Method carried out by terminal or network in wireless communication system, and apparatus therefor

Abstract

A terminal according to an embodiment of the present disclosure may: receive information about a compressed uplink precoder from a base station; obtain an uplink precoder reconstructed through inference conducted by the terminal on the basis of the information about the compressed uplink precoder; and transmit at least one uplink signal on the basis of the reconstructed uplink precoder, wherein the at least one uplink signal may include at least one of (i) a report on the reconstructed uplink precoder, (ii) a reference signal precoded on the basis of the reconstructed uplink precoder, or (iii) information related to performance monitoring of uplink precoder compression-reconstruction.

Description

A method performed by a terminal or network in a wireless communication system and an apparatus for the same

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

[0002] The 5G mobile communication system is a successor technology to LTE (Long Term Evolution) and is a new clean-slate type of mobile communication system characterized by high performance, low latency, and high availability. In the case of 5G NR, all available spectrum resources can be utilized, ranging from low-frequency bands below 1 GHz to intermediate frequency bands between 1 GHz and 10 GHz, and high-frequency (millimeter wave) bands above 24 GHz. Based on the underlying technology of 5G mobile communication, 6G mobile communication systems are being developed.

[0003] The introduction of AI / ML is being discussed in 3GPP standardization, with use cases including CSI improvement, beam management, and positioning performance enhancement. CSI improvement includes methods to reduce signaling overhead through CSI compression and is based on a two-sided model between the terminal and the base station. Other use cases are based on a one-sided model where the model is configured on only one side, either the terminal or the base station.

[0004] Meanwhile, in the current NR, the UL precoder is indicated to TPMI through the precoding information field of the UL grant DCI. However, due to the size constraint of the precoding information field, the UL precoder at the SB (sub band) level cannot be indicated and is limited to being indicated at the WB (wide band) level.

[0005] The technical problem to be solved by the present disclosure is to provide a method for efficiently performing a wireless signal transmission and reception process and an apparatus for doing so. For example, a method for compressing and indicating information about a UL precoder in a next-generation wireless communication system is provided. As a specific example, a method for monitoring terminal / network operation and performance based on a two-sided model is proposed, which includes a base station encoder model that provides information about a UL precoder based on AI / ML and a terminal encoder model that receives and restores this information.

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

[0007] A method performed by a terminal according to one aspect of the present disclosure comprises receiving information about a compressed uplink precoder from a base station; obtaining a reconstructed uplink precoder through inference by the terminal based on the information about the compressed uplink precoder; and transmitting at least one uplink signal based on the reconstructed uplink precoder, wherein the at least one uplink signal may include at least one of (i) a report about the reconstructed uplink precoder, (ii) a reference signal precoded based on the reconstructed uplink precoder, or (iii) information related to monitoring the performance of uplink precoder compression-reconstruction.

[0008] The report on the restored uplink precoder may include an index related to the restored uplink precoder determined based on the downlink or uplink codebook.

[0009] The report on the restored uplink precoder may include information regarding the time of reception of information on the compressed uplink precoder.

[0010] The above-mentioned precoded reference signal may include a sounding reference signal (SRS) precoded based on the above-mentioned restored uplink precoder.

[0011] The above-mentioned precoded reference signal is transmitted through a 1-port, and the number N of 1-port reference signal resources can be set to 1 or a maximum value.

[0012] The above-mentioned precoded reference signal is transmitted through one layer, and the one layer may be the first layer or a specific layer selected by the terminal.

[0013] The information related to the performance monitoring of the uplink precoder compression-recovery described above may include information on monitoring metrics calculated based on the recovered uplink precoder and reference precoder.

[0014] The above reference precoder can be obtained based on information regarding the Ground Truth Precoder received from the base station.

[0015] Information regarding the above-mentioned compressed uplink precoder can be received through DCI (downlink control information).

[0016] The reasoning of the above terminal can be performed based on an AI (artificial intelligence) model or functionality.

[0017] 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.

[0018] An apparatus according to another aspect of the present disclosure comprises: at least one processor; and at least one memory configured to store instructions that are executed by the at least one processor to cause the at least one processor to perform operations, wherein the operations of the processor may include receiving information about a compressed uplink precoder from a base station; obtaining a reconstructed uplink precoder through inference of the apparatus based on the information about the compressed uplink precoder; and transmitting at least one uplink signal based on the reconstructed uplink precoder. The at least one uplink signal may include at least one of (i) a report about the reconstructed uplink precoder, (ii) a reference signal precoded based on the reconstructed uplink precoder, or (iii) information related to performance monitoring of uplink precoder compression-reconstruction.

[0019] The above device may further include a transmitter and receiver.

[0020] The above device may be a terminal operating in a wireless communication system.

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

[0022] According to another aspect of the present disclosure, a method performed by a base station comprises: generating information about a compressed uplink precoder through inference of the base station; transmitting the information about the compressed uplink precoder to a terminal; and receiving at least one uplink signal from the terminal, wherein the at least one uplink signal may include at least one of (i) a report about an uplink precoder restored by the terminal, (ii) a reference signal precoded based on the restored uplink precoder, or (iii) information related to monitoring the performance of uplink precoder compression-restoration.

[0023] A base station according to another aspect of the present disclosure comprises: at least one processor; and at least one memory configured to store instructions that are executed by the at least one processor to cause the at least one processor to perform operations, wherein the operations of the processor include generating information about a compressed uplink precoder through inference of the base station; transmitting information about the compressed uplink precoder to a terminal; and receiving at least one uplink signal from the terminal, wherein the at least one uplink signal may include at least one of (i) a report about an uplink precoder restored by the terminal, (ii) a reference signal precoded based on the restored uplink precoder, or (iii) information related to performance monitoring of uplink precoder compression-restoration.

[0024] According to the present disclosure, signal transmission and reception can be performed efficiently in a wireless communication system. According to one embodiment, signaling overhead can be reduced by compressing and indicating information regarding the UL precoder. In addition, since information regarding the SB-level UL precoder is indicated based on AI / ML, more accurate frequency domain precoding can be performed, thereby improving system yield. Furthermore, through performance monitoring, life cycle management (LCM) for a two-sided model operating as an encoder / decoder for UL precoding can be supported.

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

[0026] FIG. 1 illustrates an exemplary flexible network topology to which some of the examples of the present specification may be applied.

[0027] FIG. 2 illustrates an example of a communication system applicable to the present disclosure.

[0028] FIG. 3 illustrates an example of a wireless device that can be applied to the present disclosure.

[0029] FIG. 4 illustrates a communication procedure between a first node (e.g., a terminal) and a second node (e.g., a base station) applicable to the present disclosure.

[0030] Figure 5 illustrates a general functional architecture for an AI / ML model.

[0031] FIG. 6 illustrates a communication procedure between a first node (e.g., terminal) and a second node (e.g., base station) to which an AI / ML model is applied.

[0032] FIG. 7 shows an electromagnetic spectrum according to one embodiment of the present disclosure.

[0033] FIG. 8 illustrates an example of a procedure for transmitting system information for THz communication to which the present disclosure applies.

[0034] FIG. 9 illustrates a beam management procedure applicable to the present disclosure.

[0035] FIG. 10 shows an example of a sensing operation according to one embodiment of the present disclosure.

[0036] FIG. 11 illustrates a time / frequency resource for a sensing operation according to one embodiment of the present specification.

[0037] FIG. 12 illustrates a procedure related to a sensing operation according to one embodiment of the present specification.

[0038] FIG. 13 illustrates a two-sided model configuration according to one embodiment.

[0039] FIG. 14 is a diagram illustrating the operation of a terminal and a base station according to one embodiment.

[0040] FIG. 15 illustrates the flow of a method performed by a terminal according to one embodiment.

[0041] FIG. 16 illustrates the flow of a method performed by a base station according to one embodiment.

[0042] In this specification, "A or B" may mean "only A," "only B," or "both A and B." Alternatively, in this specification, "A or B" may be interpreted as "A and / or B." For example, in this specification, "A, B or C" may mean "only A," "only B," "only C," or "any combination of A, B and C."

[0043] A slash ( / ) or a comma used in this specification may mean "and / or." For example, "A / B" may mean "A and / or B." Accordingly, "A / B" may mean "only A," "only B," or "both A and B." For example, "A, B, C" may mean "A, B or C."

[0044] In this specification, "at least one of A and B" may mean "only A," "only B," or "both A and B." Additionally, in this specification, the expressions "at least one of A or B" or "at least one of A and / or B" may be interpreted as synonymous with "at least one of A and B."

[0045] Additionally, in this specification, "at least one of A, B and C" may mean "only A," "only B," "only C," or "any combination of A, B and C." Also, "at least one of A, B or C" or "at least one of A, B and / or C" may mean "at least one of A, B and C."

[0046] Additionally, parentheses used in this specification may mean "for example." Specifically, when indicated as "control information (ABC)," "ABC" may be described as an example of "control information." For example, "control information" may include DEF as another example. In other words, "control information" in this specification is not limited to "ABC," and "ABC" may be described as an example of "control information." Also, when indicated as "control information (i.e., ABC)," "ABC" may be described as an example of "control information."

[0047] In addition, terms such as "first," "second," etc. in this specification are used solely for the purpose of distinguishing one component from another and are not used to limit the components, nor are they used to limit the order or importance of the components unless specifically limited. Accordingly, a first component in one embodiment of this specification may be referred to as a second component in another embodiment, and likewise, a second component in one embodiment may be referred to as a first component in another embodiment.

[0048] In the following explanation, 'when, if, in case of' can be replaced with 'based on'.

[0049] Technical features described individually within a single drawing in this specification may be implemented individually or simultaneously.

[0050] In this specification, a terminal is a user-side device (user equipment, UE) or a consumer-side device, and may also be referred to as a first node that receives / transmits signals from / to a base station / second node / IAB node / Transmission-Reception Point (TRP). A terminal may correspond to a physical node or a logical node. A terminal may correspond to a user-side endpoint or an intermediate point between other endpoints. In communication between two points not limited to endpoints (including one-to-one / many-to-one / one-to-many / many-to-many communication), a terminal may correspond to a served node. A terminal may be a fixed-location node or a non-fixed-location (or mobile) node.

[0051] In this specification, a Base Station (BS) is a device on the network side and may also be referred to as a second node / IAB node / x-NodeB (x-NodeB, where x may be an abbreviation related to Radio Access Technology (RAT)) / Transmission-Reception Point (TRP). A Base Station may correspond to a physical node or a logical node. A Base Station may correspond to an endpoint on the network side or an intermediate point between other endpoints. In communication between two points not limited to endpoints (including one-to-one / many-to-one / one-to-many / many-to-many communication), a Base Station may correspond to a serving node. A Base Station may be a node with a fixed location or a node with an indefinite location.

[0052] In this specification, higher layer parameters may be set for the terminal, pre-set, or pre-defined. For example, a base station may transmit higher layer parameters to the terminal. For example, the terminal may transmit parameters such as capability to the base station as higher layer parameters. For example, higher layer parameters may be transmitted via RRC (radio resource control) signaling or MAC (medium access control) signaling.

[0053] In this specification, information / state / parameters being "configured" or "pre-configured" may be interpreted as the information / state / parameters being provided / pre-provided to the terminal through pre-defined signaling (e.g., SIB, MAC, RRC) from the base station. In this specification, information / state / parameters being "defined" or "pre-defined" may be interpreted as being known or stored in advance by the base station and the terminal without signaling between the base station and the terminal.

[0054] The technology described in this specification can be used in various wireless communication systems such as CDMA (code division multiple access), FDMA (frequency division multiple access), TDMA (time division multiple access), OFDMA (orthogonal frequency division multiple access), and SC-FDMA (single carrier frequency division multiple access). CDMA can be implemented with wireless technologies such as UTRA (universal terrestrial radio access) or CDMA2000. TDMA can be implemented with wireless technologies such as GSM (global system for mobile communications) / GPRS (general packet radio service) / EDGE (enhanced data rates for GSM evolution). OFDMA can be implemented with wireless technologies such as IEEE (institute of electrical and electronics engineers) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802-20, E-UTRA (evolved UTRA), LTE (long term evolution), and 5G NR.

[0055] The technology described in this specification can be implemented as 6G wireless technology and applied to various 6G systems. For example, 6G systems may have key factors such as eMBB (enhanced mobile broadband), URLLC (ultra-reliable low latency communications), mMTC (massive machine-type communication), AI (artificial intelligence) integrated communication, tactile internet, high throughput, high network capacity, high energy efficiency, low backhaul and access network congestion, and enhanced data security.

[0056] <Symbols, Abbreviations, Terms>

[0057] - ACK: ACKnowledgement

[0058] - AL: Aggregation Level

[0059] - BM: beam management

[0060] - CB: Code Block

[0061] - CBG: Code Block Group

[0062] - CCE: Control Channel Element

[0063] - CQI: channel quality indicator

[0064] - CRI: CSI-RS (channel state information - reference signal) resource indicator

[0065] - CSI: channel state information

[0066] - CSI-IM: channel state information - interference measurement

[0067] - CSI-RS: channel state information - reference signal

[0068] - DCI: Downlink Control Information

[0069] - DL: Downlink

[0070] - DMRS: demodulation reference signal

[0071] - FDM: frequency division multiplexing

[0072] - FFT: fast Fourier transform

[0073] - HARQ: Hybrid ARQ

[0074] - IFDMA: interleaved frequency division multiple access

[0075] - IFFT: inverse fast Fourier transform

[0076] - L1-RSRP: Layer 1 reference signal received power

[0077] - L1-RSRQ: Layer 1 reference signal received quality

[0078] - MAC: medium access control

[0079] - NACK: Negative ACKnowledgement

[0080] - NZP: non-zero power

[0081] - OFDM: orthogonal frequency division multiplexing

[0082] - PDCCH: physical downlink control channel

[0083] - PDSCH: physical downlink shared channel

[0084] - PMI: precoding matrix indicator

[0085] - PRB: physical resource block

[0086] - PUCCH: Physical Uplink Control CHannel

[0087] - PUSCH: Physical Uplink Shared Channel

[0088] - QCL: quasi co-location

[0089] - RB: resource block

[0090] - RE: resource element

[0091] - REG: Resource Element Group

[0092] - RI: Rank indicator

[0093] - RNTI: Radio Network Temporary Identifier

[0094] - RRC: radio resource control

[0095] - RSSI: received signal strength indicator

[0096] - RV: Redundancy Version

[0097] - Rx: Reception

[0098] - SINR: signal to interference and noise ratio

[0099] - SSB (or SS / PBCH block): synchronization signal block (including primary synchronization signal, secondary synchronization signal and physical broadcast channel)

[0100] - TB: Transport Block

[0101] - TDM: time division multiplexing

[0102] - TRP: transmission and reception point

[0103] - TRS: tracking reference signal

[0104] - Tx: transmission

[0105] - UCI: Uplink Control Information

[0106] - UE: user equipment

[0107] - UL: Uplink

[0108] - ZP: zero power

[0109] FIG. 1 illustrates an exemplary flexible network topology to which some of the examples of the present specification may be applied.

[0110] To compensate for incomplete areas of network coverage, a network topology in which the Split Radio Access Network (RAN) is configured more flexibly and resiliently may be considered. To this end, various nodes such as IAB nodes, relays, and RF repeaters, as exemplified in Fig. 1, may be applied, and NTN may be integrated. For example, an IAB node may correspond to a node that provides wireless backhaul. For example, a relay may refer to any intermediate point, and in the case of a sidelink relay where a terminal functions as a relay, it may collectively refer to a terminal-to-network (U2N) relay and a terminal-to-terminal (U2U) relay. For example, an RF repeater may correspond to a node that performs simple signal amplification and forwarding functions, and in the case of a network-controlled repeater, it may adjust transmit / receive settings based on information provided by the network as well as signal amplification and forwarding. For example, an NTN node may correspond to a satellite or aircraft that provides NTN coverage that is difficult for a terrestrial network to provide. In addition to these examples, various intermediate points can be introduced to improve network topology.

[0111] Referring to FIG. 1, a split RAN can support the division of a base station into one centralized unit (CU) and one or more distributed units (DU). The CU and DU may correspond to logical units. The CU may be further divided into a control plane (CP) portion and one or more user plane (UP) portions. Since a failure in the CU-CP affects not only the CU-UP but also the DU, various intermediate points may be introduced to compensate for this.

[0112] An intermediate point may correspond to a terminal or a base station depending on its relative relationship with other nodes. For example, an IAB node may include a mobile-termination (MT) portion and a DU. The MT can connect the IAB node to a donor node. The DU of the IAB node may serve other terminals or connect to other IAB nodes to provide multi-hop wireless backhaul to terminals. In other words, an IAB node may correspond to a base station in its relative relationship with user-side nodes and to a terminal in its relative relationship with network-side nodes.

[0113] In some examples of this specification, the description of a terminal may apply equally to an intermediate point corresponding to a terminal in relation to a network-side endpoint as well as to a user-side endpoint. Similarly, in some examples of this specification, the description of a base station may apply equally to an intermediate point corresponding to a base station in relation to a user-side endpoint as well as to a network-side endpoint. However, in most cases where there is no additional description of the operation of three or more entities, the communication entities in this specification are briefly described by the term terminal and / or base station (or first node and / or second node), wherein the term terminal and / or base station (or first node and / or second node) is interpreted to include or replace any endpoint or any intermediate point in relation to other nodes.

[0114] That is, for the sake of brevity of description in some examples of this specification, the subject of the operation may be referred to as a base station and / or terminal (or a first node and / or a second node). Additionally, the term base station and / or terminal (or a first node and / or a second node) may be interpreted or substituted as in the following examples: for example, the base station (or the first node) and the terminal (or the second node) may correspond to a first endpoint and a second endpoint, respectively; may correspond to an endpoint and an intermediate point, respectively; may correspond to an intermediate point and an endpoint, respectively; or may correspond to a first intermediate point and a second intermediate point, respectively.

[0115] In this specification, there may be no intermediate points between the base station and the terminal, or there may be one or more. If intermediate points exist, the intermediate points may correspond to IAB nodes, relays, RF repeaters, NTN (non-terrestrial network) nodes, or nodes supporting other functions. The intermediate points may be nodes with a fixed location or nodes with an indefinite location.

[0116] FIG. 2 illustrates a communication system applicable to the present disclosure.

[0117] The communication system (100) of FIG. 2 includes a wireless device (110), a network device (120), and a network (130). Here, the wireless device (110) refers to a device that performs communication using wireless access technology (e.g., LTE, LTE-A, LTE-A pro, NR, 5G, 5G-A, 6G) and may be referred to as a communication / wireless / 5G / 6G device. Although not limited thereto, the wireless device (110) may include a robot (110a), a vehicle (110b-1, 110b-2), an XR (extended reality) device (110c), a hand-held device (110d), a home appliance (110e), an IoT (Internet of Thing) device (110f), and an AI (artificial intelligence) device / server (110g). 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 (110b-1, 110b-2) may include an unmanned aerial vehicle (UAV) (e.g., a drone). The XR device (110c) includes an augmented reality (AR) / virtual reality (VR) / mixed reality (MR) device and may be implemented in the form of a head-mounted device (HMD), a head-up display (HUD) equipped in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, digital signage, a vehicle, a robot, etc. The portable device (110d) may include a smartphone, a smartpad, a wearable device (e.g., a smartwatch, smart glasses), a computer (e.g., a laptop, etc.). The home appliance (110e) may include a TV, a refrigerator, a washing machine, etc. The IoT device (110f) may include a sensor, a smart meter, etc. The wireless device (110) may correspond to a terminal (or first node) or an intermediate point.The network device (120) may correspond to a base station (or a second node) or another intermediate point. For example, the network device (120) may also be implemented as a wireless device (110), and a specific wireless device (120a) may operate as a network device (120) to another wireless device (110).

[0118] Wireless devices (110a to 110f) can be connected to a network (130) through a network device (120). AI technology may be applied to the wireless devices (110a to 110f), and the wireless devices (110a to 110f) can be connected to an AI server (110g) through the network (130). The network (130) can be configured using a 3G network, a 4G (e.g., LTE) network, a 5G (e.g., NR) network, or a 6G network. The wireless devices (110a to 110f) may communicate with each other through the network device (120) / network (130), but may also communicate directly (e.g., sidelink communication) without going through the network device (120) / network (130). For example, vehicles (110b-1, 110b-2) can communicate directly (e.g., V2V (vehicle to vehicle) / V2X (vehicle to everything) communication). Also, an IoT device (110f) (e.g., a sensor) can communicate directly with another IoT device (e.g., a sensor) or other wireless devices (110a to 110f).

[0119] Wireless communication / connection (150a, 150b, 150c) can be established between wireless devices (110a to 110f) / network devices (120) and between network devices (120). Here, wireless communication / connection can be established through various wireless access technologies such as uplink / downlink communication (150a), sidelink communication (150b) (or D2D communication), and communication between network devices (150c) (e.g., relay, IAB (integrated access backhaul)). Through wireless communication / connection (150a, 150b, 150c), wireless devices and network devices / wireless devices, and network devices and network devices 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 the various descriptions of the present disclosure, at least some of the following may be performed: a process for setting various configuration information for transmitting / receiving wireless signals, a process for various signal processing (e.g., channel encoding / decoding, modulation / demodulation, resource mapping / demapping, etc.), a resource allocation process, etc.

[0120] FIG. 3 illustrates an example of a wireless device that can be applied to the present disclosure.

[0121] Referring to FIG. 3, the wireless device (200) can transmit and receive wireless signals through various wireless access technologies (e.g., LTE, LTE-A, LTE-A pro, NR, 5G, 5G-A, 6G). The wireless device (200) includes at least one processor (202) and at least one memory (204), and may additionally include at least one transceiver (206) and / or at least one antenna (208).

[0122] The processor (202) controls the memory (204) and / or the transceiver (206) and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or sequences of operation disclosed in this document. For example, the processor (202) may process information within the memory (204) to generate a first information / signal and then transmit a wireless signal containing the first information / signal through the transceiver (206). Additionally, the processor (202) may receive a wireless signal containing a second information / signal through the transceiver (206) and then store information obtained from the signal processing of the second information / signal in the memory (204). The memory (204) may be connected to the processor (202) and may store various information related to the operation of the processor (202). For example, 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 sequences of operations 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. A transceiver (206) may be connected to the processor (202) and may transmit and / or receive wireless signals through at least one antenna (208). The transceiver (206) may include a transmitter and / or receiver. The transceiver (206) may be interchangeable with a radio frequency (RF) unit. In this disclosure, a wireless device may mean a communication modem / circuit / chip.

[0123] Hereinafter, hardware elements of the wireless device (200) will be described in more detail. Although not limited thereto, at least one protocol layer may be implemented by at least one processor (202). For example, at least one processor (202) may implement at least one layer (e.g., functional layers such as PHY (physical), MAC (media access control), RLC (radio link control), PDCP (packet data convergence protocol), RRC (radio resource control), and SDAP (service data adaptation protocol). At least one processor (202) may generate at least one PDU (Protocol Data Unit) and / or at least one SDU (service data unit) according to the descriptions, functions, procedures, proposals, methods and / or operation sequences disclosed in this document. At least one processor (202) may generate messages, control information, data, or information according to the descriptions, functions, procedures, proposals, methods and / or operation sequences disclosed in this document. At least one processor (202) may generate a signal (e.g., baseband signal) including 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 at least one transceiver (206). At least one processor (202) may receive a signal (e.g., baseband signal) from at least one transceiver (206) and may obtain a PDU, SDU, message, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or operation sequences disclosed in this document.

[0124] At least one processor (202) may be referred to as a controller, microcontroller, microprocessor, or microcomputer. At least one processor (202) may be implemented by hardware, firmware, software, or a combination thereof. For example, at least one application-specific integrated circuit (ASIC), at least one digital signal processor (DSP), at least one digital signal processing device (DSPD), at least one programmable logic device (PLD), or at least one field programmable gate array (FPGA) may be included in at least one processor (202). The descriptions, functions, procedures, proposals, methods, and / or operation sequences 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 included in at least one processor (202) or stored in at least one memory (204) and driven by at least one processor (202). The descriptions, functions, procedures, proposals, methods, and / or flowcharts disclosed in this document may be implemented using firmware or software in the form of code, instructions, and / or sets of instructions.

[0125] At least one memory (204) may be connected to at least one processor (202) and may store various forms of data, signals, messages, information, programs, codes, instructions, and / or commands. At least one memory (204) may be composed of ROM (read-only memory), RAM (random access memory), EPROM (erasable programmable read-only memory), flash memory, hard drive, registers, cache memory, computer read storage media, and / or combinations thereof. At least one memory (204) may be located inside and / or outside of at least one processor (202). Additionally, at least one memory (204) may be connected to at least one processor (202) via various technologies, such as wired or wireless connections.

[0126] At least one transceiver (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 at least one other device. At least one transceiver (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 at least one other device. For example, at least one transceiver (206) may be connected to at least one processor (202) and may transmit and receive wireless signals. For example, at least one processor (202) may control at least one transceiver (206) to transmit user data, control information, or wireless signals to at least one other device. Additionally, at least one processor (202) may control at least one transceiver (206) to receive user data, control information, or wireless signals from at least one other device. Additionally, at least one transceiver (206) may be connected to at least one antenna (208), and at least one transceiver (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 operation sequence diagrams disclosed in this document through at least one antenna (208). In this document, at least one antenna may be a plurality of physical antennas or a plurality of logical antennas (e.g., antenna ports). At least one transceiver (206) may convert the received wireless signals / channels, etc., from RF band signals to baseband signals in order to process the received user data, control information, wireless signals / channels, etc., using at least one processor (202). At least one transceiver (206) may convert the processed user data, control information, wireless signals / channels, etc., from baseband signals to RF band signals using at least one processor (202).To this end, at least one transceiver (206) may include an (analog) oscillator and / or filter.

[0127] The components of the wireless device described with reference to FIG. 3 may be referred to by other terms in terms of their function. For example, the processor (202) may be referred to as the control unit, the transceiver (206) as the communication unit, and the memory (204) as the storage unit. In some cases, the communication unit may be used to mean at least a part of the processor (202) and the transceiver (206).

[0128] The structure of the wireless device described with reference to FIG. 3 can be understood as the structure of at least part of various devices. For example, the structure of the wireless device illustrated in FIG. 3 may be at least part of the various devices described with reference to FIG. 2 (e.g., robot (110a), vehicle (110b-1, 110b-2), XR device (110c), portable device (110d), home appliance (110e), IoT device (110f), AI device / server (110g)). Furthermore, according to various embodiments, the device may include other components in addition to the components illustrated in FIG. 3.

[0129] For example, the device may be a portable device such as a smartphone, smartpad, wearable device (e.g., smart watch, smart glasses), or portable computer (e.g., laptop, etc.). In this case, the device may further include at least one of a power supply unit that supplies power and includes a wired / wireless charging circuit, a battery, etc., an interface unit that includes at least one port for connection with another device (e.g., audio input / output port, video input / output port), and an input / output unit for inputting and outputting video information / signals, audio information / signals, data, and / or information input by a user.

[0130] For example, the device may be a mobile device such as a mobile robot, vehicle, train, manned / unmanned aerial vehicle (AV), or ship. In this case, the device may further include at least one of a drive unit comprising at least one of an engine, motor, power train, wheel, brake, and steering device of the device; a power supply unit that supplies power and includes a wired / wireless charging circuit, battery, etc.; a sensor unit that senses state information, environmental information, and user information of the device or its surroundings; an autonomous driving unit that performs functions such as path maintenance, speed control, and destination setting; and a position measurement unit that acquires position information of the moving body through a GPS (global positioning system) and various sensors.

[0131] For example, the device may be an XR device such as an HMD, a HUD (head-up display) equipped in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, digital signage, a vehicle, a robot, etc. In this case, the device may further include at least one of a power supply unit that supplies power and includes a wired / wireless charging circuit, a battery, etc., an input / output unit that acquires control information, data, etc. from the outside and outputs a generated XR object, and a sensor unit that senses state information, environment information, and user information of the device or the surroundings of the device.

[0132] For example, the device may be a robot that can be classified into industrial, medical, household, military, etc., depending on the purpose or field of use. In this case, the device may further include at least one of a sensor unit that senses state information, environmental information, and user information of the device or its surroundings, and a drive unit that performs various physical actions, such as moving robot joints.

[0133] For example, the device may be an AI device such as a TV, projector, smartphone, PC, laptop, digital broadcasting terminal, tablet PC, wearable device, set-top box (STB), radio, washing machine, refrigerator, digital signage, robot, vehicle, etc. In this case, the device may further include at least one of an input unit that acquires various types of data from the outside, an output unit that generates output related to sight, hearing, or touch, a sensor unit that senses state information, environmental information, and user information of the device or its surroundings, and a training unit that learns a model composed of an artificial neural network using training data.

[0134] The structure of the wireless device exemplified in FIG. 3 may be understood as part of a terminal (or first node), or part of an intermediate point, or part of a base station (or second node). If the device exemplified in FIG. 3 is a base station (or second node), the device may further include a wired transceiver for front haul and / or back haul communication. However, if the front haul and / or back haul communication is based on wireless communication, at least one transceiver (206) exemplified in FIG. 3 is used for front haul and / or back haul communication, and the wired transceiver may not be included.

[0135] FIG. 4 illustrates a communication procedure between a first node (e.g., a terminal) and a second node (e.g., a base station) applicable to the present disclosure.

[0136] The second node of FIG. 4 supports dynamic spectrum sharing (DSS) and can provide connectivity to both nodes where 6G technology is implemented and nodes where pre-6G wireless communication technology (e.g., 5G, 4G) is implemented. That is, the first node of FIG. 4 may have 6G technology implemented or pre-6G wireless communication technology (e.g., 5G, 4G) implemented. Additionally, the first node and / or the second node may support full duplex mode as well as non-overlapping full duplex mode.

[0137] In FIG. 4, for the sake of simplicity of explanation, the first node and the second node are assumed to be a terminal and a base station, respectively, and the operation of the terminal (110) and the base station (120) transmitting and / or receiving data, and the operation performed prior to this, are illustrated. However, the operation of FIG. 4 is not limited to the operation between the terminal and the base station, but can be interpreted as the operation between the first node and the second node. Additionally, FIG. 4 illustrates the operation of direct transmission and reception of wireless signals between the terminal (110) and the base station (120), but there may be one or more intermediate points between the terminal (110) and the base station (120), and wireless signals may be transmitted and received via one or more intermediate points.

[0138] Referring to FIG. 4, the terminal (110) and the base station (120) can perform synchronization (401). For example, the terminal (110) performs an initial cell search operation. Specifically, the terminal (110) can detect a synchronization signal for at least one base station connection transmitted from the base station (120) according to a predefined rule. Here, the synchronization signal may include a plurality of synchronization signals classified according to structure or use (e.g., a first synchronization signal (e.g., a primary synchronization signal), a second synchronization signal (e.g., a secondary synchronization signal), etc.). Through this, the terminal (110) can identify the boundary of the unit (e.g., frame, subframe, slot and / or symbol) constituting the wireless signal transmission of the base station (120) and obtain information about the base station (120) (e.g., cell identifier).

[0139] The terminal (110) can obtain system information transmitted from the base station (120) (403). The system information is information related to the attributes, characteristics, and / or capabilities of the base station (120) required to connect to the base station (120) and use the service, and can be classified according to content (e.g., whether it is essential for connection), transmission structure (e.g., channel used, whether it is provided on-demand), etc., and can be classified, for example, into first system information (e.g., MIB (master information block), primary system information), second system information (e.g., SIB (system information block), secondary system information), etc. If necessary, the terminal (110) may transmit a signal requesting system information prior to receiving the system information. However, the request and provision of system information may be performed after the random access procedure described later.

[0140] A terminal (110) and a base station (120) can perform a random access procedure (405). The terminal (110) can transmit and / or receive at least one message for a random access procedure (e.g., random access preamble, RAR (random access response) message, etc.) based on information related to the channel for the random access procedure of the base station (120) obtained through system information (e.g., channel location, channel structure, structure of supported preamble, etc.). For example, the terminal (110) can transmit a first message (e.g., preamble, MSG1) through the channel for the random access procedure, receive a second message (e.g., RAR message, MSG2), transmit a third message (e.g., MSG3) containing information related to the terminal (110) (e.g., identification information) to the base station (120) using scheduling information included in the second message, and receive a fourth message (e.g., MSG4) for contention resolution and / or connection establishment. As another example, the first message and the third message can be transmitted and received as a single message, or the second message and the fourth message can be transmitted and received as a single message.

[0141] The terminal (110) and the base station (120) can perform signaling of control information (407). Here, the control information can be defined in various layers, such as a layer that controls the connection (e.g., a radio resource control (RRC) layer), a layer that handles mapping between logical channels and transmission channels (e.g., a media access control (MAC) layer), and a layer that handles physical channels (e.g., a physical (PHY) layer). For example, the terminal (110) and the base station (120) can perform at least one of signaling to establish a connection, signaling to determine settings related to communication, and signaling to indicate allocated resources.

[0142] The terminal (110) and the base station (120) can transmit and / or receive data (409). In other words, the terminal (110) and the base station (120) can process data based on the signaling of control information and transmit and / or receive data. For example, when transmitting data, the terminal (110) or the base station (120) can perform at least one of channel encoding, rate matching, scrambling, constellation mapping, layer mapping, waveform modulation, antenna mapping, and resource mapping on the information bits. Conversely, when receiving data, the terminal (110) or the base station (120) can perform at least one of signal extraction from resources, antenna-specific waveform demodulation, signal placement considering layer mapping, constellation demapping, descrambling, and channel decoding.

[0143] 6G System Core Technology

[0144] The 6G (wireless communication) system aims for (i) very high data rates per device, (ii) a very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) reduced energy consumption of battery-free IoT (internet of things) devices, (vi) ultra-reliable connectivity, and (vii) connected intelligence with machine learning capabilities. The vision of the 6G system can be four aspects: intelligent connectivity, deep connectivity, holographic connectivity, and ubiquitous connectivity.

[0145] As core implementation technologies for 6G systems, technologies such as artificial intelligence (AI), THz (Terahertz) communication, optical wireless technology, FSO backhaul network, massive MIMO technology, blockchain, 3D networking, quantum communication, unmanned aerial vehicles, cell-free communication, wireless information and energy transfer (WIET), integration of sensing and communication, integration of access backhaul networks, holographic beamforming, big data analysis, and large intelligent surface (LIS) can be adopted.

[0146] artificial intelligence

[0147] The introduction of AI into communications can streamline and enhance real-time data transmission. AI can determine how complex target tasks are performed using numerous analyses. In other words, AI can increase efficiency and reduce processing latency. Time-consuming tasks such as handover, network selection, and resource scheduling can be performed instantly using AI. AI can also play a significant role in M2M, machine-to-human, and human-to-machine communication. Furthermore, AI can enable rapid communication in Brain-Computer Interfaces (BCI). AI-based communication systems can be supported by metamaterials, intelligent structures, intelligent networks, intelligent devices, intelligent cognitive radios, self-sustaining wireless networks, and machine learning.

[0148] The following describes a functional framework for AI / ML operations.

[0149] Below, to provide a more specific explanation of AI (or AI / ML), terms may be defined as follows.

[0150] - Data collection: Data collected from network nodes, management entities, or terminals, serving as a basis for AI model training, data analysis, and inference.

[0151] - 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.

[0152] - 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.

[0153] - AI / ML Inference: A process of making predictions or deriving decisions based on collected data and AI models using trained AI models.

[0154] Life Cycle Management (LCM) procedures for AI / ML models (i.e., model training, model deployment, model inference, model monitoring, model updating, etc.) can be classified into functionality-based LCM and model-based LCM. In functionality-based LCM, AI / ML models may not be identifiable within the network, and the network can direct the activation, deactivation, fallback, or switching of AI / ML functionality. In model-ID (identifier)-based LCM, AI / ML models can be identified within the network, and the network or terminal can activate, deactivate, select, or switch AI / ML models via the model ID.

[0155] Figure 5 illustrates a general functional architecture for an AI / ML model.

[0156] In particular, Figure 5 illustrates a general functional architecture related to both Functionality-based LCM and Model-based LCM. Some functions or some data / information / command flows (i.e., arrows) illustrated in Figure 5 may be omitted.

[0157] Referring to FIG. 5, a general functional framework may be configured to include a data collection function (10), a model training function (20), a management function (30), an inference function (40), and a model storage function (50).

[0158] The Data Collection function (10) is a function that provides input data to the Model Training function (20), Management function (30), and Inference function (40). The Data Collection function (10) performs data preparation based on raw data and can provide input data processed through data preparation. Examples of raw data may include received data / measurement data from terminals or other network entities, inference / output of AI / ML models, etc. The Data Collection function (10) may be performed by a single entity (e.g., terminal, network node, etc.) but may also be performed by multiple entities.

[0159] Here, training data (11) refers to data required as input for the AI / ML model training function (20). monitoring data (12) refers to data required as input for the management (30) of the AI / ML model or AI / ML function. inference data (13) refers to data required as input for the AI / ML inference function (30).

[0160] The Model Training function (20) is a function that performs AI / ML model training, validation, and testing, which can generate model performance metrics that can be used as part of the AI / ML model testing procedure. If necessary, the Model Training function (20) can perform data preparation (e.g., data pre-processing and cleaning, forming and transformation) based on the Training Data (11) delivered from the Data Collection function (10).

[0161] Trained / Updated Model (21): If there is a Model Storage function (50), it is used to transfer trained, validated, and tested AI / ML models to the Model Storage function (50) or to transfer updated versions of the models to the Model Storage function (50).

[0162] The Management function (30) is a function that supervises the operation of an AI / ML model or an AI / ML function. Additionally, the Management function (30) may make decisions to ensure appropriate inference operations based on data received from the Data Collection function (10) (i.e., Monitoring Data (12)) and / or data received from the Inference function (40) (i.e., Inference Output (41)).

[0163] Management Instruction (32) is information required as input to manage the Inference function (40). The relevant information may include the selection / (de)activation / switching of an AI / ML model or an AI / ML-based function, and may also include a fallback to a non-AI / ML operation (i.e., not relying on the inference process).

[0164] A Model Transfer / Delivery Request (33) can be used to request model(s) from Model Storage (50).

[0165] Performance Feedback / Retraining Request (31) refers to information required as input to Model Training function (20) (e.g., for the purpose of retraining or updating the model).

[0166] The inference function (40) is a function that provides output from the process of applying an AI / ML model or AI / ML function using data (i.e., inference data (13)) provided by the data collection (10) as input. Data preparation (e.g., data preprocessing and cleaning, formatting and transformation) may also be performed based on the inference data (13) delivered by the data collection (10). If necessary, the inference function (40) may also perform data preparation (e.g., data pre-processing and cleaning, forming and transformation) based on the inference data (13) provided by the data collection function (10).

[0167] Inference Output (41) is data used in the Management function (30) to monitor the performance of an AI / ML model or AI / ML function. Inference Output (41) may include the inference output of an AI / ML model generated by the Inference function (30), and the details of the inference output may vary depending on the use case.

[0168] The Model Storage function (50) is a function that stores a trained / updated model that can be used to perform the Inference function (40). The Model Storage function (50) exemplified in FIG. 5 can be used as a reference point (if any) applicable to protocol termination, model transmission / delivery, and related processes. Additionally, the Model Storage function (50) is an example and is not intended to restrict the storage location of the actual AI / ML model, and may be omitted.

[0169] Model Transfer / Delivery (51) is used to transfer an AI / ML model to an inference function.

[0170] Cooperation levels can be defined as follows based on the capability of AI / ML functions among multiple nodes, and variations resulting from the combination of multiple levels or the separation of any one level are also possible.

[0171] Cat 0a) No collaboration framework: AI / ML algorithms are based on pure implementation and do not require changes to the wireless interface.

[0172] 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.

[0173] Cat 1) Inter-node support is involved to improve the AI / ML algorithms of each node. For example, this applies when a specific node receives support from other nodes (for training, adaptation, etc.) and vice versa. At this level, model exchange between network nodes is not required.

[0174] Cat 2) Collaborative AI / ML tasks can be performed among multiple nodes. This level requires the exchange of AI / ML model commands or network nodes.

[0175] FIG. 5 is a diagram illustrating an overall functional framework for an AI / ML model, and all functions and / or all data / information / command signals illustrated in FIG. 5 may not be performed within a specific node, and only some may be performed.

[0176] AI / ML models can be classified into one-side models and two-side models depending on whether training and / or inference are performed on a single node or jointly / sequentially on multiple nodes.

[0177] A one-side model can refer to an AI / ML model where inference is performed entirely by a single node (e.g., a terminal or a network). Here, the training of the AI / ML model can also be performed entirely by a single node. The training and inference of the AI / ML model may be performed by the same node, or they may be performed by different nodes.

[0178] A two-side model can refer to an AI / ML model in which joint inference is performed across multiple nodes (e.g., terminals and networks). Joint inference means that inference is performed collaboratively across multiple nodes; for example, the first part of the inference may be performed by the first node, and the remainder by the second node. Two-side models can be classified into various types as follows, depending on the training method of the AI / ML model.

[0179] - First type: An AI / ML model can be trained on a single node. In this case, joint training can be performed. The trained model can then be distributed to other nodes / entities.

[0180] - Second type: Joint training of AI / ML models can be performed on multiple nodes / entities (e.g., networks and terminals). Joint training can mean that model generation (e.g., CSI generation) and model reconstruction (CSI compression by sub-use cases) are trained in the same loop for forward activation and backward gradient. In this type, joint training can include both simultaneous training (i.e., model generation training and model reconstruction training are performed simultaneously) and sequential training (i.e., model reconstruction training is performed after model generation training).

[0181] - Third Type: Separate training of AI / ML models can be performed at multiple nodes (e.g., networks and terminals). Separate training may mean that training starts sequentially at one node and continues at another node. In this case, if the first node performs the AI / ML model first and shares the training data with the second node, the second node can perform the AI / ML model using the shared training data. For example, training for the CSI generation part may be performed by the terminal, while CSI reconstruction may be performed by the network.

[0182] FIG. 6 illustrates a communication procedure between a first node (e.g., terminal) and a second node (e.g., base station) to which an AI / ML model is applied.

[0183] The operations described below may be explained / interpreted based on an AI / ML model as shown in FIG. 6 below, even without separate mention (i.e., without explicit mention of being by / based on / for an AI / ML model). Furthermore, unless specifically limited, the AI / ML model may correspond to a one-side model in which inference is performed entirely by a single node or a two-side model in which joint inference is performed by multiple nodes.

[0184] First signaling (601): In the following description, the signaling (e.g., information / data / channel / signal, etc.) or set of signaling between a specific node (e.g., terminal, network, etc.) and another node may be interpreted as the signaling or set of signaling of the first signaling (601) used to perform an operation based on an AI / ML model, even if not otherwise mentioned. For example, it may correspond to training data for training (i.e., creation and / or reconstruction) of the AI / ML model of FIG. 5, or to inference data used for inference of the AI / ML model, or to feedback for the AI / ML model. If, in this specification, signaling between nodes is not required prior to an operation based on an AI / ML model, the first signaling (601) may be omitted. In this specification, if a one-side model is used, the unidirectional / bidirectional signaling (set) in this specification may correspond to the signaling of the first signaling (601). Additionally, when a two-side model is used in the present specification, unidirectional / bidirectional signaling in the present specification may correspond to the first signaling (601), and repetitive signaling operation may also correspond to the first signaling (601).

[0185] For example, in AI / ML model-based beam management, when a base station predicts (i.e., infers) high-quality beam(s) based on an AI / ML model, the base station can receive quality / intensity information for multiple beams from the terminal. Additionally, when a terminal predicts (i.e., infers) high-quality beam(s) based on an AI / ML model, the terminal can receive multiple beams from the base station.

[0186] AI / ML model-based operation (602): In the following description, an operation (e.g., computation, selection, prediction, etc.) at a specific node (e.g., terminal, network, etc.) or a common operation (e.g., computation, selection, prediction, etc.) at multiple nodes (e.g., terminal, network, etc.) may correspond to an AI / ML model-based operation (602) based on one or more functions in the functional framework of the AI / ML model, even without separate mention. For example, it may correspond to the training (i.e., creation and / or reconstruction) of the AI / ML model of FIG. 5 or to the inference of the AI / ML model. When a one-side model is used, an operation performed by a single node in this specification may correspond to an AI / ML model-based operation (602), and when a two-side model is used, a common operation performed by multiple nodes in this specification may correspond to an AI / ML model-based operation (602).

[0187] For example, in an AI / ML model-based BM, a base station can predict (i.e., infer) high-quality beam(s) based on an AI / ML model by using quality / intensity information for multiple beams received from a terminal as inference data. Additionally, a terminal can measure multiple beams received from a base station and predict (i.e., infer) high-quality beam(s) based on an AI / ML model by using the measurement results as inference data.

[0188] Second signaling (603): In the following description, signaling (e.g., information / data / channel / signal, etc.) or a set of signaling between a specific node (e.g., terminal, network, etc.) and another node may be interpreted as the second signaling (603) or a set of signaling generated as a result of an operation based on an AI / ML model, even without separate mention. For example, it may correspond to the output resulting from the inference of the AI / ML model of FIG. 5. If signaling between nodes is not required as a result of an operation based on an AI / ML model in this specification, the second signaling (603) may be omitted. If a one-side model is used in this specification, the unidirectional / bidirectional signaling (set) in this specification may correspond to the second signaling (603). Additionally, when a two-side model is used in this specification, unidirectional / bidirectional signaling in this specification may correspond to the second signaling (603), and repetitive signaling operation may also correspond to the second signaling (603).

[0189] For example, in an AI / ML model-based BM, the base station may transmit beam(s) predicted based on the AI / ML model as candidates to the terminal so that the terminal can determine the optimal beam. Additionally, the terminal may report the beam(s) predicted based on the AI / ML model to the base station to request the base station to transmit candidate beams as candidates for determining the optimal beam.

[0190] THz communication

[0191] Data transmission rates can be increased by expanding bandwidth. This can be achieved by using sub-THz communication with wide bandwidth and applying advanced large-scale MIMO technology. THz waves, also known as sub-millimeter radiation, generally refer to a frequency band between 0.1 THz and 10 THz with corresponding wavelengths ranging from 0.03 mm to 3 mm. The 100 GHz–300 GHz band range (Sub-THz band) is considered the primary portion of the THz band for cellular communication. Adding the Sub-THz band to the mmWave band increases 6G cellular communication capacity. Among the defined THz bands, the 300 GHz–3 THz band is located in the far-infrared (IR) frequency band. Although the 300 GHz–3 THz band is part of the broadband, it lies at the boundary of the broadband and immediately following the RF band. Therefore, this 300 GHz–3 THz band exhibits similarities to RF.

[0192] FIG. 7 illustrates an electromagnetic spectrum according to one embodiment of the present disclosure. The embodiment of FIG. 7 may be combined with various embodiments of the present disclosure. Key characteristics of THz communication include (i) a widely available bandwidth to support very high data transmission rates, and (ii) high path loss occurring at high frequencies (highly directional antennas are indispensable). The narrow beam width generated by highly directional antennas reduces interference. The small wavelength of THz signals allows a much larger number of antenna elements to be integrated into devices and BSs operating in this band. This enables the use of advanced adaptive array techniques that can overcome range limitations.

[0193] Transmitting system information (i.e., information related to the attributes, characteristics, and / or capabilities of the BS required to use the service, etc.) (e.g., MIB, SIB, etc.) in the THz frequency band can be inefficient because, in the case of high frequency bands, beam sweeping must be performed more frequently to cover the entire area of ​​the cell as the beam width becomes narrow. In particular, transmitting system information in this manner is even more inefficient when there are not many users in the cell. Accordingly, a system information transmission procedure as shown in FIG. 8 below may be used.

[0194] FIG. 8 illustrates an example of a procedure for transmitting system information for THz communication to which the present disclosure applies. Although this example is written with THz conditions in mind, it is also applicable to 6G communication environments where THz is not applied. Furthermore, the procedure exemplified in FIG. 8 can be combined with various embodiments of the present disclosure described below. For example, the embodiments described below may be performed based on the system information obtained by the procedure exemplified in FIG. 8.

[0195] Referring to FIG. 8, the base station can transmit system information of cell #1 through cell #2 (801). That is, the base station provides at least two cells, cell #1 uses the THz frequency band, and cell #2 uses a frequency band other than the THz frequency band. Here, the system information may include at least one information / state / parameter / setting generated at the higher layer and the physical layer, respectively. For example, at least one information / state / parameter / setting generated at the higher layer may include at least one of SFN, control information setting for SIB1 (e.g., PDCCH configuration for SIB1, etc.), information related to cell selection / entry (e.g., cell barring, cell re-selection, etc.), and subcarrier spacing, and at least one information / state / parameter / setting generated at the physical layer may include at least one of SFN, half frame indicator, and SSB index. However, this is merely an example, and system information may include information, status, parameters, and settings related to Cell #1 / Cell #2 generated at various types of physical layers / upper layers. To this end, as an example, Cell #1 and Cell #2 may have a secondary cell and primary cell relationship.

[0196] The UE can obtain synchronization for cell #1 (803). Synchronization can be obtained by detecting a synchronization signal. Generally, synchronization is obtained prior to receiving system information, but since the system information for cell #1 is received in cell #2, synchronization for cell #1 can be obtained after receiving system information. For example, the UE can obtain synchronization based on system information. However, unlike FIG. 8, synchronization may be obtained before step 801 according to other examples.

[0197] The UE can transmit a signal to connect to Cell #1 (805). For example, the signal may include information for connecting to Cell #1 (e.g., a random access preamble). The structure of the signal and the resources for transmitting the signal (e.g., a channel) can be identified through system information. Subsequently, the UE and the base station can perform a connection procedure to Cell #1 and perform communication (807). In this process, operations according to various embodiments described below may be performed.

[0198] The procedure described with reference to FIG. 8 may be performed when the UE (801) first connects to cell #1 of the base station. Alternatively, a similar procedure may be performed when the UE (801) handovers to cell #1 of the base station. However, in the case of a handover, the system information of cell #1 may be received from a cell of a different base station rather than cell #2 of the base station.

[0199] Communication in the THz band is expected to experience severe path loss, and to overcome this, terminals and base stations must use very sharp beams. The use of sharp beams means that terminals and base stations must perform beam control along with beamforming, and the number of beams used becomes very large. Therefore, it takes a very long time to align the transmit and receive beams between the base station and the terminal. In addition, if the beam alignment between the base station and the terminal is misaligned due to the movement of the terminal, time is frequently required to realign the beams, which may result in an unstable link. Accordingly, a beam management procedure as shown in Fig. 9 below may be used.

[0200] FIG. 9 illustrates a beam management procedure applicable to the present disclosure. FIG. 9 illustrates an example of a procedure for searching and / or selecting beams for THz communication, but is not limited to a THz environment and is applicable to a 6G communication environment. Additionally, the procedure exemplified in FIG. 9 may be combined with various embodiments of the present disclosure described below. Here, a beam may be interpreted as 'spatial (configuration) information', 'spatial domain filter', 'spatial domain transmit filter', 'spatial domain receive filter', or / and a term having an equivalent technical meaning capable of distinguishing a beam (e.g., Reference signal, SSB (Synchronization Signal Block) Index, TRP (transmission reception point), panel, cell, TP (transmission point), base station, control resource-related information (e.g., CORESET (control resource set)-related information, etc.).

[0201] Referring to FIG. 9, the base station can configure resources for beam management (901). Here, the resources may include at least one of time-frequency resources, channels, and spatial resources (e.g., antenna ports). For example, the base station may utilize a beam search signal (BSS) that is spatially separated from existing downlink signals / channels for beam search. Here, the BSS may be transmitted based on a dedicated port for beam search. The dedicated port may be a port different from the port for transmitting existing downlink signals / channels (e.g., synchronization signals (e.g., SSB, etc.), data channels (e.g., PDSCH, etc.)). BSS is a term defined for convenience of explanation, and the technical concept according to the present embodiment is not limited to the term BSS itself. That is, a signal transmitted based on a dedicated port defined / configured for beam search may be included in the technical concept according to the present embodiment.

[0202] The base station can transmit measurement signals using multiple transmission beams (903). For example, the measurement signals may include at least one of a reference signal and a synchronization signal. At this time, the measurement signals may be transmitted as many times as the number of beams required for measurement, and may be transmitted using a multi-beam transmission method that forms multiple beams simultaneously to reduce sweeping time. Here, multi-beam transmission may be performed based on at least one of a multi-panel, a sub-array, and a true time delay (TTD).

[0203] The UE can transmit a feedback signal to the base station (905). The feedback signal indicates at least one beam selected by the UE. The UE can select at least one preferred beam based on the received measurement signals. The UE and the base station can perform communication (907). At this time, the UE and the base station can perform communication using the previously selected beam. If channel reciprocity is established, the UE's transmission beam can also be determined through operations 903 and 905, so the UE's transmission can also be performed using the beam selected in operation 905. If channel reciprocity is not established, a procedure including the transmission of the UE's measurement signals and the transmission of the base station's feedback signal may be performed first to determine the UE's transmission beam. In operation 907, operations according to various embodiments described below may be performed.

[0204] Integrated Sensing and Communication (ISAC)

[0205] Wireless sensing is a technology that utilizes radio frequencies to determine the instantaneous linear velocity, angle, and distance (range) of an object, thereby obtaining information about the characteristics of the environment and / or objects within that environment. Since radio frequency sensing capabilities do not require connecting to objects via devices within a network, they can provide services for determining object locations without the need for devices. The ability to obtain range, velocity, and angle information from radio frequency signals can provide a wide range of new functions, such as various object detection, object recognition (e.g., vehicles, humans, animals, UAVs), and high-precision localization, tracking, and activity recognition. Wireless sensing services can provide information to various industries (e.g., unmanned aerial vehicles, smart homes, V2X, factories, railways, public safety, etc.) that enable applications such as intruder detection, assisted vehicle steering and navigation, trajectory tracking, collision avoidance, traffic management, and health and traffic management. In some cases, wireless sensing may utilize non-3GPP type sensors (e.g., radar, cameras) to further support 3GPP-based sensing. For example, the operation of a wireless sensing service, that is, the sensing operation, may depend on the transmission, reflection, and scattering processing of wireless sensing signals. Therefore, wireless sensing can provide an opportunity to enhance existing communication systems from communication networks into wireless communication and sensing networks.

[0206] FIG. 10 illustrates an example of a sensing operation according to an embodiment of the present disclosure. The embodiment of FIG. 10 may be combined with various embodiments of the present disclosure. Specifically, FIG. 10(a) illustrates an example of sensing using a sensing receiver and a sensing transmitter located at the same position (e.g., monostatic sensing), and FIG. 10(b) illustrates an example of sensing using a separated sensing receiver and a sensing transmitter (e.g., bistatic sensing).

[0207] For example, in a wireless communication system based on a 6G network of the present specification, referring to FIG. 10(a), the sensing transmitter and the sensing receiver may be configured to be included in a single base station (i.e., the same base station) or a single terminal (i.e., the same terminal). Alternatively, referring to FIG. 10(b), the sensing transmitter and the sensing receiver may be configured to be included in different base stations, in different terminals, or in a terminal and a base station, respectively.

[0208] In this regard, based on whether the sensing transmitter and the sensing receiver are each included in a base station or a terminal, the following six types of sensing modes can be defined.

[0209] - Mode 1: A mode in which the sensing transmitter and sensing receiver are included in a single base station (e.g., base station-based sensing mode in monostatic mode)

[0210] - Second mode: A mode in which the sensing transmitter is included in the first base station and the sensing receiver is included in a second base station different from the first base station (e.g., base station-based sensing mode in bistatic mode)

[0211] - 3rd Mode: A mode in which the sensing transmitter is included in the base station and the sensing receiver is included in the terminal (e.g., base station-terminal sensing mode)

[0212] - 4th Mode: A mode in which the sensing transmitter is included in the terminal and the sensing receiver is included in the base station (e.g., terminal-base station sensing mode)

[0213] - 5th Mode: A mode in which the sensing transmitter and the sensing receiver are contained in a single terminal (e.g., terminal-based sensing mode in monostatic mode)

[0214] - 6th mode: A mode in which the sensing transmitter is included in the first terminal and the sensing receiver is included in a second terminal different from the first terminal (e.g., terminal-based sensing mode in bistatic mode)

[0215] In a wireless communication system based on a 6G network of the present specification, one or more of the six types of sensing modes described above may be utilized independently or in combination.

[0216] In relation to the sensing operation in FIG. 10, the sensing transmitter may transmit a sensing signal for sensing one or more objects (and / or the environment surrounding the objects). For example, the sensing signal may correspond to a radio (frequency) signal defined to be transmittable by a base station / terminal in a wireless communication system based on a 6G network of the present specification. The sensing receiver may receive a signal that is scattered / reflected by one or more objects (and / or the environment surrounding the objects) from the sensing signal transmitted from the sensing transmitter. In the sensing receiver, sensing data may be derived from the scattered / reflected signal, and sensing results may be generated / obtained through processing of the sensing data. Here, the sensing result may include characteristic information (e.g., location, distance, speed, angle, etc.) about one or more objects (and / or the environment surrounding the objects). The sensing result thus generated / acquired may be utilized for wireless sensing services (e.g., detection, tracking, etc. of objects and / or environments) provided by a wireless communication system based on a 6G network of the present specification, or may be provided / disclosed to a trusted third party.

[0217] Additionally, the sensing operation in FIG. 10 is described using a representative example of operation in a wireless communication system based on a 6G network, but it can be extended and applied to cases where terminals / base stations / signals based on previous generations (e.g., 4G, 5G, etc.) networks are utilized.

[0218] Additionally, with respect to the wireless sensing described in this specification, in a wireless communication system based on a 6G network of this specification, time / frequency resources for sensing operations and time / frequency resources for general communication (e.g., UL / DL / sidelink-based communication, etc.) may be scheduled / configured separately.

[0219] FIG. 11 illustrates a time / frequency resource for a sensing operation according to one embodiment of the present specification. The embodiment of FIG. 11 may be combined with various embodiments of the present disclosure.

[0220] Referring to FIG. 11, the time / frequency resources (hereinafter, sensing resources) for the aforementioned sensing operation (e.g., sensing operation based on FIG. 10) can be set / assigned separately from the time / frequency resources (hereinafter, communication resources) for general communication.

[0221] For example, as illustrated in FIG. 11, sensing resources may be configured / assigned in units of symbols in the time domain and / or in units of resource blocks in the frequency domain. Resources other than those configured / assigned to the sensing resources may be utilized as resources for general communication. That is, sensing resources and communication resources may be configured / assigned based on time-division multiplexing (TDM) and / or frequency-division multiplexing (FDM) methods in terms of base station / terminal operation. Additionally or alternatively, unlike that illustrated in FIG. 10, sensing resources may be configured / assigned based on other units in the time domain (e.g., slot, frame, absolute time (ms, us), etc.) and / or other units in the frequency domain (e.g., subcarrier, carrier, absolute frequency (MHz, GHz), etc.).

[0222] Additionally or alternatively, in relation to the setup / allocation / scheduling of resources for general communication described herein, it may be necessary to consider the relationship between said resources and the aforementioned sensing resources. For example, when setting / allocating resources for general communication according to the embodiments of the present disclosure, said resources may be set / allocated to rate-match or puncturing resource areas corresponding to the sensing resources. For example, when scheduling resources for general communication according to the embodiments of the present disclosure, said resources may be scheduled so as not to overlap with resource areas corresponding to the sensing resources. If resources for general communication and resource areas corresponding to the sensing resources are set / allocated / scheduled to overlap according to the embodiments of the present disclosure, either one or both operations may be dropped, skipped, or postponed based on priority, predefined rules, etc. That is, in the embodiments of this specification, resources related to general communication (e.g., resources for signals / channels related to UL / DL / Sidelink-based data / control, etc.) may be configured / assigned / scheduled so as not to overlap with the aforementioned sensing resources.

[0223] Additionally, various channel modeling methods may be applied in relation to the wireless sensing described herein. Channel modeling related to sensing may mean constructing a path for transmitting and receiving sensing signals and / or scattered / reflected signals by considering the object to be sensed and / or the environment to which the object belongs. Since channel modeling may be related to the performance / requirements of sensing in a wireless communication system, it may be an important matter for verifying the validity of the sensing function.

[0224] Channels related to sensing can be classified into channels between an object (e.g., target of interest) and a sensing transmitter / receiver, and channels between the environment to which the object belongs and a sensing transmitter / receiver. In this regard, channel modeling related to sensing can be classified based on the sensing mode (e.g., the six types of modes mentioned above), whether it is an object or an environment, and / or sensing scenarios. For example, channel modeling for a target in a base station / terminal-based monostatic sensing mode, channel modeling for a target in a base station / terminal-based bistatic sensing mode, channel modeling for an environment in a base station / terminal-based monostatic sensing mode, and channel modeling for an environment in a base station / terminal-based bistatic sensing mode can be optimized and configured differently. For example, when various sensing scenarios are classified, they can be divided into channel modeling for detection, location, and tracking scenarios, channel modeling for motion recognition, and channel modeling for imaging / environment reconstruction scenarios. Additionally, channel modeling related to sensing may be based on statistical channel modeling techniques and / or deterministic channel modeling techniques. For example, modeling for sensing in a wireless communication system based on a 6G network of this specification may be based on stochastic geometry channel modeling techniques and / or hybrid with ray tracing channel modeling techniques. Here, the stochastic geometry channel model may be based on various statistical characteristics of the channel state. Furthermore, the hybrid channel model may be based on both ray tracing techniques and stochastic techniques.In the case of a hybrid approach, channels for objects requiring high accuracy and consistency (e.g., targets of interest) can be modeled using ray tracing techniques, while channels for the environment can be modeled using probabilistic techniques.

[0225] FIG. 12 illustrates a procedure related to a sensing operation according to one embodiment of the present specification. The embodiment of FIG. 12 may be combined with various embodiments of the present disclosure.

[0226] For example, in a wireless communication system based on a 6G network of the present specification, in the case of a sensing operation in which a terminal participates, the base station may need to verify (1205) the terminal's capability for the sensing operation. In this regard, the terminal may be configured to report capability information to the base station regarding whether it supports the sensing operation. Additionally, or alternatively, if the terminal is defined in advance in the specification as supporting the sensing operation, the procedure may be omitted. Furthermore, in the case of a sensing operation in which only the base station participates, the base station may be configured to report capability information regarding whether it supports the sensing operation to the entity setting / controlling its sensing operation (e.g., a network entity at the upper level / layer of the base station).

[0227] For example, a base station may perform signaling with a terminal to exchange configuration information related to a sensing operation. For example, the base station may set / instruct the terminal information regarding the mode of the sensing operation (e.g., based on the six types of modes mentioned above), the subject of the sensing operation (e.g., a sensing transmitter, a sensing receiver), the resource of the sensing operation (e.g., a sensing resource as shown in FIG. 11), the target of utilization of the sensing result (e.g., a type of wireless sensing service based on a 6G network, a trusted third party), and channel modeling for sensing (e.g., a channel between the base station / terminal and an object / environment) (1210). For example, the base station may receive such information from a network entity at the upper level / layer of the base station.

[0228] For example, a base station and / or terminal may perform a sensing operation on information set / instructed (1215). For example, the base station and / or terminal may perform procedures such as transmitting a sensing signal as in FIG. 9 described above, receiving scattered / reflected signals, deriving sensing data, obtaining a sensing result through processing the sensing data, and providing the sensing result, as a role of a sensing transmitter and / or sensing receiver. For example, in the operation of the base station / terminal described in this specification, the sensing result provided through the sensing operation may be utilized.

[0229] NR's CSI-related procedures

[0230] The terminal can receive configuration information related to CSI from the base station via RRC signaling. The configuration information related to CSI may include at least one of information related to CSI-IM (interference management) resources, information related to CSI measurement configuration, information related to CSI resource configuration, information related to CSI-RS resources, or information related to CSI report configuration.

[0231] - 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.

[0232] - 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.

[0233] - 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.

[0234] - 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.

[0235] - 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.

[0236] The terminal can measure CSI based on configuration information related to CSI. CSI measurement may include a procedure for receiving CSI-RS and acquiring CSI by computing the received CSI-RS.

[0237] The terminal can transmit CSI reports to the base station. For CSI reporting, the time and frequency resources available to the UE are controlled by the base station. 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), L1-RSRP, and / or L-SINR.

[0238] 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.

[0239] NR Precoder

[0240] Precoders defined in NR standards (e.g., codebooks for CSI reporting or PMI codebooks) can be classified into Type I codebooks, Type II codebooks, and Enhanced Type II codebooks.

[0241] - Type I Codebook

[0242] Type I codebooks are primarily targeted at Single User (SU)-MIMO, which supports both high and low orders. Type I codebooks can be divided into (i) single-panel codebooks and (ii) multi-panel codebooks. (i) Single-panel codebooks may be based on the assumption that a terminal receives downlink transmissions from a single antenna panel. (ii) Multi-panel codebooks may support base station configurations using multiple (e.g., 2 or 4) antenna panels. Unlike single-panel codebooks, which support ranks 1 through 8, multi-panel codebooks may support ranks 1 through 4.

[0243] The Type I codebook can be composed of the selection of preferred DFT vector(s) from the oversampled DFT vector set, which is the SD (spatial domain) basis, and the indication aspect for the co-phase of the base station antenna's cross-polarization.

[0244] - Type II Codebook

[0245] Type II codebooks can primarily support MI-MIMO supporting up to two layers. Compared to Type I, Type II codebooks can provide more accurate PMI, but this may result in increased signaling overhead. In Type II codebooks, PMI can identify sets of beams and sets of amplitude coefficients. Amplitude coefficients can be used to generate a weighted sum of beams. Type II codebooks can also identify phase shifts for co-phasing between beams. Type II port selection codebooks can be configured based on broadband / long-term PMI components (e.g., i1) and subband / short-term PMI components (e.g., i2).

[0246] In the case of Type II codebooks, multiple SD basis DFT vectors are selected, and the selected DFT vectors are linearly combined to achieve high resolution and excellent MU-MIMO performance.

[0247] - Enhanced Type II Codebook

[0248] Enhanced Type II codebooks (e.g., Rel. 16 DL codebook) are designed to address the disadvantage of CSI overhead associated with existing Type II codebooks. Enhanced Type II was introduced by reducing the payload of the codebook by taking into account the correlation of the frequency axis.

[0249] In Enhanced Type II codebooks (e.g., Rel-16 DL codebook), the precoding matrix W is W = W1*W2*W3 (or W1*W c *W H FIt can be expressed as follows. W1 corresponds to the SD basis related to SD compression, W3 corresponds to the FD basis related to FD compression, and W2 corresponds to the linear combining (LC) coefficients according to the FD compression of W3. If the terminal determines matrix W as an enhanced Type II PMI, it may report the indices of W1, the coefficients of W2, and the indices of W3 to the network. The dimension of matrix W is P(=2N1*N2)*N3, W1 may be P*2L, W2 may be 2L*M, and W3 may be N3*M. N1 may represent the number of columns in the first domain (the number of antenna ports in the first domain within the panel), and N2 may represent the number of rows in the second domain (the number of antenna ports in the second domain within the panel). The terminal can select 2L basis beam vectors in relation to W1, select M LC coefficients in relation to W2, and select M FD basis vectors in relation to W3 (where each vector is an N3*1 orthogonal DFT vector). L is a parameter for SD compression, representing the number of SD beams, which can be 2, 3, or 6. N3 and M are parameters for FD compression, where N3 is the DFT size for FD compression, and N3 = N SB It is expressed as *R, where R is the granularity between CQI and PMI and can be 1 or 2. The (L, p, β) parameter combination (paramCombination-r16) defining enhanced Type II is shown in Table 1.

[0250]

[0251] In Table 1, v corresponds to RI, and β is a parameter related to the upper limit of the LC coefficients selected by the terminal.

[0252] AI / ML-based UL precoder compression

[0253] Due to advancements in computational processing technology and AI / ML technologies, the nodes and terminals constituting wireless communication networks are becoming more intelligent and sophisticated. In particular, as a result of this network intelligence, it is expected that various network decision parameter values ​​(e.g., transmit / receive power of each base station, transmit power of each terminal, precoder / beam of base stations and terminals, time / frequency resource allocation for each terminal, duplex method of each base station, etc.) can be rapidly optimized, derived, and applied according to diverse network environment parameters (e.g., distribution / location of base stations, distribution / location / material of buildings / furniture, location / movement direction / speed of terminals, weather information, etc.).

[0254] The present disclosure describes a method in which, when a wireless communication service is supported based on AI / ML in a next-generation wireless communication system, a base station compresses precoder / transmission rank instructions for uplink transmission based on AI / ML and instructs them to a terminal.

[0255] The Rel-18 AI / ML study investigated the introduction of AI / ML for CSI feedback enhancement (e.g., CSI compression, CSI prediction), beam management (e.g., spatial domain beam prediction, temporal domain beam prediction), and positioning accuracy enhancement (e.g., direct positioning, AI / ML assisted positioning). With the exception of CSI compression in the CSI feedback use case, all are based on one-sided models where AI / ML models / functionalities are configured only in the base station or the terminal. In the case of CSI compression, it is based on a two-sided model where the base station (e.g., decoder model) and the terminal (e.g., encoder model) collaborate using their respective models to perform CSI feedback.

[0256] In the following, we propose a method to utilize a two-sided model for uplink transmission precoder instruction / recovery by extending the two-sided model. For example, we propose an uplink precoding technique based on a two-sided model for the purpose of reducing the overhead required when performing UL subband precoding and for improving uplink performance.

[0257] FIG. 13 illustrates a two-sided model configuration according to one embodiment.

[0258] In the case of a two-sided model for CSI compression, when a terminal compresses (e.g., encoding) information about a DL channel obtained based on DL RS (e.g., CSI-RS) and transmits it to a base station (1301), the base station restores (e.g., decoding) the compressed CSI based on AI.

[0259] On the other hand, in the case of the two-sided model for UL precoder compression proposed in this specification, when a base station acquires / calculates / compresses (e.g., AI-based inference / encoding) information about a UL channel, e.g., a UL precoder, and transmits it to a terminal (1302), the terminal restores (e.g., decoding) the compressed UL precoder information based on AI. The acquisition / calculates / compresses of the UL precoder by the base station may be based on the UL RS (e.g., SRS) transmitted by the terminal. Based on the compressed and restored UL precoder information, the terminal may generate / precode a UL signal and transmit it to the base station. Various configuration information for such UL precoder compression (e.g., two-sided model configuration, training, maintenance / management, etc.) may be provided in advance as upper-layer signaling.

[0260] The following describes proposals for uplink precoding techniques based on a two-sided model. The distinction between Proposal 1 and Proposal 2 is for convenience of explanation and may be implemented individually, but is not limited thereto; they may also be implemented together based on at least a partial combination of Proposal 1 and Proposal 2.

[0261] Proposal 1

[0262] For example, to provide instructions for an Uplink (subband) precoder, consider a two-sided model, and the model input / output may include at least one of the following.

[0263] - Model input at NW side: (group of) UL precoder or eigenvector

[0264] - Model output at NW side: compressed precoder information

[0265] - Model input at UE side: indicated information from BS

[0266] - Model output at UE side: reconstructed (group of) UL precoder information

[0267] Proposal 1 is a proposal regarding model input / output on the NW side and the UE side when performing UL precoder indication based on a two-sided model, and an example of such an AI / ML-based precoder indication / beamforming instruction method procedure may include all or part of the following.

[0268] - Step 1: SRS transmission by UE

[0269] - Step 2: UL Channel estimation and calculation of UL precoder at NW

[0270] - Step 3: Model inference at NW (ie, UL encoder)

[0271] - Step 4: Indication of compressed precoder information by NW

[0272] - Step 5: Reception of compressed precoder information by UE

[0273] - Step 6: Model inference at UE (ie, UL decoder)

[0274] For example, in Proposal 1, precoder information may be included as an input to the NW-sided model. More specifically, the base station calculates optimal precoding information by considering UL channel information measured based on the SRS transmitted by the terminal, / and UL channel information of other UEs scheduled for MU-MIMO operation, / and interference that may be caused by said UEs when transmitting UL. If there is a predefined / configured codebook form common to both the base station and the terminal, the codebook index may be used as the input to the NW-sided model as the precoding information. Additionally, information such as the frequency unit to which the codebook applies (e.g., wideband, subband, group of subband, group of subcarrier) may also be utilized as additional input or assistant information, and such information may also be signaled for the terminal-side decoder. Here, the precoder may be composed of a single or multiple layers, may be a group of multiple precoders consisting of a single layer per specific frequency unit, or a single precoder consisting of multiple layers, and may also be signaled along with a rank indicator. In such cases, since the size of the entire compressed precoder information may vary depending on the rank and frequency unit, information regarding this size may also be additionally instructed / set to the terminal.

[0275] As an example of the above UL precoder instruction and additional information, the TPMI instruction for N subbands may be indicated as compressed M bits (e.g., indicated based on Proposal 2 described below), and information about N and / or rank information may also be indicated to the terminal (separately). The information about N may be determined based on rule (e.g., PRB size, subband size), or NW may set / instruct one value among a plurality of candidate values.

[0276] The output of the NW-sided model is compressed information of the above (group of) precoder information, which can be further quantized and expressed as binary bits. The above quantization may be scalar quantization that quantizes the real part into amplitude and phase, respectively, or codebook-based quantization based on a specific (predefined) codebook.

[0277] Information regarding the above UL precoder and instructions regarding the NW-sided model output (after quantization) may be indicated based on DCI (similar to conventional methods), and specific instructions may follow Proposal 2.

[0278] The terminal can de-quantize the received information and input it into a UE-sided model to perform UE-sided model inference. The UE-sided model output may include reconstructed (group of) UL precoder information. As described above, frequency unit and / or frequency band information (start subcarrier (or RB) and / or end subcarrier (or RB)) to which the reconstructed UL precoder information is to be applied may also be additionally instructed / set. The terminal can perform UL transmission, for example, PUSCH transmission, based on the information of the reconstructed UL precoder and the frequency unit / band information. In the case of Proposal 1, since it is a method that utilizes compression using AI / ML when the signaling overhead required for precoder instruction is large, such as in subband precoding, an increase in throughput can be expected when intending to perform PUSCH transmission using frequency selective precoding. If at least some of the information of the above-mentioned UL precoder and the set frequency unit and / or band information do not match, the terminal may use a specific precoder among the reconstructed precoders (e.g., the first precoder of the dual stage structure W=W1*W2) as a WB precoder to transmit UL in the corresponding frequency unit / band, or use an average precoder of the reconstructed precoders to perform UL (e.g., PUSCH) transmission as a WB precoder.

[0279] In Proposal 1 above, the terminal's coherent transmission capability (e.g., non-coherent, partial coherent, full coherent) can be considered in the UL precoder instructions. Here, coherent transmission refers to whether the terminal can maintain a relative phase between transmission ports from SRS transmission to PUSCH transmission, and this can be reported to the base station through the terminal's capability report. Here, non-coherent means that no port can perform coherent transmission. Partial coherent is a case where coherent transmission is possible between specific port groups, but not between across port groups. Full coherent means that all ports are capable of coherent transmission. In the UL precoder instructions based on the above two-sided model, this coherent transmission capability can be considered as side information during training, and this coherent transmission can be considered as a specific constraint of the cost function (e.g., mean square error minimization, throughput maximization) to train the model. In the case of such coherent capability, for a general-purpose model (for coherent transmission), it can be applied as model input and used for encoding / decoding compressed precoder information. Alternatively, different model IDs for each of the aforementioned coherent transmissions (e.g.It can be managed as non-coherent - model #A, partial-coherent = model #B, full coherent = model #C), and information on the model ID and / or mapping information between the model ID and coherent transmission can be instructed / configured to the terminal.

[0280] One of the major issues with UL (subband) precoder instruction is the significant overhead required to implement it. To address this, restrictions on specific UL precoders can be considered. For example, rather than configuring a large payload to instruct the precoder to support full-directional terminal UL precoding, it may be more advantageous in terms of overhead and performance to set or restrict the precoding direction to a specific direction using information regarding the terminal's location / mobility and the location of the (serving) base station. Such UL precoder direction settings or restrictions can be applied as input or side information during training for the encoder, which is the base station's model. To this end, the terminal can report mobility information, such as its location and velocity, and / or angle information between the terminal's transmitting antenna and the base station (e.g., via global coordinate) to the base station. The base station can then use this information to perform model training or inference by utilizing pre-processed input information (in this case, the restricted precoder).

[0281] In the UL precoder compression and instruction method based on a two-sided model of Proposal 1, data collection can be performed to train and / or inference and / or monitor the two-sided model. For such data collection, some or all of the following measurements may be considered, and the results of the measurements can be transmitted to a training node and / or inference node and / or monitoring node.

[0282] - NW side measurement: SRS-based UL channel measurements transmitted by the terminal

[0283] - UE side measurement: ground truth precoder information and / or final reconstructed precoder information

[0284] For example, reporting via UCI can be used for reporting ground truth precoder information / final reconstructed precoder information. For reporting via UCI, a new reporting quantity can be defined / configured at the terminal through upper-layer signaling, etc.

[0285] As an example of Proposal 1 above, UL precoder compression may consider a method in which precoder information with SB (subband) attributes (e.g., index, parameter) is compressed, while precoder information with WB (wideband) attributes (e.g., index, parameter) is transmitted as before (e.g., without compression). For example, UL subband precoding may be in a dual-stage codebook format (e.g., W=W1*W2) or a codebook format of a compression type (e.g., Rel-16 DL codebook) (e.g., W=W1*W2). c *W H F It can be in the form of ). In the case of a dual-stage codebook, codebook parameters and indices (e.g., spatial domain basis) corresponding to the WB attribute W1 can be instructed / configured to the terminal using the existing legacy DCI method, while codebook parameters and indices corresponding to the remaining SB attribute W2 can be compressed based on AI / ML and instructed / transmitted to the terminal. Additionally, W = W1 * W c *W H F If you configure a codebook in the form of, the WB property W1 and / or W F (eg, Codebook parameters, indices, etc. corresponding to the frequency domain basis are instructed / set to the terminal using the existing legacy DCI method, and W cCodebook parameters and indices (e.g., combining coefficients in Rel-16 type 2 codebook) can be compressed using AI / ML and transmitted / instructed to the terminal. In the above embodiments, UL precoder information compressed by AI / ML using a separate field from the WB attribute codebook parameters / index or the method of Proposal 2 can be transmitted / instructed to the terminal. Therefore, if the AI / ML functionality / model, etc., is determined to be unsuitable through monitoring during the LCM (life cycle management) process, it is predefined / agreed upon that only the WB attribute UL precoder is used as valid, and the AI / ML functionality / model can be disabled / de-active. Alternatively, if unsuitability is determined through terminal-side monitoring, the terminal can use the WB attribute precoder (or fallback precoder) when transmitting UL PUSCH and ignore the AI / ML-based precoder information. If transmission based on the dual DCI of Proposal 2 is used in the above example, the WB attribute codebook parameters / index are 1 st It is transmitted / instructed via DCI, and the remaining AI / ML-based compressed precoder information is 2 nd It may be transmitted / instructed via DCI. Alternatively, if MAC-CE-based transmission is used, the codebook parameters / indexes of WB attributes may be transmitted / instructed via DCI, while the remaining AI / ML-based compressed precoder information may be transmitted / instructed via MAC-CE.

[0286] Proposal 2

[0287] For example, to provide UL precoder instructions based on a two-sided model, consider the following UL precoder instruction method.

[0288] (1) Option 1: DCI-based indication method

[0289] 1) Option 1-1: One-step DCI-based instruction method

[0290] 2) Option 1-2: two-step DCI-based indication method,

[0291] - For example, a method of using the 1st DCI to indicate information other than precoding, and the 2nd DCI to indicate information regarding precoding.

[0292] (2) Option 2: MAC-CE-based indication method

[0293] The above proposal 2 can be used to resolve payload limitations that may occur when performing the operation corresponding to step 4 of proposal 1 (as in the conventional method) (one step) based on DCI.

[0294] In the case of Option 1 of (1) above, it is a DCI-based UL precoder and / or rank indication method.

[0295] According to current NR standards, up to 11 bits in the DCI can be used for UL precoder instructions. However, to support subband precoding, 11 bits is too restrictive, so a larger number of bits may be required. As a solution for this, a dedicated DCI format (e.g., DCI format 0_x) for subband precoding or for performing AI / ML-based UL precoder instructions, as described in option 1-1, can be configured, defined, and used. For instance, since the primary purpose of a dedicated DCI format is to provide UL precoder information, all or most of the bits in the DCI can be used for this purpose, thereby resolving the issue of existing methods where only a small number of bits, such as 11 bits, are allocated for UL precoder information.

[0296] Alternatively, two-step DCI can be used as in option 1-2. Here, two-step DCI refers to a method in which two DCIs (e.g., 1st DCI and 2nd DCI) are indicated / configured. For example, these two DCIs can be indicated / configured to the terminal with different periodicities / offsets, etc. The field size, number, etc. of the 2nd DCI can be determined by the field values ​​within the 1st DCI.

[0297] For example, the 1st DCI can transmit / set scheduling information unrelated to actual compressed precoder information and / or elements that can determine the size of the actual compressed precoder (e.g., rank information, frequency granularity information, quantization information, model (pair) ID, etc.), and the 2nd DCI can transmit / set actual compressed precoder information.

[0298] As another example, the size value of the UL precoder information of the 2nd DCI (e.g., M bits in the embodiment of Proposal 1) can be directly indicated from the 1st DCI.

[0299] In addition, as described above, WB precoder information (e.g., SD basis) can be transmitted through the 1st DCI, and AI / ML-based compressed information can be transmitted through the 2nd DCI.

[0300] Meanwhile, (2) Option 2 is a method of transmitting actual compressed precoder information using MAC-CE. As an example of the above method, compressed precoder information associated with scheduling information set / instructed from DCI can be set / instructed to MAC-CE in a manner similar to option 1-2. The DCI associated with the above MAC-CE can be agreed / defined to be determined as the latest valid DCI prior to receiving the above MAC-CE.

[0301] In the case of Configured Grant PUSCH, the base station uses a semi-static scheduling method based on RRC rather than dynamic scheduling. To this end, the base station must pre-configure the UL precoder information to be used by the terminal during CG-PUSCH transmission via RRC. For example, the base station can set the model ID / pairing ID information to be used for UL precoder compression to the terminal, and additionally, signal a compressed dataset to the terminal. The terminal can perform CG-PUSCH transmission using the UL precoder calculated from the above dataset. Alternatively, it may be agreed / restricted not to use AI / ML-based UL precoder compression for CG-PUSCH. Furthermore, the terminal may expect to be configured / instructed to always use only WB precoding instead of SB precoding during CG-PUSCH scheduling.

[0302] Meanwhile, regarding Proposal 2, a more specific example of a method using DCI and MAC-CE or at least one of DCI / MAC-CE / RRC in combination is described.

[0303] For example, in the case of a method using DCI and MAC-CE in combination, configuration information related to factors that can determine the size of the actual compressed precoder (e.g., rank information, frequency granularity information, quantization information, and / or model (pair) ID, etc.) is transmitted via MAC-CE, and based on this, the actual compressed precoder information is indicated via DCI. Here, the bit-width of the actual compressed precoder information indicated within DCI can be determined based on the information set by MAC-CE.

[0304] For example, in a method that combines DCI, MAC-CE, and RRC, multiple configuration details related to factors determining the size of the actual compressed precoder (e.g., rank information, frequency granularity information, quantization information, model (pair) ID, etc.) are transmitted via RRC; some of these settings can be selected or instructed (e.g., activated) via MAC-CE; and finally, the actual compressed precoder information can be instructed via DCI within the range selected or instructed by MAC-CE. Here, the bit-width of the actual compressed precoder information instructed within DCI can be determined based on the information selected by MAC-CE.

[0305] For model training in the UL precoder instruction method based on a two-side model based on the above Proposal 1 and / or Proposal 2, the following three methods can be considered.

[0306] - Approach 1: One node training and transfer to inference nodes

[0307] - Approach 2: Two node joint training by exchanging gradient and forward activation

[0308] - Approach 3: Two node sperate training by exchanging dataset

[0309] The above approaches 1, 2, and 3 are partially similar to those discussed in DL CSI compression; however, the main difference between DL CSI compression and UL precoder compression is that in UL precoder compression, the data collection entity is the network (e.g., base station). As such, transmitting the dataset collected from the network to the terminal side for training can be somewhat inefficient in terms of signaling overhead. To address this, one may consider transferring / delivering at least a portion of the information regarding the UE-side model that has been trained from the network side.

[0310] Alternatively, as an example, training can be performed by considering the relationship between DL CSI compression (model) and UL precoder compression (model). For example, referring to FIG. 13, a terminal may be equipped with an encoder for DL ​​CSI compression and a decoder for decompression of compressed UL CSI (e.g., UL precoder). A base station may be equipped with a decoder for decompression of compressed DL CSI and an encoder for UL CSI (e.g., UL precoder) compression. Accordingly, the encoder-decoder (pairs) of each node (e.g., terminal, base station) can be designed and deployed integrally. To this end, a certain ID managing the encoder-decoder for each node may be assigned.

[0311] For example, at a terminal, an encoder for CSI compression and a decoder for restoring compressed UL CSI (e.g., UL precoder) are paired, and an ID may be assigned to the pair. The ID may be used for the LCM (e.g., training, monitoring, and / or update) of the pair.

[0312] These encoder-decoders can be trained using one of the aforementioned training methods, specifically approach 1, and then transferred to each node. Subsequently, each node can be fine-tuned to suit its specific configuration; for example, in the case of a terminal, the encoder can operate according to DL and the decoder according to UL. In the case of a base station, the encoder can be fine-tuned to operate according to UL, and the decoder according to DL. The advantage of this integrated design is that it facilitates the estimation / prediction of the output of a decoder node from a single encoder node when performing model monitoring or CQI calculations. For instance, a terminal can obtain the final UL / DL deconstructed precoder using the encoder and decoder equipped by the terminal. However, due to fine-tuning at each node, the assumed models may be configured differently, potentially leading to a problem where the encoder of one node and the decoder of another node are no longer compatible. To address this, parameter exchange, dataset exchange, or monitoring output can be exchanged via periodic, semi-periodic, non-periodic, or event-triggered methods.

[0313] Regarding training, the terminal may report capabilities for DL ​​CSI compression only, UL CSI compression only, and both DL / UL compression to the base station as UE capabilities, or report them to the base station via applicable report via UAI. In particular, when DL / UL compression is applied together, a pairing ID between the encoder and decoder of each node may be set separately, and a separate ID may be additionally set for compatibility between the network and the UE, allowing the model pairing procedure between the network and the UE to be performed. If the two pairs mentioned above (e.g., the encoder-decoder pair between each node and the encoder-decoder pair of another node) are managed with the same ID or the same group ID, the terminal may assume a decoder identical or similar to the network and / or decoder, and utilize this for performance monitoring calculations and CQI calculations. For example, when calculating performance monitoring metrics (e.g., SGCS) or CQI, the terminal may be instructed by the base station to provide specific correction values ​​or thresholds. In such cases, if the same ID or the same group ID is set for the terminal and the base station, the terminal may calculate the metric or CQI without reflecting these correction values ​​or thresholds. The terminal may calculate the performance monitoring metric or CQI without reflecting the correction values ​​caused by the model mismatch and report it to the base station.

[0314] Monitoring related to AI / ML-based UL precoder compression

[0315] As described above, in next-generation wireless communication systems, existing communication functions can be replaced with AI / ML models / functionalities; however, since most of these AI / ML models are data-driven, specific models possess characteristics specific to a particular dataset, cell, or site. Except for cases where a generalized model is trained to operate across multiple datasets, cells, or sites, life cycle management (LCM) must be performed (e.g., model / functionality monitoring, model / functionality switching, model / functionality updating, model / functionality activate / deactivate, model transfer, model / functionality fine-tuning, functionality fallback) to adapt to changes in the wireless communication environment, such as channels, in order for the aforementioned functionality to operate properly.

[0316] Based on the discussion above, we describe measures for life cycle management (LCM) related to the use of an AI / ML-based method for compressing precoder / transmission rank instructions for uplink transmission and instructing them to the terminal. More specifically, we propose methods for performing performance and model monitoring.

[0317] Performance / model monitoring can be classified into NW-side (e.g., BS) monitoring and UE-side monitoring depending on the entity calculating the monitoring metric / output and the entity collecting the information.

[0318] First, we propose NW-side monitoring (e.g., a method in which the NW performs monitoring by calculating monitoring metrics, etc.).

[0319] Proposal 3

[0320] According to one embodiment, for a UL precoder indication based on a two-sided model, the following NW-side monitoring method is proposed. Specifically, a NW-side monitoring method is proposed that performs monitoring using information obtained by the NW from a terminal or when the NW has a UE-side model (e.g., reconstruction model).

[0321] At least one of the following four options may be used for NW-side monitoring.

[0322] (1) Option 1: Direct estimation of intermediate KPI (eg, SGCS, NMSE, etc.) or monitoring output

[0323] (2) Option 2: Based on output reconstruction model at NW (eg, actual reconstruction model delivered / transferred from training entity, proxy (reference) reconstruction model)

[0324] (3) Option 3: Based on the report of reconstruction model output from UE

[0325] (4) Option 4: Based on precoded SRS (eg, precoding is based on reconstruction model output at UE)

[0326] Specific examples for each of the above options are described.

[0327] (1) Option 1 is an example in which NW is equipped with an AI / ML model for monitoring separate from the UL precoder indication and outputs monitoring-related information (e.g., monitoring metric, monitoring output). Here, the monitoring metric may be a metric representing the correlation between the actual precoder (e.g., AI / ML encoder input) and the reconstructed precoder (e.g., NW-side reconstruction model output), and specifically, it may be squared generalized cosine similarity (SGCS) or normalized mean squared error (NMSE). In the case of the monitoring output, recommendation values ​​for subsequent monitoring actions (e.g., model / functionality switching, model / functionality update, model / functionality activate / deactivate, model transfer, model / functionality fine-tuning, functionality fallback), etc. may be output. Since the above UL precoder operates based on a two-sided model, in the case of monitoring output, if an AI / ML model capable of identifying whether the problem is with the NW-side model or the performance issue is with the UE-side model is utilized, subsequent monitoring actions can be instructed separately for each UE-side model and NW-side model. Since Option 1 is a value based on the NW-side model, it proceeds transparently to the UE and can operate as a base station implementation.

[0328] (2) In the case of Option 2, the NW receives the actual reconstruction model from the training entity and performs monitoring using it, or it is a monitoring method based on a proxy model or reference model that is (semi) compatible with the actual reconstruction model. For example, the NW performs monitoring by comparing the output value of the reconstruction model provided by the NW with the actual precoder value. The monitoring metric may be the aforementioned SGCS or NMSE. Alternatively, if the DL CSI compression (encoder-decoder) and the AI / ML UL precoder (encoder-decoder) can use mutually compatible models, the NW may use the DL-decoder as the aforementioned proxy model or reference model and use it for monitoring.

[0329] (3) Option 3 is a method in which the NW performs monitoring based on information reported from the terminal (e.g., reconstructed precoder information). To report information on the precoder reconstructed by the terminal, a DL codebook (e.g., NR Type 1 / 2 CSI) or a UL codebook (e.g., NR UL codebook) may be used. The NR codebook mentioned here is an example for convenience of explanation, and the available codebooks are not limited to this and can be extended to codebooks defined in the next generation, such as 6G. The terminal may report the most suitable (preferred) codebook index (or codebook parameters) from the above codebooks to the base station using a UL channel (e.g., PUCCH / PUSCH, etc.). For such terminal reporting for NW side monitoring, the base station may newly define / set a specific reporting quantity (e.g., UL-TRI-TPMI) to the terminal (e.g., RRC setting). Alternatively, the terminal may report with a higher resolution from the aforementioned DL codebook or UL precoder, and to this end, new codebook parameters (e.g., oversampling factor, amplitude / phase granularity, Codebook parameter configurations for L, β and p_v (paramCombination)) may be introduced, defined, or configured. In addition, information such as the reception time of the corresponding reconstruction UL precoder (e.g., time stamp) may also be reported to the base station. Additionally, an explicit reporting method of the terminal regarding the output (e.g., decoder output / reconstructed output) (e.g., element-wise quantization of decoder output) can also be considered. In this case, float format 32 / 16, etc., can be used for reporting. For example, since representing channel information with a finite number of precoders in a codebook is a type of compressed channel information, the terminal may transmit the real part vector in float format 32 / 16, etc., at a higher resolution than the defined codebook to transmit channel information reported for performance monitoring purposes, such as LCM, in the form of uncompressed raw data. However, because this type of reporting has a relatively large payload size and overhead, the reporting period for monitoring by the terminal may be set longer than the period of general UL precoder information / CSI reporting.

[0330] (4) Option 4 is a method in which the terminal precodes the (1 port) SRS based on the precoder information reconstructed by the terminal and transmits it to the base station, and the base station decodes the precoded SRS to determine the precoding information reconstructed by the terminal and performs monitoring based on this. To transmit the precoder SRS, the base station can separately set the usage for the SRS (e.g., monitoring usage) (through the SRS resource configuration that is signaled to the upper layer). In addition, the number of (1 port) SRS resources in the SRS resource configuration that includes the SRS usage can be set to a specific number N (e.g., N=1) or a maximum number for the terminal. For example, if N=1 is set, the terminal can precode the UL precoder for the first layer (e.g., layer 1) into the SRS and transmit it to the base station even if the rank of the actual UL precoder is >1. Alternatively, the terminal may select specific N layers to generate a precoded SRS and transmit it to the base station, and report to the base station via a separate signaling which layer selection the precoded SRS corresponds to (e.g., UL layer indicator (LI) report, where LI may correspond to the most recently reported PUSCH). If the maximum number is set to 4 and the actual rank is 2, the terminal may precode only 2 layers for SRS resources #1 and #2 respectively and transmit them to the base station, and for the remaining SRS resources #3 and #4, it may not transmit them or transmit them with arbitrary precoding, and the base station may ignore the resources transmitted with arbitrary precoding.Alternatively, repetitive transmission based on a specific rule can be considered. In the above example, layers 1 and 2 may be mapped to SRS resources #1 / #2 / #3 / #4 and transmitted according to a specific rule, and the specific rule for mapping is information that can be agreed upon in advance or set by the base station. For instance, layer 1 / layer 1 / layer 2 / layer 2 may be mapped to each SRS resource #1 / #2 / #3 / #4 respectively, or layer 1 / layer 2 / layer 1 / layer 2 may be mapped to each SRS resource respectively and transmitted. As another example, methods such as the terminal selecting the best N layer(s) or transmitting N precoded SRS determined by a pre-defined rule can also be considered. If the terminal selects the best N layer(s), this may be performed according to the terminal implementation, or a separate metric may be utilized for the selection. For instance, the terminal may utilize channel reciprocity to select a metric with a good correlation (e.g., SGCS) with the DL channel (the most recently measured). Alternatively, when using X-port SRS, the terminal may decode the compressed X-port UL precoder information received from the base station, beamform the value into the X-port SRS, and transmit it to the base station.

[0331] Meanwhile, although non-precoded and precoded SRS transmissions were supported in existing NR, they differ from the SRS usage described above. Existing non-precoded SRS transmissions were used to determine the base station's TPMI, RI, etc. (e.g., UL channel estimation), and the base station could perform UL scheduling by instructing the TPMI, RI, etc. determined based on the SRS as UL grant DCI. Existing precoded SRS was used for the purpose of non-codebook-based UL (e.g., PUSCH) transmission, and the base station could select a port for UL transmission through the precoded SRS. However, existing non-precoded / precoded SRS transmissions do not have the function of conveying information to a reconstructed precoder for monitoring as proposed in this disclosure.

[0332] Proposal 4

[0333] According to one embodiment, a UE-side monitoring method is proposed for UL precoder indication based on a two-sided model. Specifically, a method is described in which a terminal performs monitoring and executes subsequent actions (e.g., model / functionality switching, model / functionality update, model / functionality activate / deactivate, model transfer, model / functionality fine-tuning, functionality fallback), and / or a method in which the UE calculates a monitoring metric / output and reports it to the base station, and the base station executes subsequent actions.

[0334] At least one of the following three options may be used for UE-side monitoring.

[0335] (1) Option 1: Based on the explicit indication (eg, ground truth info) from BS

[0336] (2) Option 2: Based on precoded CSI-RS

[0337] (3) Option 3: Direct estimation of intermediate KPI (eg, SGCS, NMSE, etc.) or monitoring output

[0338] Specific examples for each of the above options are described.

[0339] (1) Option 1 refers to a case where the base station explicitly instructs the terminal with ground truth information for monitoring purposes. For the ground truth precoder instruction, the base station may use a predefined DL codebook or UL codebook as described in Option 3 of Proposal 3. Alternatively, a new codebook parameter with a higher resolution than the predefined codebooks may be used. Since such ground truth precoder instruction occupies a large payload, it may be limited to a specific rank (e.g., rank 1) and instructed to the terminal. In addition, the ground truth precoder information may be instructed via DCI, or instructed / set via RRC / MAC-CE, etc., considering the constraints on the payload. Also, information indicating which compressed UL precoder corresponds to the ground truth information (e.g., time stamp or linkage information) may be instructed to the terminal together, or the compressed UL precoder may be instructed together with the ground truth precoder (via RRC / MAC-CE). The terminal can calculate a monitoring metric / output based on the ground truth precoder instructed above and perform a subsequent action, and / or report the monitoring metric / output to the base station.

[0340] (2) In the case of Option 2, ground truth precoder information is precoded into CSI-RS and the base station instructs / transmits it to the terminal, and the terminal performs monitoring based on the (1 port) precoded CSI-RS. For example, the terminal can perform monitoring by utilizing the correlation between the precoded CSI-RS and the reconstruction UL precoder (e.g., via intermediate KPI). The terminal can perform subsequent actions by calculating monitoring metrics / outputs based on the precoded CSI-RS, and / or report the monitoring metrics / outputs to the base station. The precoded CSI-RS can be configured by linking to a monitoring report in which the monitoring metric / output is instructed as a report quantity in the CSI report Config, etc. In addition, the precoded CSI-RS mentioned above may be beamformed and transmitted to a single port; in this case, to indicate a UL precoder with multiple layers, a CSI-RS resource configured with multiple ports (e.g., X-ports) or multiple 1-port CSI-RS may be configured. The mapping relationship between the precoded CSI-RS port / resource and the layer of the actual UL precoder can be defined / configured similarly to the mapping relationship between the SRS port / resource and the actual UL precoder mentioned in Option 4 of Proposal 3. Meanwhile, the UL precoder (e.g., implicitly indicated to the terminal via precoded CSI-RS) rather than the compressed UL precoder (explicitly indicated via UL grant DCI)It may be desirable for the ground truth precoder to have higher accuracy / resolution. DCI has a relatively compact payload and is allocated with fewer resources than CSI-RS, whereas CSI-RS is allocated with various resources across a wider frequency band, and in particular, accuracy / resolution can be further improved by configuring / precoding the precoded CSI-RS for the above proposal in a UE-specific manner rather than configuring it in a UE group-specific or cell-specific manner.

[0341] (3) In the case of Option 3, similar to Option 1 of Proposal 3, it means that an AI / ML model for monitoring is separately provided in the terminal. The terminal can perform a subsequent action by utilizing information on the monitoring metric / output calculated based on the AI / ML model for monitoring, or by reporting the information to the base station so that the base station performs a subsequent action.

[0342] Meanwhile, Proposal 1-4 can also be commonly applied to CSI compression based on a two-sided model of DL. Extending this, when applied to UL functionality based on a two-sided model, the above model / performance monitoring methods can be extended and applied.

[0343] In addition, each of proposals 1-4 can be applied individually or in combination.

[0344] FIG. 14 is a diagram illustrating the operation of a terminal and a base station according to one embodiment.

[0345] Referring to FIG. 14, the terminal can transmit a UE capability report to the base station (A05). The UE capability report may include information about AI / ML models / functionality supported by the terminal and / or information about coherent transmission capabilities.

[0346] The terminal may receive one or more RRC signalings from the network (base station or other node) (A10). The RRC signalings may include receiving a trained AI / ML model from a base station or a training entity. The RRC signalings may include receiving UL RS (e.g., SRS) configuration information.

[0347] The terminal can receive activation for a configured / installed AI / ML model (e.g., UL precoder compression) from the base station (A15).

[0348] The terminal can transmit SRS to the base station (A20).

[0349] The terminal can receive compressed UL precoder information and UL transmission scheduling information from the base station (A25). The base station can calculate the UL precoder and rank based on the UL RS received from the terminal, and encode them to transmit / instruct the compressed UL precoder information and PUSCH scheduling information to the terminal. The terminal can decode the compressed UL precoder information received from the base station by inputting it into a terminal model (e.g., decoder).

[0350] The terminal can transmit an uplink signal, for example, PUSCH, based on decoded precoder information and scheduling information (A30).

[0351] The terminal performs monitoring based on at least some of the above proposals and can report information / results related to the monitoring to the base station (A35).

[0352] Based on monitoring information received from the terminal, the base station may perform LCM follow-up operations (e.g., model / functionality switching, model / functionality update, model / functionality activate / deactivate, model transfer, model / functionality fine-tuning, functionality fallback) or / or instruct / set the terminal (A40).

[0353] The operation of the terminal or base station described in the example of FIG. 14 can be performed by the device (200) of FIG. 3. For example, one or more processors (202) of the device (200) of FIG. 3 may be configured to perform an operation according to the terminal or base station. Furthermore, one or more memories (204) of the device (200) may store instructions for performing the method in the example of FIG. 14 or in various examples of the foregoing specification when executed by one or more processors (202).

[0354] According to the proposals of the present disclosure, through the smooth LCM operation of UL precoder instructions based on a two-sided model for improving UL throughput, stable system operation and the improvement and maintenance of AI / ML model performance can be achieved.

[0355] FIG. 15 illustrates the flow of a method performed by a terminal according to one embodiment. FIG. 15 is an example of implementation for at least some of the embodiments described above, and the previously described content may be referenced unless otherwise noted.

[0356] Referring to FIG. 15, the terminal can receive information about a compressed uplink precoder from a base station (B05).

[0357] The terminal can obtain a reconstructed uplink precoder through inference of the terminal based on information about the compressed uplink precoder (B10).

[0358] The terminal may transmit at least one uplink signal based on the restored uplink precoder (B15). The at least one uplink signal may include at least one of (i) a report on the restored uplink precoder, (ii) a reference signal precoded based on the restored uplink precoder, or (iii) information related to monitoring the performance of uplink precoder compression-restoration.

[0359] The report on the restored uplink precoder may include an index related to the restored uplink precoder determined based on the downlink or uplink codebook.

[0360] The report on the restored uplink precoder may include information regarding the time of reception of information on the compressed uplink precoder.

[0361] The above-mentioned precoded reference signal may include a sounding reference signal (SRS) precoded based on the above-mentioned restored uplink precoder.

[0362] The above-mentioned precoded reference signal is transmitted through a 1-port, and the number N of 1-port reference signal resources can be set to 1 or a maximum value.

[0363] The above-mentioned precoded reference signal is transmitted through one layer, and the one layer may be the first layer or a specific layer selected by the terminal.

[0364] The information related to the performance monitoring of the uplink precoder compression-recovery described above may include information on monitoring metrics calculated based on the recovered uplink precoder and reference precoder.

[0365] The above reference precoder can be obtained based on information regarding the Ground Truth Precoder received from the base station.

[0366] Information regarding the above-mentioned compressed uplink precoder can be received through DCI (downlink control information).

[0367] The reasoning of the above terminal can be performed based on an AI (artificial intelligence) model or functionality.

[0368] FIG. 16 illustrates the flow of a method performed by a base station according to one embodiment. FIG. 16 is an example of implementation for at least some of the embodiments described above, and the previously described content may be referenced unless otherwise noted.

[0369] Referring to Fig. 16, the base station can generate information about the compressed uplink precoder through inference (C05).

[0370] The base station can transmit information about the compressed uplink precode to the terminal (C10).

[0371] The base station may receive at least one uplink signal from the terminal (C15). The at least one uplink signal may include at least one of (i) a report on an uplink precoder restored by the terminal, (ii) a reference signal precoded based on the restored uplink precoder, or (iii) information related to monitoring the performance of uplink precoder compression-restoration.

[0372] The report on the restored uplink precoder may include an index related to the restored uplink precoder determined based on the downlink or uplink codebook.

[0373] The report on the restored uplink precoder may include information regarding the transmission time of the information on the compressed uplink precoder.

[0374] The above-mentioned precoded reference signal may include a sounding reference signal (SRS) precoded based on the above-mentioned restored uplink precoder.

[0375] The above-mentioned precoded reference signal is received through a 1-port, and the number N of 1-port reference signal resources can be set to 1 or a maximum value.

[0376] The above-mentioned precoded reference signal is received through one layer, and the one layer may be the first layer or a specific layer selected by the terminal.

[0377] The information related to the performance monitoring of the uplink precoder compression-recovery described above may include information on monitoring metrics calculated based on the recovered uplink precoder and reference precoder.

[0378] The above reference precoder can be indicated based on information about the Ground Truth Precoder transmitted from the base station.

[0379] Information regarding the above-mentioned compressed uplink precoder can be transmitted via DCI (downlink control information).

[0380] The inference of the above base station can be performed based on an AI (artificial intelligence) model or functionality.

[0381] 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.

[0382] It is obvious to those skilled in the art that the present disclosure may be embodied in other specific forms without departing from the features of the present disclosure. Accordingly, the foregoing detailed description should not be interpreted restrictively in all respects and should be considered exemplary. The scope of the present disclosure shall be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the present disclosure are included within the scope of the present disclosure.

[0383] 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 information about the compressed uplink precoder from the base station; Obtaining a reconstructed uplink precoder through inference of the terminal based on information regarding the compressed uplink precoder; and It includes transmitting at least one uplink signal based on the restored uplink precode above, and A method wherein the at least one uplink signal comprises (i) a report on the restored uplink precoder, (ii) a reference signal precoded based on the restored uplink precoder, or (iii) information related to monitoring the performance of uplink precoder compression-restoration.

2. In Paragraph 1, A method in which a report on the restored uplink precoder includes an index related to the restored uplink precoder determined based on a downlink or uplink codebook.

3. In Paragraph 1, A method in which a report on the restored uplink precoder includes information on the time of reception of information on the compressed uplink precoder.

4. In Paragraph 1, A method in which the precoded reference signal comprises a sounding reference signal (SRS) precoded based on the restored uplink precoder.

5. In Paragraph 1, The above-mentioned precoded reference signal is transmitted through port 1, and A method in which the number of 1-port reference signal resources N is set to 1 or a maximum value.

6. In Paragraph 1, The above-mentioned precoded reference signal is transmitted through one layer, and A method in which the above-mentioned 1 layer is the first layer or a specific layer selected by the terminal.

7. In Paragraph 1, A method in which information related to performance monitoring of the uplink precoder compression-recovery includes information on a monitoring metric calculated based on the recovered uplink precoder and the reference precoder.

8. In Paragraph 7, A method in which the above reference precoder is obtained based on information about the Ground Truth Precoder received from the base station.

9. In Paragraph 1, Information regarding the above-mentioned compressed uplink precoder is received through DCI (downlink control information), and A method in which the reasoning of the above terminal is performed based on an AI (artificial intelligence) model or functionality.

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

11. Regarding the device, At least one processor; and It includes at least one memory configured to store instructions that cause the at least one processor to perform operations by being executed by the at least one processor, and The operations of the above processor are, Receive information about the compressed uplink precoder from the base station; Obtaining a reconstructed uplink precode through inference of the device based on information regarding the compressed uplink precode; and It includes transmitting at least one uplink signal based on the restored uplink precode above, and A device comprising at least one uplink signal including (i) a report on the restored uplink precoder, (ii) a reference signal precoded based on the restored uplink precoder, or (iii) information related to monitoring the performance of uplink precoder compression-restoration.

12. In Paragraph 11, It further includes a transmitter and receiver, 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, Generate information about the compressed uplink precoder through inference of the above base station; Transmitting information about the above-mentioned compressed uplink precode to a terminal; and It includes receiving at least one uplink signal from the above terminal, and A method wherein the at least one uplink signal comprises (i) a report on an uplink precoder restored by the terminal, (ii) a reference signal precoded based on the restored uplink precoder, or (iii) information related to monitoring the performance of uplink precoder compression-restoration.

15. Regarding base stations, At least one processor; and It includes at least one memory configured to store instructions that cause the at least one processor to perform operations by being executed by the at least one processor, and The operations of the above processor are, Generate information about the compressed uplink precoder through inference of the above base station; Transmitting information about the above-mentioned compressed uplink precode to a terminal; and It includes receiving at least one uplink signal from the above terminal, and The base station, wherein the at least one uplink signal comprises (i) a report on an uplink precoder restored by the terminal, (ii) a reference signal precoded based on the restored uplink precoder, or (iii) information related to monitoring the performance of uplink precoder compression-restoration.

Images