Referring to Figure 12, an auto-encoder is a neural network that takes a feature vector x as input and outputs an identical or similar vector x', where the input and output nodes share the same features and is a type of unsupervised learning. Since the auto-encoder reconstructs the input, the output can be referred to as a reconstruction. The loss function can be expressed as shown in Equation 1.

[0186]

[0187] The loss function of the autoencoder exemplified in Fig. 12 is calculated based on the difference between the input and the output, and based on this, the degree of loss of the input is determined, and the autoencoder performs an optimization process to minimize the loss.

[0188] Figure 13 is a diagram illustrating segmented AI inference.

[0189] Figure 13 illustrates a split AI operation in which, in particular, the Model Inference function is performed in cooperation between an end device such as a UE and a network AI / ML endpoint.

[0190] In addition to the Model Inference function, the Model Training function, Actor, and Data Collection function can each be split into multiple parts depending on the current task and environment, and can be performed through the cooperation of multiple entities.

[0191] For example, computationally intensive and energy-intensive parts may be performed at a network endpoint, while privacy-sensitive and latency-sensitive parts may be performed at an end device. In this case, the end device can execute a task / model up to a specific part / layer from the input data and then transmit intermediated data to the network endpoint. The network endpoint executes the remaining part / layer and provides the inference outputs to one or more devices performing the operation / task.

[0192] The following describes a functional framework for AI operation.

[0193] Below, to provide a more specific explanation of AI (or AI / ML), terms may be defined as follows.

[0194] - Data collection: Data collected from network nodes, management entities, or UEs, etc., as a basis for AI model training, data analysis, and inference.

[0195] - AI Model: A data-driven algorithm that applies AI technology to generate a set of outputs containing predictive information and / or decision parameters based on a set of inputs.

[0196] - AI / ML Training: An online or offline process of training an AI model by learning features and patterns that best represent data and acquire an AI / ML model trained for inference.

[0197] - AI / ML Inference: A process of making predictions or deriving decisions based on collected data and AI models using trained AI models.

[0198] Referring to FIG. 14, the data collection function (10) is a function that collects input data and provides processed input data to the model training function (20) and the model inference function (30).

[0199] Examples of input data may include measurements from UEs or other network entities, feedback from actors, and outputs from AI models.

[0200] The Data Collection function (10) performs data preparation based on input data and provides the input data processed through data preparation. Here, the Data Collection function (10) does not perform specific data preparation (e.g., data pre-processing and cleaning, forming and transformation) for each AI algorithm, and can perform data preparation common to AI algorithms.

[0201] After the data preparation process is performed, the Model Training 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 data required as input for the AI ​​Model Training function (20). The inference data (12) is the data required as input for the AI ​​Model Inference function (30).

[0202] The Data Collection function (10) may be performed by a single entity (e.g., UE, RAN node, network node, etc.) but may also be performed by multiple entities. In this case, Training Data (11) and Inference Data (12) from multiple entities may be provided to the Model Training function (20) and Model Inference function (30), respectively.

[0203] The Model Training function (20) is a function that performs AI model training, validation, and testing, which can generate model performance metrics as part of the AI ​​model testing procedure. If necessary, the Model Training function (20) also handles data preparation (e.g., data pre-processing and cleaning, forming and transformation) based on the Training Data (11) provided by the Data Collection function (10).

[0204] Here, Model Deployment / Update (13) is used to initially deploy a trained, validated, and tested AI model to the Model Inference function (30) or to provide an updated model to the Model Inference function (30).

[0205] The Model Inference function (30) is a function that provides AI model inference output (16) (e.g., prediction or decision). The Model Inference function (30) can provide model performance feedback (14) to the Model Training function (20) where applicable. Additionally, the Model Inference function (30) is responsible for data preparation (e.g., data pre-processing and cleaning, forming and transformation) based on the inference data (12) provided by the Data Collection function (10) if necessary.

[0206] 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.

[0207] Model Performance Feedback (14) can be used to monitor the performance of the AI ​​model if available, and this feedback may be omitted.

[0208] The Actor function (40) is a function that receives an output (16) from the Model Inference function (30) and triggers or performs a corresponding operation / action. The Actor function (40) can trigger an operation / action on another entity (e.g., one or more UEs, one or more RAN nodes, one or more network nodes, etc.) or on itself.

[0209] Feedback (15) can be used to derive training data (11) and inference data (12), or to monitor the performance of the AI ​​model, the impact on the network, etc.

[0210] Meanwhile, the definitions of training, validation, and testing in data sets used in AI / ML can be distinguished as follows.

[0211] - Training data: Refers to the data set used to train a model.

[0212] - Validation data: Refers to a data set used to validate a model that has already been trained. In other words, it refers to a data set typically used to prevent overfitting of the training data set.

[0213] Furthermore, it refers to a data set used to select the best among various models learned during the learning process. Therefore, it can also be viewed as a type of learning.

[0214] - Test data: Refers to the data set for final evaluation. This data is unrelated to training.

[0215] In the case of the above data set, generally, if the training set is divided, the training data and validation data within the entire training set can be divided in a ratio of about 8:2 or 7:3, and if the test is included, it can be divided in a ratio of 6:2:2 (training: validation: test).

[0216] Depending on the capability of the AI / ML function between the base station and the terminal, the cooperation level can be defined as follows, and variations resulting from the combination of multiple levels or the separation of any one level are also possible.

[0217] Cat 0a) No collaboration framework: AI / ML algorithms are based on pure implementation and do not require changes to the wireless interface.

[0218] Cat 0b) This level corresponds to a framework that involves a wireless interface modified to fit efficient implementation-based AI / ML algorithms but without cooperation.

[0219] Cat 1) Inter-node support is involved to improve the AI / ML algorithms of each node. This applies when a UE receives support from a gNB (for training, adaptation, etc.) and vice versa. At this level, model exchange between network nodes is not required.

[0220] Cat 2) Joint ML operations can be performed between the UE and gNB. This level requires the exchange of AI / ML model commands or network nodes.

[0221] The functions previously exemplified in FIG. 14 may be implemented in RAN nodes (e.g., base station, TRP, central unit (CU) of the base station, etc.), network nodes, OAM (operation administration maintenance) of the network operator, or UE.

[0222] Alternatively, two or more entities among a RAN, a network node, a network operator's OAM, or a UE may cooperate to implement the functions exemplified in FIG. 14. For example, one entity may perform some of the functions of FIG. 14, and another entity may perform the remaining functions. As such, some of the functions exemplified in FIG. 14 are performed by a single entity (e.g., UE, RAN node, network node, etc.), the transmission / provision of data / information between each function may be omitted. For example, if the Model Training function (20) and the Model Inference function (30) are performed by the same entity, the transmission / provision of Model Deployment / Update (13) and Model Performance Feedback (14) may be omitted.

[0223] Alternatively, any one of the functions exemplified in FIG. 14 may be performed by two or more entities among the RAN, network node, network operator's OAM, or UE in collaboration. This may be referred to as a split AI operation.

[0224] Figure 15 illustrates a case where the AI ​​Model Training function is performed by a network node (e.g., a core network node, the network operator's OAM, etc.) and the AI ​​Model Inference function is performed by a RAN node (e.g., a base station, TRP, the base station's CU, etc.).

[0225] Step 1: RAN Node 1 and RAN Node 2 transmit input data (i.e., training data) for AI Model Training to the network node. Here, RAN Node 1 and RAN Node 2 may also transmit data collected from the UE (e.g., UE measurements related to RSRP, RSRQ, and SINR of the serving cell and neighboring cells, UE location, velocity, etc.) to the network node.

[0226] Step 2: The network node trains the AI ​​model using the received training data.

[0227] Step 3: The network node distributes / updates the AI ​​Model to RAN Node 1 and / or RAN Node 2. RAN Node 1 (and / or RAN Node 2) may continue model training based on the received AI Model.

[0228] For the sake of convenience of explanation, it is assumed that the AI ​​Model was deployed / updated only to RAN Node 1.

[0229] Step 4: RAN Node 1 receives input data (i.e., Inference data) for AI Model Inference from UE and RAN Node 2.

[0230] Step 5: RAN Node 1 performs AI Model Inference using the received Inference data to generate output data (e.g., prediction or decision).

[0231] Step 6: If applicable, RAN node 1 can transmit model performance feedback to network nodes.

[0232] 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 an action based on the output data. For example, in the case of a load balancing action, a UE may move from RAN Node 1 to RAN Node 2.

[0233] Step 8: RAN Node 1 and RAN Node 2 transmit feedback information to the network node.

[0234] Figure 16 illustrates a case where both the AI ​​Model Training function and the AI ​​Model Inference function are performed by RAN nodes (e.g., base station, TRP, base station CU, etc.).

[0235] Step 1: UE and RAN Node 2 transmit input data (i.e., training data) for AI Model Training to RAN Node 1.

[0236] Step 2: RAN Node 1 trains the AI ​​Model using the received Training data.

[0237] Step 3: RAN Node 1 receives input data (i.e., Inference data) for AI Model Inference from UE and RAN Node 2.

[0238] Step 4: RAN Node 1 performs AI Model Inference using the received Inference data to generate output data (e.g., prediction or decision).

[0239] 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 an action based on the output data. For example, in the case of a load balancing action, a UE may move from RAN Node 1 to RAN Node 2.

[0240] Step 6: RAN Node 2 transmits feedback information to RAN Node 1.

[0241] Figure 17 illustrates a case where the AI ​​Model Training function is performed by a RAN node (e.g., base station, TRP, base station CU, etc.) and the AI ​​Model Inference function is performed by a UE.

[0242] Step 1: The UE transmits input data (i.e., training data) for AI model training to the RAN node. Here, the RAN node may collect data from various UEs and / or other RAN nodes (e.g., UE measurements related to RSRP, RSRQ, SINR of the serving cell and neighboring cells, UE location, velocity, etc.).

[0243] Step 2: The RAN node trains the AI ​​model using the received training data.

[0244] Step 3: The RAN node distributes / updates the AI ​​Model to the UE. The UE may continue model training based on the received AI Model.

[0245] Step 4: Receive input data (i.e., inference data) for AI Model Inference from the UE and RAN nodes (and / or other UEs).

[0246] Step 5: The UE performs AI Model Inference using the received Inference data to generate output data (e.g., prediction or decision).

[0247] Step 6: If applicable, the UE can send model performance feedback to the RAN node.

[0248] Step 7: The UE and RAN nodes perform actions based on the output data.

[0249] Step 8: The UE transmits feedback information to the RAN node.

[0250] AI / ML-based channel estimation

[0251] In existing NR (New Radio) systems, the CSI-RS (channel state information-reference signal) is primarily used for time and / or frequency tracking, CSI computation, etc. CSI (channel state information) is a general term for information that can indicate the quality of the radio channel (also called a link) formed between a terminal and an antenna port. A base station can transmit CSI-RS to a terminal to determine the characteristics of the downlink channel and can receive feedback from the terminal regarding channel measurement results based on CSI-RS. Such CSI-RS-based CSI measurement / reporting is performed based on configured N-ports (e.g., in existing NR, the Max number of N is 32, N_max=32) CSI-RS(s). Additionally, in CSI measurement / reporting, the same N can be applied to each of the CSI-RS resources (sets) configured via RRC signaling.

[0252] According to conventional 3GPP NR standards (e.g., 3GPP TS 38.211 and 3GPP TS 38.331, etc.), at least one set of CSI-RS resources is established through upper layer signaling, and CSI-RS is transmitted based on one or more CSI-RS resources (e.g., CSI-RS resource IE), each CSI-RS resource is defined on CSI-RS REs and mapped to one or more CSI-RS ports.

[0253] Specifically, the number of CSI-RS ports can be individually set for each of the multiple CSI-RS resources included in the CSI-RS resource set. For example, a first CSI-RS port number may be set for the first CSI-RS resource included in the CSI-RS resource set, and a second CSI-RS port number may be set for the second CSI-RS resource. However, when performing CSI measurement / reporting, particularly for Codebook-based reporting (e.g., type 1, type 2), all CSI-RS resources may be CSI-RS resources with the same number of ports set, in order to ensure reporting consistency and port mapping consistency.

[0254] Meanwhile, "CSI-RS resource" as an RRC parameter name is information that determines CSI-RS REs. The CSI-RS resource IE includes information such as OFDM symbol location (firstOFDMSymbolInTimeDomain), frequency domain resource (freqDomainAllocation), density (one, three, dot5), number of ports (rowNumber or numberOfPorts), and sequence arrangement per port according to CDM / FDM / TDM methods; based on this information, the CSI-RS REs on the resource grid are determined. In the following description, the CSI-RS resource may refer to at least one of the CSI-RS IE and / or CSI-RS RE(s) as RRC parameters.

[0255] Each CSI-RS resource is associated with one or more antenna ports, and a unique CSI-RS sequence assigned to each port is mapped to a designated RE and transmitted. Therefore, the entire set of REs to which the sequences corresponding to the ports included in the CSI-RS resource are mapped defines the physical transmission structure of the CSI-RS resource, and the terminal can receive these CSI-RS REs to measure channel quality.

[0256] When multiple ports are configured on a CSI-RS resource, these multiple ports can be multiplexed in the following ways. If multiple ports are mapped to the same RE(s) (same time and frequency resources), these ports are transmitted simultaneously using Code Division Multiplexing (CDM). Conversely, if different ports are mapped to REs separated by time or frequency, the multiplexing method for these ports becomes Time Division Multiplexing (TDM) or Frequency Division Multiplexing (FDM).

[0257] There are Full-port mapping / transmission and partial-port mapping / transmission methods for mapping CSI-RS ports to REs and TXRU (transceiver unit).

[0258] In the case of full-port mapping, the full MIMO channel can be measured at the receiving end. For example, all transmission ports configured for the corresponding CSI-RS resource are mapped to CSI-RS REs without omission, and the full MIMO channel from all TXRUs can be measured at the terminal. For example, full-port mapping refers to the case where a CSI-RS sequence is transmitted over all CSI-RS REs of a specific CSI-RS resource for the entire set of ports configured for that specific CSI-RS resource. For example, if ports 3000 to 3003 are configured for a specific CSI-RS resource and all ports 3000 to 3003 are mapped using the REs of that resource, it can be called full-port mapping / transmission.

[0259] Partial-port mapping / transmission is a method of measuring the channel using only a subset of CSI-RS ports to reduce CSI-RS overhead. When partial-port mapping / transmission is used, additional calculations (e.g., channel interpolation and / or extrapolation, additional CSI calculation procedures, etc.) may be required to reconstruct the full MIMO channel. For example, "transmitting CSI-RS via a partial port" refers to a case where a CSI-RS sequence is mapped and transmitted only to a subset of ports within a configured set of ports in a resource. For instance, if a CSI-RS resource is configured with ports 3000 through 3003, but CSI-RS is transmitted only to ports 3000 and 3001 while the remaining ports are not, this constitutes partial port transmission. When only some ports are transmitted, only some REs may be utilized; however, it cannot be definitively assumed that only some REs are used, as this may vary depending on the multiplexing method between the transmitting ports. For example, if only one of the two ports multiplexed with the first CDM is transmitted, and only one of the other two ports multiplexed with the second CDM is transmitted, this corresponds to a case where only some ports are transmitted but the entire REs are utilized.

[0260] Next-generation wireless communication (e.g., 6G) considers massive MIMO environments (e.g., N_max=64, 128, or higher). If full-port CSI-RS is used in such next-generation wireless communication, resource overhead is expected to intensify due to the increase in the number of ports. On the other hand, while partial-port CSI-RS has advantages in terms of resource overhead, if channel estimation is performed based on existing linear algorithms, the channel estimation results differ from the actual full MIMO channel. Partial-port CSI-RS is disadvantageous in terms of accuracy and performance, and the degradation of accuracy and performance is exacerbated, particularly as fewer ports are used for partial-port CSI-RS.

[0261] As a solution to these problems, a method of estimating / reconstructing the full-port channel using AI / ML for partial-port CSI-RS measurements can be considered. For example, a terminal acquires measurement information regarding the partial-port CSI-RS and inputs this information into an AI / ML model. The terminal can obtain a full-port channel estimation result through the inference of the AI / ML model.

[0262] The full-port channel estimation performed based on Partial-port CSI-RS in this manner can be based on the following spatial / frequency / temporal correlations.

[0263] First, spatial correlation may relate to the structural characteristics of MIMO antennas. For example, since spatial correlation exists between antenna ports, partial-port CSI-RS information not only partially reflects the characteristics of the channel state occurring across all ports, but adjacent antenna ports frequently exhibit similar channel response characteristics. Therefore, an AI / ML model can construct a full-port channel estimation by learning such spatial correlations.

[0264] Second, frequency correlation utilizes the characteristic that channel responses between adjacent frequencies / sub-carriers have a high correlation. By utilizing information provided by partial-port CSI-RS from a specific frequency resource, the channel response of another frequency band corresponding to the full-port can be estimated.

[0265] Third, temporal correlation utilizes the characteristic that channel responses maintain similar temporal patterns in static environments or environments with minimal user movement. Full-port channel estimation can be performed based on the temporal continuity of partial-port CSI-RS information.

[0266] Based on these characteristics, the AI / ML model can reconstruct a full-port channel by learning spatial, frequency, and temporal patterns from partial-port CSI-RS, and since it can also learn non-linear relationships regarding the aforementioned features, it can operate by inferring relationships between ports with low correlation.

[0267] In this specification, we propose a method in which, when a partial-port CSI-RS is established, an AI / ML model learns the spatial, frequency, and temporal patterns of the partial-port CSI-RS based on it, and obtains channel estimation performance similar to that of a full-port CSI-RS through the channel estimation of the learned AI / ML model.

[0268] According to the proposed method, partial-port CSI-RS is utilized for CSI measurement and reporting, resulting in lower transmission resource usage compared to full-port CSI-RS and consequently improved efficiency in frequency and time resource utilization. Furthermore, since the reduced resource usage resulting from the application of partial-port CSI-RS can be utilized for data scheduling, there are advantages in terms of network throughput as well.

[0269] The distinction between Proposals 1 and 2 described below is for convenience of explanation, and Proposals 1 and 2 may each be implemented separately, but they do not necessarily mean embodiments configured separately, and at least some parts of Proposals 1 and 2 may be implemented in a combined form.

[0270] Proposal 1

[0271] The terminal can infer full-port channel estimation information using partial-port CSI-RS information as input through an AI / ML model.

[0272] For example, accuracy information regarding channel estimation and / or valid time duration information for maintaining such accuracy can be set / used as performance metrics for each partial-port CSI-RS resource(s). Based on such performance metrics, AI / ML model management, such as AI / ML model performance monitoring and / or model updates, can be performed.

[0273] For example, a metric for resource evaluation for each partial-port can be defined, set, or calculated. The metric can be set with the following weights or determined / calculated based on said weights. As a specific example, the weights can be set as weight = a*accuracy + b*duration, where a / b represent adjustment coefficients. At least one of the a / b coefficients can be determined based on pre-definition or network signaling. Alternatively, at least one of the a / b coefficients may be determined by the terminal itself or based on the results of training an AI / ML model. At least one of the a / b coefficients may be set to 0, in which case the weight may be determined based on either the accuracy information or the effective duration of the accuracy.

[0274] Based on the corresponding metric, RS(s) associated with resource(s) whose weight is higher than or equal to the threshold (e.g., high spatial / frequency / temporal similarity or based on pre-configured connection relationship information) may be triggered / transmitted / received. Conversely, RS(s) associated with resource(s) whose weight is lower than the threshold may be turned off or operated by reducing the transmission / reception frequency in terms of temporal / frequency density. For example, the threshold may be set / determined based on predefined or network signaling.

[0275] For example, a network / base station can signal configuration information of the corresponding performance indicator to a terminal.

[0276] Alternatively, the terminal may perform reporting based on operations defined in the standard and corresponding performance indicators. For example, the terminal may report resources requiring RS(s) trigger / transmission / reception and / or resources requiring RS off / transmission / reception frequency reduction based on the corresponding performance indicators.

[0277] Even after RS ​​resource control, such as triggering or turning off the RS, has been performed, if the specific accuracy and / or valid time duration for each partial-port CSI-RS are not consistently satisfied, a model update operation may be performed.

[0278] For example, in order to increase the accuracy of full-port channel estimation and / or increase the effective time duration, the terminal may request CSI-RS transmission based on the reported information by reporting some or all of the preferred port configuration relationships, preferred frequency information (e.g., Sub-Band(s)), and / or partial-port CSI-RS resource / resource group to the base station, and subsequently, the terminal may receive the partial-port CSI-RS(s) and use them for model training / update.

[0279] Alternatively, the base station may instruct the terminal to report the relevant information for the purpose of comparing performance with specific port(s), and operations such as changing potential ports may be performed accordingly.

[0280] Meanwhile, since performance may be degraded due to the difference between the learned frequency band and the actual inference frequency band, the terminal may report information about the frequency domain applied during training / retraining / fine-tuning to the base station.

[0281] Below, more specific examples regarding the above proposal are described.

[0282] Proposal 1 includes a method for performing monitoring and model updates by utilizing this as a model performance indicator, taking into account that when a terminal performs full-port channel estimation through an AI / ML model using partial-port CSI-RS information as input, the performance of channel estimation and / or the effective time interval for maintaining said performance may differ for each resource.

[0283] When a terminal estimates full-port channel estimation information using partial-port CSI-RS as input, partial-port CSI-RS with different numbers of ports can reflect various spatial or frequency characteristics. When combining CSI-RS information with different numbers of ports, spatial / frequency correlations must be considered, and if the number of ports of each CSI-RS information is different, the importance of each CSI-RS information can be assigned differently and used for learning and estimation.

[0284] For example, accuracy can be calculated using metrics such as NMSE between the output of an AI / ML model and the full-port CSI-RS used as the actual ground truth label.

[0285] For example, the time when channel estimation information is valid can be evaluated based on temporal correlation.

[0286] It is common for the channel estimation accuracy and corresponding effective time duration with respect to ground truth label information to differ depending on the partial-port CSI-RS, and a method is needed to manage AI / ML models by reflecting these characteristics.

[0287] In terms of accuracy, since estimation accuracy may vary depending on the number of partial-port ports and their spatial / frequency distribution, resources with low accuracy may not be used as model inputs, or they may be used but with reduced weights to be reflected in the training data. Similar attributes are expected in terms of effective time duration, and accuracy and duration may fluctuate depending on external factors such as user movement speed or multipath environments.

[0288] The accuracy and / or valid time intervals of each partial-port CSI-RS can be set as key performance indicators, and the performance status of the corresponding AI / ML model can be determined based on resource-specific performance. For example, if the accuracy and / or time duration values ​​consistently remain below a preset threshold, it may be determined that there is an abnormality in the performance of the corresponding AI / ML model. For instance, the terminal can periodically report performance monitoring results to the base station. The reported information may include at least one of the accuracy and valid time duration information for each resource.

[0289] For example, an AI / ML model update may be triggered when the accuracy consistently and / or over a specific time interval is lower than a certain number of times, or when the effective time duration is shorter than a certain threshold and the resource increases to a certain level. In this case, it may mean that a model update is performed when the terminal is placed in an environment different from the environment in which the current model was trained, or when the terminal's movement speed changes.

[0290] Alternatively, after the terminal reports accuracy and / or valid time duration information to the base station, a method may be used in which resource(s) with short specific accuracy and / or valid time durations are switched on or off according to an agreement between the terminal and the base station, or the training weight for the AI / ML model is readjusted by setting the temporal / frequency density of transmission for the said resource to a low level. For example, the terminal / network may operate based on a value table derived from accuracy and valid time duration levels based on a pre-configured RS configuration.

[0291] For operation according to the above proposal, the terminal requires full-port CSI-RS and partial-port CSI-RS resource configurations to calculate CSI. In this case, the partial-port CSI resource set can primarily be utilized as input for training and inference of AI / ML models. The full-port CSI-RS resource set can be utilized for ground truth labels for AI / ML models or for existing CSI measurement and reporting purposes.

[0292] The terminal may report the maximum number of full-ports (e.g., 32 ports) or types of full-ports (e.g., 8 ports, 16 ports, 32 ports) that the terminal can support through terminal capability reporting, and may also report the minimum number of partial-ports for each full-port. For example, it may report values ​​such as (minimum number of ports, max number of ports) = {(4,8), (4,16), (8, 32)}.

[0293] The following exemplary operations can be considered regarding the method of receiving part or all of the resource configuration information. Regarding resource configuration, port mapping can be considered for CSI-RS with the same or different number of ports. For example, for four identical 4-port CMRs (channel measurement resources, e.g., CSI-RS), when the number of full-ports is 16, these ports can be mapped so that they do not overlap. For example, they can be mapped so that they do not overlap, such as CMR1 port configuration = {1, 2, 3, 4}, CMR2 port configuration = {5, 6, 7, 8}, CMR3 port configuration = {9, 10, 11, 12}, and CMR4 port configuration = {13, 14, 15, 16}. Based on this, the CSI-RS ports transmitted to the actual inference can be indicated by a bitmap. For example, if the bitmap information is '1010', the terminal may infer a 16-port channel estimation by using a partial 8-port consisting of CMR1 and CMR3 as input to an AI / ML model.

[0294] [Example #1]

[0295] The terminal utilizes the existing full-port CSI-RS resource set for existing CSI measurements and reports, and, if necessary, can use some or all resources within that set as ground truth labels. Additionally, an additional partial-port CSI resource set may be configured for operation according to the above proposal.

[0296] For example, a terminal can receive an N-port CSI-RS resource set from a base station via an RRC message. The terminal can utilize this resource set to calculate and report CSI as before, and, if necessary, operate by setting part or all of the resource set as ground truth labels for training an AI / ML model.

[0297] Additionally, the terminal may receive configuration information for a partial-port CSI-RS resource set from a base station. Each resource setting in the set may include at least one of antenna port mapping information, frequency / time domain resource (RE) information, and connection relationship information with a highly correlated full-port CSI-RS resource. For each resource within the full-port CSI-RS resource set, relationship information between the connected partial-port CSI-RS resource and the resource set may be provided. Based on the connected relationships, the terminal may estimate full-port channel estimation information using the partial-port CSI-RS information as input.

[0298] [Example #2]

[0299] The terminal may perform full-port channel estimation based on existing resource configuration and reporting methods. The terminal may receive configurations from the base station for a CSI-RS resource set that include not only full-port CSI-RS(s) but also multiple partial-port CSI-RS resources. The full-port CSI-RS(s) for the set may be used for ground truth labeling purposes. In this case, linkage information may be established between the CSI-RS resource / resource set for ground truth labeling purposes and the partial-port CSI-RS resource / resource set. This linkage information may be established based on the mapping relationship between REs and TXRU for CSI-RS ports, the similarity of frequency / time / spatial-domain configuration information, or may include identical / adjacent subband configurations. Additionally, the partial-port CSI-RS may be used for performance monitoring purposes. In this case, even if the base station is the entity performing the performance monitoring, the base station may receive the CSI report from the partial-port CSI-RS for performance monitoring and directly compare it with the predicted CSI report reported by the terminal. In this case, performance monitoring can be performed by comparing the partial-port CSI report for performance monitoring purposes with the predicted CSI report for the full-port channel reported by the terminal.Alternatively, the terminal may perform a performance monitoring report by receiving partial-port CSI-RS for monitoring, comparing the full-port channel estimated by the terminal with the predicted CSI, and calculating a metric. In this case, the terminal may receive the ground truth channel for the partial-port for performance monitoring purposes, compare it with the predicted channel for the full-port channel, and report a metric.

[0300] In addition, it is necessary to consider the method of setting QCL for the aforementioned CSI-RS resource(s). Resource(s) within the same set may assume the same QCL-Type D, or different QCL-Type D may be set / instructed for each resource / resource-group, and the connection relationship information may be constructed based on this. In this case, to achieve high similarity with the channel information configured by the corresponding ground truth label, it may be necessary to use partial-port CSI-RS(s) as input that are identical or similar to the configuration information in the spatial, frequency, and temporal domains of the corresponding CSI-RS. Therefore, to facilitate more efficient learning, the terminal may include the preferred CSI-RS(s) as additional information in the existing CSI report or transmit this information to the base station via a UE-initiated method.

[0301] In addition, the power control (Pc_max) value for each resource of the configured full-port CSI-RS resource / resource set and / or partial-port CSI-RS resource / resource set may be determined differently. For example, in the case of a specific CSI-RS used for ground truth labels, a higher value may be set compared to other CSI-RS used for input or training purposes to increase the reliability of the ground truth label, and the received response may be amplified by transmitting with high power.

[0302] Therefore, if the terminal reports at least one of the accuracy and duration information of each resource to the base station, a new partial-port resource to be used based on the reported information may be predefined / agreed upon between the terminal and the base station, or the base station may explicitly trigger another partial-port CSI resource / resource group to operate. Alternatively, the learning weight can be adjusted by utilizing the reported information, such as by readjusting the learning weight for a specific resource or increasing the time duration period of that resource.

[0303] Proposal 2

[0304] Prediction accuracy / reliability information related to full-port channel estimation can be calculated based on accuracy information and / or validity time information for each partial-port CSI-RS, and a CSI report for full-port channel estimation can be configured / set based on the level of calculated prediction accuracy / reliability information.

[0305] [Configuration Information]

[0306] In addition to the (existing) CSI report, the following information may be considered / reported.

[0307] Prediction accuracy / reliability information can be calculated through a metric for each resource determined by combining accuracy and valid time duration information. For resource(s) with high prediction accuracy / reliability (or high priority), high-resolution channel information (e.g., explicit feedback) and related details may be reported. Conversely, for resource(s) with low prediction accuracy / reliability (or low priority), information may not be reported, or it may be included in the report but in a compressed form. Indicators for the compressed form may be included in the report.

[0308] - CQI margin values ​​based on accuracy information may also be reported.

[0309] [Setup Method]

[0310] For example, a terminal may be requested by a base station to perform a CSI report based on a specific partial-port CSI-RS and full-port channel estimation. Alternatively, the terminal may include full-port channel estimation in the CSI report during a time interval when AI / ML channel estimation is valid, according to a pre-configured CSI report configuration.

[0311] Resources with an effective time duration greater than or equal to the threshold are reported via the periodic CSI report, while resources with an effective time duration shorter than the threshold can be reported via the aperiodic CSI report.

[0312] Alternatively, a CSI report for a specific resource may be triggered if its accuracy and / or effective time duration fall below a set threshold.

[0313] More specific examples of Proposal 2 are described below.

[0314] The terminal may select target resource(s) to be reported based on accuracy and valid time duration results and include them in the CSI report. In this case, reporting of resource(s) that do not satisfy the conditions may be omitted. Resource(s) with a valid time duration greater than or equal to a threshold may be reported as a periodic report, and resource(s) with a valid time duration less than the threshold may be reported as an aperiodic report. For example, a CSI report based on full-port channel estimation of an AI / ML model may be reported separately or may be reported as a single CSI report together with existing legacy CSI information.

[0315] The method for configuring CSI reports considering the accuracy and validity time of Partial-port CSI-RS can be configured by extending existing methods or by configuring them as separate CSI reports. For example, the terminal may exclude resources with low accuracy and / or validity time duration from reporting, or report them in a compressed form by performing a certain compression. Alternatively, it may report only the information of specific resources based on a specific threshold. For example, it may report only resources with an accuracy of 90% or higher and / or a time duration (T_valid) of 50ms or more.

[0316] The terminal can perform periodic reporting for resources with a long T_valid and non-periodic reporting for resources with a short duration.

[0317] As an example of a terminal reporting full-port channel information, the terminal can report legacy information and new full-port channel estimation results in a single CSI report.

[0318] Additionally, to increase the accuracy of full-port channel estimation and / or extend the valid time duration, the terminal may transmit some or all of the preferred port configuration relationships, sub-band (SB)(s), and / or partial-port CSI-RS resources / resource groups to the base station. Subsequently, the terminal may receive the corresponding partial-port CSI-RS(s) and use them for model training / update. This information (e.g., preferred port configuration relationships, SB(s), and / or partial-port CSI-RS resources / resource groups) may be included in the CSI report or reported to the base station via a separate PUSCH / PUCCH. In this case, the terminal may expect that the base station will not transmit partial-port CSI-RS(s) that do not satisfy a specific accuracy and / or valid time duration.

[0319] FIG. 18 illustrates the flow of a method performed at a terminal according to one embodiment.

[0320] Referring to FIG. 18, the terminal can receive configuration information for resources of the downlink reference signal (1805).

[0321] Based on the above configuration information, the terminal can measure the downlink reference signal through some of the total antenna ports of the downlink reference signal (1810).

[0322] Based on the results of the above measurements, the terminal can obtain channel information for all antenna ports through the AI / ML (artificial intelligence / machine learning) model of the terminal (1815).

[0323] The terminal can transmit a CSI (channel state information) report based on the channel information obtained above (1820).

[0324] The above resources may include first resources for all antenna ports and second resources for some antenna ports.

[0325] The above setting information may include linkage information that links each second resource with at least one of the first resources.

[0326] The above linkage information can be used as input data for at least one of the training or inference of the above AI / ML model.

[0327] Channel information for all antenna ports measured in the first resources can be used as a Ground Truth Label for training the AI / ML model.

[0328] The AI / ML model can be monitored based on performance indicators for each resource configured on the above-mentioned antenna ports.

[0329] The above performance indicator may be determined based on at least one of information regarding channel estimation accuracy evaluated for each resource and information regarding the time interval during which the channel estimation accuracy is valid.

[0330] Based on the above performance indicators, at least one of the resources to which the downlink reference signal is transmitted and the transmission frequency of the downlink reference signal at each resource can be determined.

[0331] The terminal can transmit at least one of information regarding antenna ports preferred by the terminal among the entire antenna ports and information regarding resources preferred by the terminal for some of the antenna ports.

[0332] The terminal may transmit a terminal capability report. The terminal capability report may include information regarding a combination of the number of maximum antenna ports and the number of minimum antenna ports supported by the terminal for the downlink reference signal.

[0333] The above downlink reference signal may be a CSI-RS (channel state information-reference signal).

[0334] FIG. 19 illustrates the flow of a method performed at a base station according to one embodiment.

[0335] Referring to FIG. 19, the base station can transmit configuration information for resources of the downlink reference signal to the terminal (1905).

[0336] Based on the above configuration information, the base station can transmit the downlink reference signal to the terminal through some of the total antenna ports of the downlink reference signal (1910).

[0337] The base station may receive a CSI (channel state information) report from the terminal (1915). The CSI report may include channel information for all antenna ports. The channel information for all antenna ports may be estimated by the terminal's AI / ML (artificial intelligence / machine learning) model based on the downlink reference signal transmitted through some antenna ports.

[0338] The above resources may include first resources for all antenna ports and second resources for some antenna ports.

[0339] The above setting information may include linkage information that links each second resource with at least one of the first resources.

[0340] The above linkage information can be used as input data for at least one of the training or inference of the AI / ML model of the terminal.

[0341] The AI / ML model can be monitored based on performance indicators for each resource configured on the above-mentioned antenna ports.

[0342] The above performance indicator may be determined based on at least one of information regarding channel estimation accuracy evaluated for each resource and information regarding the time interval during which the channel estimation accuracy is valid.

[0343] Based on the above performance indicators, at least one of the resources to which the downlink reference signal is transmitted and the transmission frequency of the downlink reference signal at each resource can be determined.

[0344] The base station can receive at least one of information regarding antenna ports preferred by the terminal among the entire antenna ports and information regarding resources preferred by the terminal for some of the antenna ports.

[0345] The base station may receive a terminal capability report. The terminal capability report may include information regarding a combination of the number of maximum antenna ports and the number of minimum antenna ports supported by the terminal for the downlink reference signal.

[0346] The above downlink reference signal may be a CSI-RS (channel state information-reference signal).

[0347] FIG. 20 illustrates a communication system (1) applicable to the present disclosure.

[0348] Referring to FIG. 20, the communication system (1) includes a wireless device, a base station, and a network. Here, the wireless device refers to a device that performs communication using wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution)) and may be referred to as a communication / wireless / 5G device. Although not limited thereto, the wireless device may include a robot (100a), a vehicle (100b-1, 100b-2), an XR (eXtended Reality) device (100c), a hand-held device (100d), a home appliance (100e), an IoT (Internet of Thing) device (100f), and an AI device / server (400). For example, the vehicle may include a vehicle equipped with wireless communication capabilities, an autonomous vehicle, a vehicle capable of performing inter-vehicle communication, etc. Here, the vehicle may include an Unmanned Aerial Vehicle (UAV) (e.g., a drone). XR devices include AR (Augmented Reality) / VR (Virtual Reality) / MR (Mixed Reality) devices and can be implemented in the form of HMDs (Head-Mounted Devices), HUDs (Head-Up Displays) equipped in vehicles, televisions, smartphones, computers, wearable devices, home appliances, digital signage, vehicles, robots, etc. Portable devices may include smartphones, smartpads, wearable devices (e.g., smartwatches, smart glasses), computers (e.g., laptops, etc.). Home appliances may include TVs, refrigerators, washing machines, etc. IoT devices may include sensors, smart meters, etc. For example, base stations and networks may be implemented as wireless devices, and a specific wireless device (200a) may operate as a base station / network node to other wireless devices.

[0349] Wireless devices (100a to 100f) can be connected to a network (300) through a base station (200). Artificial Intelligence (AI) technology may be applied to the wireless devices (100a to 100f), and the wireless devices (100a to 100f) can be connected to an AI server (400) through the network (300). The network (300) can be configured using a 3G network, a 4G (e.g., LTE) network, or a 5G (e.g., NR) network. The wireless devices (100a to 100f) may communicate with each other through the base station (200) / network (300), but they may also communicate directly (e.g., sidelink communication) without going through the base station / network. For example, vehicles (100b-1, 100b-2) can communicate directly (e.g., V2V (Vehicle to Vehicle) / V2X (Vehicle to everything) communication). Also, IoT devices (e.g., sensors) can communicate directly with other IoT devices (e.g., sensors) or other wireless devices (100a to 100f).

[0350] Wireless communication / connection (150a, 150b, 150c) can be established between wireless devices (100a~100f) / base station (200) and base station (200) / base station (200). Here, wireless communication / connection can be achieved through various wireless access technologies (e.g., 5G NR), such as uplink / downlink communication (150a), sidelink communication (150b) (or D2D communication), and inter-base station communication (150c) (e.g., relay, IAB (Integrated Access Backhaul)). Through wireless communication / connection (150a, 150b, 150c), wireless devices and base stations / wireless devices, and base stations and base stations can transmit / receive wireless signals to / from each other. For example, wireless communication / connection (150a, 150b, 150c) can transmit / receive signals through various physical channels. To this end, based on various proposals of the present disclosure, at least some of the following may be performed: various configuration information setting processes for transmitting / receiving wireless signals, various signal processing processes (e.g., channel encoding / decoding, modulation / demodulation, resource mapping / demapping, etc.), resource allocation processes, etc.

[0351] FIG. 21 illustrates a wireless device that can be applied to the present disclosure.

[0352] Referring to FIG. 21, the first wireless device (100) and the second wireless device (200) can transmit and receive wireless signals through various wireless access technologies (e.g., LTE, NR). Here, {the first wireless device (100), the second wireless device (200)} may correspond to {wireless device (100x), base station (200)} and / or {wireless device (100x), wireless device (100x)} of FIG. 20.

[0353] The first wireless device (100) includes one or more processors (102) and one or more memories (104), and may additionally include one or more transceivers (106) and / or one or more antennas (108). The processor (102) controls the memory (104) and / or transceivers (106) and may be configured to implement the descriptions, functions, procedures, proposals, methods and / or flowcharts of operation disclosed in this document. For example, the processor (102) may process information within the memory (104) to generate a first information / signal and then transmit a wireless signal containing the first information / signal through the transceiver (106). Additionally, the processor (102) may receive a wireless signal containing a second information / signal through the transceiver (106) and then store information obtained from the signal processing of the second information / signal in the memory (104). The memory (104) may be connected to the processor (102) and may store various information related to the operation of the processor (102). For example, the memory (104) may store software code containing instructions for performing some or all of the processes controlled by the processor (102) or for performing the descriptions, functions, procedures, proposals, methods, and / or operation sequence diagrams disclosed in this document. Here, the processor (102) and the memory (104) may be part of a communication modem / circuit / chip designed to implement wireless communication technology (e.g., LTE, NR). The transceiver (106) may be connected to the processor (102) and may transmit and / or receive wireless signals through one or more antennas (108). The transceiver (106) may include a transmitter and / or receiver. The transceiver (106) may be combined with an RF (Radio Frequency) unit. In the present disclosure, a wireless device may refer to a communication modem / circuit / chip.

[0354] The second wireless device (200) includes one or more processors (202) and one or more memories (204), and may additionally include one or more transceivers (206) and / or one or more antennas (208). The processor (202) controls the memory (204) and / or transceivers (206) and may be configured to implement the descriptions, functions, procedures, proposals, methods and / or operation sequences disclosed in this document. For example, the processor (202) may process information within the memory (204) to generate a third information / signal and then transmit a wireless signal containing the third information / signal through the transceiver (206). Additionally, the processor (202) may receive a wireless signal containing a fourth information / signal through the transceiver (206) and then store information obtained from the signal processing of the fourth information / signal in the memory (204). Memory (204) may be connected to the processor (202) and may store various information related to the operation of the processor (202). For example, memory (204) may store software code containing instructions for performing some or all of the processes controlled by the processor (202) or for performing the descriptions, functions, procedures, proposals, methods, and / or flowcharts of operation disclosed in this document. Here, the processor (202) and memory (204) may be part of a communication modem / circuit / chip designed to implement wireless communication technology (e.g., LTE, NR). A transceiver (206) may be connected to the processor (202) and may transmit and / or receive wireless signals through one or more antennas (208). The transceiver (206) may include a transmitter and / or receiver. The transceiver (206) may be interchangeable with an RF unit. In this disclosure, a wireless device may refer to a communication modem / circuit / chip.

[0355] Hereinafter, hardware elements of the wireless device (100, 200) will be described in more detail. Although not limited thereto, one or more protocol layers may be implemented by one or more processors (102, 202). For example, one or more processors (102, 202) may implement one or more layers (e.g., functional layers such as PHY, MAC, RLC, PDCP, RRC, SDAP). One or more processors (102, 202) may generate one or more Protocol Data Units (PDUs) and / or Service Data Units (SDUs) according to the descriptions, functions, procedures, proposals, methods, and / or flowcharts of operation disclosed in this document. One or more processors (102, 202) may generate messages, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or flowcharts of operation disclosed in this document. One or more processors (102, 202) may generate a signal (e.g., baseband signal) containing a PDU, SDU, message, control information, data, or information according to the functions, procedures, proposals, and / or methods disclosed in this document and provide it to one or more transceivers (106, 206). One or more processors (102, 202) may receive a signal (e.g., baseband signal) from one or more transceivers (106, 206) and may obtain a PDU, SDU, message, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or flowcharts disclosed in this document.

[0356] One or more processors (102, 202) may be referred to as a controller, microcontroller, microprocessor, or microcomputer. One or more processors (102, 202) may be implemented by hardware, firmware, software, or a combination thereof. For example, one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), one or more Digital Signal Processing Devices (DSPDs), one or more Programmable Logic Devices (PLDs), or one or more Field Programmable Gate Arrays (FPGAs) may be included in one or more processors (102, 202). The descriptions, functions, procedures, proposals, methods, and / or flowcharts disclosed in this document may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, etc. Firmware or software configured to perform the descriptions, functions, procedures, proposals, methods, and / or operation sequences disclosed in this document may be contained in one or more processors (102, 202) or stored in one or more memories (104, 204) and driven by one or more processors (102, 202). The descriptions, functions, procedures, proposals, methods, and / or operation sequences disclosed in this document may be implemented using firmware or software in the form of code, instructions, and / or sets of instructions.

[0357] One or more memories (104, 204) may be connected to one or more processors (102, 202) and may store various forms of data, signals, messages, information, programs, code, instructions, and / or commands. One or more memories (104, 204) may be composed of ROM, RAM, EPROM, flash memory, hard drive, registers, cache memory, computer read storage media, and / or combinations thereof. One or more memories (104, 204) may be located inside and / or outside of one or more processors (102, 202). Additionally, one or more memories (104, 204) may be connected to one or more processors (102, 202) through various technologies such as wired or wireless connections.

[0358] One or more transceivers (106, 206) may transmit user data, control information, wireless signals / channels, etc., as mentioned in the methods and / or operation flowcharts, etc., of this document to one or more other devices. One or more transceivers (106, 206) may receive user data, control information, wireless signals / channels, etc., as mentioned in the descriptions, functions, procedures, proposals, methods and / or operation flowcharts, etc., disclosed in this document from one or more other devices. For example, one or more transceivers (106, 206) may be connected to one or more processors (102, 202) and may transmit and receive wireless signals. For example, one or more processors (102, 202) may control one or more transceivers (106, 206) to transmit user data, control information, or wireless signals to one or more other devices. Additionally, one or more processors (102, 202) may control one or more transceivers (106, 206) to receive user data, control information, or wireless signals from one or more other devices. Additionally, one or more transceivers (106, 206) may be connected to one or more antennas (108, 208), and one or more transceivers (106, 206) may be configured to transmit and receive user data, control information, wireless signals / channels, etc., as described in the descriptions, functions, procedures, proposals, methods, and / or flowcharts of operation disclosed in this document through one or more antennas (108, 208). In this document, one or more antennas may be multiple physical antennas or multiple logical antennas (e.g., antenna ports). One or more transceivers (106, 206) can convert the received wireless signal / channel, etc. from an RF band signal to a baseband signal in order to process the received user data, control information, wireless signal / channel, etc. using one or more processors (102, 202).One or more transceivers (106, 206) can convert user data, control information, wireless signals / channels, etc. processed using one or more processors (102, 202) from baseband signals to RF band signals. To this end, one or more transceivers (106, 206) may include (analog) oscillators and / or filters.

[0359] FIG. 22 illustrates another example of a wireless device to which the present disclosure applies. The wireless device may be implemented in various forms depending on the use—example / service (see FIG. 20).

[0360] Referring to FIG. 22, the wireless device (100, 200) corresponds to the wireless device (100, 200) of FIG. 21 and may be composed of various elements, components, units / parts, and / or modules. For example, the wireless device (100, 200) may include a communication unit (110), a control unit (120), a memory unit (130), and additional elements (140). The communication unit may include a communication circuit (112) and transceiver(s) (114). For example, the communication circuit (112) may include one or more processors (102, 202) and / or one or more memories (104, 204) of FIG. 21. For example, the transceiver(s) (114) may include one or more transceivers (106, 206) and / or one or more antennas (108, 208) of FIG. 21. The control unit (120) is electrically connected to the communication unit (110), the memory unit (130), and additional elements (140) and controls the general operation of the wireless device. For example, the control unit (120) may control the electrical / mechanical operation of the wireless device based on a program / code / command / information stored in the memory unit (130). Additionally, the control unit (120) may transmit information stored in the memory unit (130) to an external (e.g., another communication device) via a wireless / wired interface through the communication unit (110), or store information received from an external (e.g., another communication device) via a wireless / wired interface through the communication unit (110) in the memory unit (130).

[0361] The additional element (140) can be configured in various ways depending on the type of wireless device. For example, the additional element (140) may include at least one of a power unit / battery, an input / output unit (I / O unit), a driving unit, and a computing unit. Although not limited thereto, the wireless device may be implemented in the form of a robot (Fig. 20, 100a), a vehicle (Fig. 20, 100b-1, 100b-2), an XR device (Fig. 20, 100c), a portable device (Fig. 20, 100d), a home appliance (Fig. 20, 100e), an IoT device (Fig. 20, 100f), a digital broadcasting terminal, a hologram device, a public safety device, an MTC device, a medical device, a fintech device (or financial device), a security device, a climate / environment device, an AI server / device (Fig. 20, 400), a base station (Fig. 20, 200), a network node, etc. Wireless devices can be used in a movable or fixed location depending on the use—e.g., service.

[0362] In FIG. 22, various elements, components, units / parts, and / or modules within the wireless device (100, 200) may be entirely interconnected via a wired interface, or at least a portion may be wirelessly connected via a communication unit (110). For example, within the wireless device (100, 200), the control unit (120) and the communication unit (110) may be wired, and the control unit (120) and the first unit (e.g., 130, 140) may be wirelessly connected via the communication unit (110). Additionally, each element, component, unit / part, and / or module within the wireless device (100, 200) may include one or more additional elements. For example, the control unit (120) may be composed of one or more sets of processors. For example, the control unit (120) may be composed of a set of a communication control processor, an application processor, an Electronic Control Unit (ECU), a graphics processing processor, a memory control processor, etc. As another example, the memory unit (130) may be composed of RAM (Random Access Memory), DRAM (Dynamic RAM), ROM (Read Only Memory), flash memory, volatile memory, non-volatile memory and / or a combination thereof.

[0363] FIG. 23 illustrates a vehicle or autonomous vehicle to which the present disclosure applies. The vehicle or autonomous vehicle may be implemented as a mobile robot, vehicle, train, manned / unmanned aerial vehicle (AV), ship, etc.

[0364] Referring to FIG. 23, a vehicle or autonomous vehicle (100) may include an antenna unit (108), a communication unit (110), a control unit (120), a driving unit (140a), a power supply unit (140b), a sensor unit (140c), and an autonomous driving unit (140d). The antenna unit (108) may be configured as part of the communication unit (110). Blocks 110 / 130 / 140a to 140d each correspond to blocks 110 / 130 / 140 of FIG. 22.

[0365] The communication unit (110) can transmit and receive signals (e.g., data, control signals, etc.) with external devices such as other vehicles, base stations (e.g., base stations, roadside base stations (Roadside unit), etc.), and servers. The control unit (120) can 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 driving unit (140a) can drive the vehicle or autonomous vehicle (100) on the ground. The driving unit (140a) may include an engine, motor, power train, wheels, brakes, steering device, etc. The power supply unit (140b) supplies power to the vehicle or autonomous vehicle (100) and may include wired / wireless charging circuits, batteries, etc. The sensor unit (140c) can obtain vehicle status, surrounding environment information, user information, etc. The sensor unit (140c) may include an IMU (inertial measurement unit) sensor, a collision sensor, a wheel sensor, a speed sensor, an inclination sensor, a weight detection sensor, a heading sensor, a position module, a vehicle forward / reverse sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor, a temperature sensor, a humidity sensor, an ultrasonic sensor, an illuminance sensor, a pedal position sensor, etc. The autonomous driving unit (140d) may implement technologies such as maintaining the driving lane, technologies for automatically adjusting speed such as adaptive cruise control, technologies for automatically driving along a predetermined path, and technologies for automatically setting a path and driving when a destination is set.

[0366] For example, the communication unit (110) can receive map data, traffic information data, etc. from an external server. The autonomous driving unit (140d) can generate an autonomous driving path and a driving plan based on the acquired data. The control unit (120) can control the drive unit (140a) so that the vehicle or the autonomous vehicle (100) moves along the autonomous driving path according to the driving plan (e.g., speed / direction control). During autonomous driving, the communication unit (110) can acquire the latest traffic information data from an external server non-periodically and can acquire surrounding traffic information data from surrounding vehicles. Additionally, during autonomous driving, the sensor unit (140c) can acquire vehicle status and surrounding environment information. The autonomous driving unit (140d) can update the autonomous driving path and the driving plan based on the newly acquired data / information. The communication unit (110) can transmit information regarding the vehicle location, autonomous driving path, driving plan, etc. to an external server. An external server can predict traffic information data in advance using AI technology, etc., based on information collected from vehicles or autonomous vehicles, and can provide the predicted traffic information data to vehicles or autonomous vehicles.

[0367] The embodiments described above are combinations of the components and features of the present disclosure in a specific form. Each component or feature should be considered optional unless otherwise explicitly stated. Each component or feature may be implemented in a form not combined with other components or features. Additionally, it is possible to construct embodiments of the present disclosure by combining some components and / or features. The order of operations described in the embodiments of the present disclosure may be changed. Some components or features of one embodiment may be included in another embodiment, or may be replaced with corresponding components or features of another embodiment. It is obvious that embodiments may be constructed by combining claims that are not explicitly related in the claims, or that they may be included as new claims by amendment after filing.

[0368] It is obvious to those skilled in the art that the present invention may be embodied in other specific forms without departing from the features. Accordingly, the foregoing detailed description should not be interpreted restrictively in all respects but should be considered exemplary. The scope of the invention shall be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the invention are included within the scope of the invention.

[0369] The present disclosure may be used in a terminal, base station, or other equipment of a wireless mobile communication system.

Claims

1. In a method performed by a terminal, Receive configuration information for resources of the downlink reference signal; Based on the above setting information, the downlink reference signal is measured through some of the total antenna ports of the downlink reference signal; Based on the results of the above measurements, channel information for all antenna ports is obtained through the AI / ML (artificial intelligence / machine learning) model of the terminal; and A method comprising transmitting a CSI (channel state information) report based on the above-mentioned acquired channel information.

2. In Paragraph 1, The above resources include first resources for the entire antenna ports and second resources for some antenna ports, and The above setting information includes linkage information that links each second resource with at least one of the first resources.

3. In Paragraph 2, A method in which the above-mentioned linkage information is used as input data for at least one of training or inference of the above-mentioned AI / ML model.

4. In Paragraph 2, A method in which channel information for all antenna ports measured at the first resources is used as a Ground Truth Label for training the AI / ML model.

5. In Paragraph 1, The AI / ML model is monitored based on performance indicators for each resource configured on the above-mentioned antenna ports, and A method in which the above performance indicator is determined based on at least one of information regarding channel estimation accuracy evaluated for each resource and information regarding the time interval in which the channel estimation accuracy is valid.

6. In Paragraph 5, A method in which at least one of the resources to which the downlink reference signal is transmitted and the transmission frequency of the downlink reference signal at each resource is determined based on the above performance indicators.

7. In Paragraph 1, A method further comprising transmitting at least one of information regarding antenna ports preferred by the terminal among the entire antenna ports and information regarding resources preferred by the terminal for some of the antenna ports.

8. In Paragraph 1, It further includes transmitting terminal capability reports, A method in which the above terminal capability report includes information on a combination of the number of maximum antenna ports and the number of minimum antenna ports supported at the terminal for the downlink reference signal.

9. In Paragraph 1, A method in which the above downlink reference signal is a CSI-RS (channel state information-reference signal).

10. A computer-readable non-transitory recording medium storing a program for performing the method described in claim 1.

11. Regarding the device, Memory for storing instructions; and A processor that operates by executing the above instructions, comprising The operation of the above processor is, Receive configuration information for resources of the downlink reference signal; Based on the above setting information, the downlink reference signal is measured through some of the total antenna ports of the downlink reference signal; Based on the results of the above measurements, channel information for all antenna ports is obtained through the AI / ML (artificial intelligence / machine learning) model of the device; and A device comprising transmitting a CSI (channel state information) report based on the above-mentioned acquired channel information.

12. In Paragraph 11, It further includes a transceiver that transmits or receives a wireless signal under the control of the above processor, and The above device is a device that is a terminal operating in a wireless communication system.

13. In Paragraph 11, The above device is a processing device configured to control a terminal operating in a wireless communication system.

14. In a method performed by a base station, Transmit configuration information for resources of the downlink reference signal to the terminal; Based on the above setting information, the downlink reference signal is transmitted to the terminal through some of the total antenna ports of the downlink reference signal; and It includes receiving a CSI (channel state information) report from the above terminal, and The above CSI report includes channel information for all the above antenna ports, and A method in which channel information for all of the above antenna ports is estimated by an AI / ML (artificial intelligence / machine learning) model of the terminal based on the downlink reference signal transmitted through some of the above antenna ports.

15. Regarding base stations, Memory for storing instructions; and A processor that operates by executing the above instructions, comprising The operation of the above processor is, Transmit configuration information for resources of the downlink reference signal to the terminal; Based on the above setting information, the downlink reference signal is transmitted to the terminal through some of the total antenna ports of the downlink reference signal; and It includes receiving a CSI (channel state information) report from the above terminal, and The above CSI report includes channel information for all the above antenna ports, and A base station, wherein channel information for all of the above antenna ports is estimated by an AI / ML (artificial intelligence / machine learning) model of the terminal based on the downlink reference signal transmitted through some of the above antenna ports.

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