Method and apparatus for transmitting / receiving uplink control information in wireless communication system

Abstract

A method and an apparatus for transmitting / receiving uplink control information in a wireless communication system are disclosed. A method according to an embodiment of the present disclosure may comprise the steps of: receiving configuration information related to a UCI report from a base station; and transmitting the UCI report to the base station based on the configuration information.

Description

TECHNICAL FIELD

[0001] The disclosure relates to a wireless communication system, and more particularly, to a method and device for transmitting and receiving uplink control information (UCI) in a wireless communication system. BACKGROUND

[0002] A mobile communication system has been developed to provide a voice service while guaranteeing the mobility of a user. However, the mobile communication system has been expanded to data traffic as well as voice traffic, and currently, an explosive increase in traffic has led to a shortage of resources, and users have demanded faster services, and thus a more advanced mobile communication system has been required.

[0003] The overall requirements of the next-generation mobile communication system should be able to support the accommodation of explosive data traffic, a significant increase in transmission rate per user, the accommodation of a significantly increased number of connected devices, a very low end-to-end latency, and high energy efficiency. To this end, various technologies such as dual connectivity, massive multiple input multiple output (massive MIMO), in-band full duplex, non-orthogonal multiple access (NOMA), ultra-wideband support, device networking, etc. have been researched. SUMMARY

[0004] TECHNICAL PROBLEM

[0005] The technical object of the disclosure is to provide a method and device for transmitting and receiving UCI considering a change in dynamic parameter setting.

[0006] The technical objects of the disclosure to be achieved are not limited to the above-mentioned technical objects, and other technical objects not mentioned herein can be clearly understood by a person of ordinary skill in the art from the following description.

[0007] TECHNICAL SOLUTION

[0008] According to one aspect of the disclosure, a method performed by a user equipment (UE) in a wireless communication system can include receiving, from a base station, configuration information related to an uplink control information (UCI) report, and transmitting, to the base station, the UCI report based on the configuration information. Among a plurality of parameters related to the UCI report in the configuration information, a set value for at least one parameter can be changed without reconfiguring the configuration information.

[0009] According to an additional aspect of the disclosure, a method performed by a base station in a wireless communication system can include transmitting, to a user equipment (UE), configuration information related to an uplink control information (UCI) report, and receiving, from the UE, the UCI report based on the configuration information. Among a plurality of parameters related to the UCI report in the configuration information, a set value for at least one parameter can be changed without reconfiguring the configuration information.

[0010] TECHNICAL EFFECT

[0011] According to embodiments of the disclosure, signaling overhead for configuring dynamic parameters related to UCI reporting can be reduced.

[0012] Additionally, according to embodiments of the disclosure, latency for changing dynamic parameter configuration related to UCI reporting can be reduced.

[0013] Additionally, according to embodiments of the disclosure, even if multiple functionalities / models are configured for UCI reporting, flexible UCI reporting operation can be appropriately performed according to circumstances. Additionally, according to embodiments of the disclosure, signaling overhead of downlink control information for scheduling PUSCH transmission through multiple panels can be reduced.

[0014] Effects achievable by the disclosure are not limited to the above-mentioned effects, and other effects not described herein can be clearly understood by those skilled in the art from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0015] The accompanying drawings, which are included as part of the detailed description for understanding the disclosure, provide embodiments of the disclosure and describe technical features of the disclosure through the detailed description.

[0016] Figure 1 A structure of a wireless communication system to which the disclosure can be applied is illustrated.

[0017] Figure 2 A frame structure in a wireless communication system to which the disclosure can be applied is illustrated.

[0018] Figure 3 A resource grid of a wireless communication system to which the disclosure can be applied is illustrated.

[0019] Figure 4 A physical resource block in a wireless communication system to which the disclosure can be applied is illustrated.

[0020] Figure 5 A slot structure in a wireless communication system to which the disclosure can be applied is illustrated.

[0021] Figure 6 Physical channels used in a wireless communication system to which the disclosure can be applied, and a general signal transmission and reception method using the physical channels are illustrated.

[0022] Figure 7 A classification of artificial intelligence is illustrated.

[0023] Figure 8 A feedforward neural network is illustrated.

[0024] Figure 9 A recurrent neural network is illustrated.

[0025] Figure 10 A convolutional neural network is exemplified.

[0026] Figure 11 An autoencoder is exemplified.

[0027] Figure 12 A functional framework for AI operations is exemplified.

[0028] Figure 13 is a diagram exemplifying segmentation AI inference.

[0029] Figure 14 Application of the functional framework in a wireless communication system is exemplified.

[0030] Figure 15 Application of the functional framework in a wireless communication system is exemplified.

[0031] Figure 16 Application of the functional framework in a wireless communication system is exemplified.

[0032] Figure 17 is a diagram exemplifying a signaling procedure between a network and a UE for a method for transmitting and receiving uplink control information according to an embodiment of the disclosure.

[0033] Figure 18 is a diagram exemplifying UE operation for a method for transmitting and receiving uplink control information according to an embodiment of the disclosure.

[0034] Figure 19 is a diagram exemplifying base station operation for a method for transmitting and receiving uplink control information according to an embodiment of the disclosure.

[0035] Figure 20 is a block diagram of a wireless communication device according to an embodiment of the disclosure. DETAILED DESCRIPTION

[0036] Hereinafter, embodiments according to the disclosure will be described in detail with reference to the accompanying drawings. The detailed description disclosed through the accompanying drawings is to describe exemplary embodiments of the disclosure and is not intended to represent the only embodiments in which the disclosure can be implemented. The following detailed description includes specific details to provide a thorough understanding of the disclosure. However, it will be apparent to those skilled in the art that the disclosure can be implemented without these specific details.

[0037] In some cases, known structures and devices can be omitted, or can be shown in the form of block diagrams in order to facilitate the prevention of obscuring the concept of the disclosure based on the core function of each structure and device.

[0038] In the disclosure, when an element is referred to as being "connected", "combined", or "linked" to another element, it can include an indirect connection relationship between the other element and a further element in addition to a direct connection relationship. Also, in the disclosure, the term "comprising" or "having" specifies the existence of the mentioned features, steps, operations, components, and / or elements, but does not exclude the existence or addition of one or more other features, stages, operations, components, elements, and / or groups thereof.

[0039] In the disclosure, the terms such as "first", "second", etc. are used only to distinguish one element from another element and are not used to limit the elements, unless otherwise specified, and do not limit the order or importance between the elements, etc. Therefore, within the scope of the disclosure, a first element in an embodiment can be referred to as a second element in another embodiment, and likewise, a second element in an embodiment can be referred to as a first element in another embodiment.

[0040] The terms used in the disclosure are intended to describe specific embodiments, not to limit the claims. As used in the description of the embodiments and the appended claims, the singular form is intended to include the plural form, unless the context clearly dictates otherwise. The term "and / or" used in the disclosure can refer to one of the relevant listed items, or mean that it refers to and includes any and all possible combinations of two or more of them. In addition, unless otherwise specified, " / " and "and / or" between words in the disclosure have the same meaning.

[0041] The disclosure describes a wireless communication network or a wireless communication system, and operations performed in the wireless communication network can be performed in a process in which a device (e.g., a base station) controlling the network and transmitting or receiving a signal controls the corresponding wireless communication network, or can be performed in a process in which a terminal associated with the corresponding wireless network transmits or receives a signal between the network or the terminal.

[0042] In the disclosure, a transmission or reception channel includes the meaning of transmitting or receiving information or a signal through a corresponding channel. For example, transmitting a control channel means transmitting control information or a control signal through a control channel. Similarly, transmitting a data channel means transmitting data information or a data signal through a data channel.

[0043] Hereinafter, downlink (DL) means communication from a base station to a terminal, and uplink (UL) means communication from a terminal to a base station. In the downlink, a transmitter can be a part of a base station, and a receiver can be a part of a terminal. In the uplink, a transmitter can be a part of a terminal, and a receiver can be a part of a base station. The base station can be expressed as a first communication device, and the terminal can be expressed as a second communication device. The base station (BS) can be replaced with terms such as a fixed station, a Node B, an eNB (evolved Node B), a gNB (next-generation Node B), a BTS (base transceiver system), an access point (AP), a network (5G network), an AI (artificial intelligence) system / module, an RSU (roadside unit), a robot, a drone (UAV: unmanned aerial vehicle), an AR (augmented reality) device, a VR (virtual reality) device, etc. In addition, the terminal can be fixed as well as mobile, and can be replaced with terms such as a UE (user equipment), a MS (mobile station), a UT (user terminal), a MSS (mobile subscriber station), a SS (subscriber station), an AMS (advanced mobile station), a WT (wireless terminal), an MTC (machine type communication) device, an M2M (machine to machine) device, a D2D (device to device) device, a vehicle, an RSU (roadside unit), a robot, an AI (artificial intelligence) module, a drone (UAV: unmanned aerial vehicle), an AR (augmented reality) device, a VR (virtual reality) device, etc.

[0044] The following description can be used for various radio access systems such as CDMA, FDMA, TDMA, OFDMA, SC-FDMA, etc. CDMA can be implemented by such as UTRA (Universal Terrestrial Radio Access), or CDMA2000. TDMA can be implemented by such as a radio technology of GSM (Global System for Mobile communication) / GPRS (General Packet Radio Service) / EDGE (Enhanced Data Rates for GSM Evolution). OFDMA can be implemented by such as IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802-20, E-UTRA (Evolved UTRA), etc. UTRA is a part of UMTS (Universal Mobile Telecommunications System). 3GPP (Third Generation Partnership Project) LTE (Long Term Evolution) is a part of E-UMTS (Evolved UMTS) using E-UTRA, and LTE-A (Advanced) / LTE-A pro is a high-speed version of 3GPP LTE. 3GPP NR (New Radio or New Radio Access Technology) is a high-speed version of 3GPP LTE / LTE-A / LTE-A pro.

[0045] For a clearer description, description is made based on a 3GPP communication system (e.g., LTE-A, NR), but the technical idea of the disclosure is not limited thereto. LTE means a technology after Release 8 of 3GPP TS (Technical Specification) 36.xxx. Specifically, LTE technology in or after Release of 3GPP TS 36.xxx is referred to as LTE-A, and LTE technology in or after Release 13 of 3GPP TS 36.xxx is referred to as LTE-A pro. 3GPP NR means a technology in or after Release of TS 38.xxx. LTE / NR can be referred to as a 3GPP system. "xxx" means a detailed number of a standard document. LTE / NR can be generally referred to as a 3GPP system. For background technology, terms, abbreviations, etc. used to describe the disclosure, matters described in standard documents disclosed before the disclosure can be referred to. For example, the following documents can be referred to.

[0046] For 3GPP LTE, TS 36.211 (Physical Channels and Modulation), TS 36.212 (Reuse and Channel Coding), TS 36.213 (Physical Layer Procedures), TS 36.300 (Overall Description), TS 36.331 (Radio Resource Control) can be referred to.

[0047] For 3GPP NR, TS 38.211 (Physical Channels and Modulation), TS 38.212 (Reuse and Channel Coding), TS 38.213 (Physical Layer Procedures for Control), TS 38.214 (Physical Layer Procedures for Data), TS 38.300 (NR and NG-RAN (New Generation Radio Access Network) Overall Description), TS 38.331 (Radio Resource Control Protocol Specification) can be referred to.

[0048] Abbreviations of terms that can be used in the disclosure are defined as follows.

[0049] -BM: Beam Management

[0050] -CQI: Channel Quality Indicator

[0051] -CRI: Channel State Information-Reference Signal Resource Indicator

[0052] -CSI: Channel State Information

[0053] -CSI-IM: Channel State Information-Interference Measurement

[0054] -CSI-RS: Channel State Information-Reference Signal

[0055] -DMRS: Demodulation Reference Signal

[0056] -FDM: Frequency Division Multiplexing

[0057] - FFT: Fast Fourier Transform

[0058] - IFDMA: Interleaved Frequency Division Multiple Access

[0059] - IFFT: Inverse Fast Fourier Transform

[0060] - L1-RSRP: Layer 1 Reference Signal Received Power

[0061] - L1-RSRQ: Layer 1 Reference Signal Received Quality

[0062] - MAC: Medium Access Control

[0063] - NZP: Non-Zero Power

[0064] - OFDM: Orthogonal Frequency Division Multiplexing

[0065] - PDCCH: Physical Downlink Control Channel

[0066] - PDSCH: Physical Downlink Shared Channel

[0067] - PMI: Precoding Matrix Indicator

[0068] - RE: Resource Element

[0069] - RI: Rank Indicator

[0070] - RRC: Radio Resource Control

[0071] - RSSI: Received Signal Strength Indicator

[0072] - Rx: Reception

[0073] - QCL: Quasi Co-Location

[0074] - SINR: Signal to Interference and Noise Ratio

[0075] - SSB (or SS / PBCH Block): Synchronization Signal Block (including PSS (Primary Synchronization Signal), SSS (Secondary Synchronization Signal) and PBCH (Physical Broadcast Channel))

[0076] - TDM: Time Division Multiplexing

[0077] - TRP: Transmission and Reception Point

[0078] - TRS: Tracking Reference Signal

[0079] - Tx: Transmission

[0080] - UE: User Equipment

[0081] - ZP: Zero Power

[0082] Overall system

[0083] As more communication devices require higher capacity, there has been a demand for improved mobile broadband communication compared to existing radio access technologies (RATs). Also, massive MTC (machine type communication) that provides various services anytime anywhere by connecting a plurality of devices and things is one of main issues to be considered in a next-generation communication system. Also, communication system design considering services / terminals sensitive to reliability and latency is discussed. Accordingly, introduction of a next-generation RAT considering eMBB (enhanced mobile broadband communication), mMTC (massive MTC), URLLC (ultra-reliable and low-latency communication), etc. is discussed, and for convenience, corresponding technologies are referred to as NR in the present disclosure. NR is an expression representing an example of a 5G RAT.

[0084] A new RAT system including NR uses an OFDM transmission method or a transmission method similar thereto. The new RAT system can follow OFDM parameters different from those of LTE. Alternatively, the new RAT system follows the parameters of the existing LTE / LTE-A as they are, but can support a wider system bandwidth (for example, 100 MHz). Alternatively, one cell can support a plurality of numerologies. In other words, terminals operating according to different numerologies can coexist in one cell.

[0085] A numerology corresponds to one subcarrier spacing in a frequency domain. Different numerologies can be defined as a reference subcarrier spacing is scaled by an integer N.

[0086] Figure 1 A structure of a wireless communication system to which the present disclosure is applicable is exemplified.

[0087] Reference Figure 1 , an NG-RAN is configured with gNBs providing control plane (RRC) protocol terminations for NG-RA (NG Radio Access) user plane (i.e., new AS (Access Stratum) sublayer / PDCP (Packet Data Convergence Protocol) / RLC (Radio Link Control) / MAC / PHY) and UE. The gNBs are interconnected with each other through the Xn interface. Also, the gNBs are connected to a NGC (Next Generation Core) through the NG interface. More specifically, the gNBs are connected to an AMF (Access and Mobility Management Function) through the N2 interface and to a UPF (User Plane Function) through the N3 interface.

[0088] Figure 2 A frame structure in a wireless communication system to which the present disclosure is applicable is exemplified.

[0089] NR systems can support multiple parameter sets. These parameter sets can be defined by subcarrier spacing and cyclic prefix (CP) overhead. Multiple subcarrier spacings can be derived by scaling the basic (reference) subcarrier spacing by an integer N (or μ). Furthermore, while it is assumed that very low subcarrier spacings are not used at very high carrier frequencies, the parameter set used can be selected independently of the frequency band. Moreover, various frame structures based on multiple parameter sets can be supported in NR systems.

[0090] The OFDM parameter sets and frame structures that can be considered in an NR system are described below. Several OFDM parameter sets supported in an NR system can be defined as shown in Table 1 below.

[0091] [Table 1]

[0092] μ Delta f = 2 μ • 15 [kHz]] CP 0 15 Normal 1 30 Normal 2 60 Normal, extended 3 120 Normal 4 240 Normal

[0093] NR supports multiple sets of parameters (or subcarrier spacing (SCS)) to support various 5G services. For example, a SCS of 15kHz supports wide-area coverage of traditional cellular bands; a SCS of 30kHz / 60kHz supports dense urban areas, lower latency, and wider carrier bandwidth; and a SCS of 60kHz or higher supports bandwidths exceeding 24.25GHz to overcome phase noise.

[0094] The NR band is defined as frequency ranges of two types (FR1, FR2). FR1 and FR2 can be configured as shown in Table 2 below. Additionally, FR2 can refer to millimeter wave (mmW).

[0095] [Table 2]

[0096] Frequency range designation Corresponding frequency range Subcarrier spacing FR1 410 MHz - 7125 MHz 15, 30, 60 kHz FR2 24250 MHz - 52600 MHz 60, 120, 240 kHz

[0097] Regarding the frame structure in the NR system, the size of various fields in the time domain is expressed as T. c =1 / (Δf) max ·N f A multiple of the time unit. Here, Δf max 480·10 3 Hz, and N f The value is 4096. Downlink and uplink transmissions are configured (organized) to have a duration T. f =1 / Δf max N f / 100)·T c A radio frame of 10ms. Here, the radio frame is configured with 10 subframes, each with a T... sf =(Δf max N f / 1000)·T c= 1 ms duration. In this case, there can be one set of frames for uplink and one set of frames for downlink. Furthermore, the transmission in the i-th uplink frame from a terminal should be earlier than the beginning of the corresponding downlink frame in the corresponding terminal by T TA = (N TA + i)T TA,offset )T c start. For a subcarrier spacing configuration μ, the slots are numbered in increasing order of n s μ ∈ {0,..., N slot subframe,μ - 1} in a subframe and in increasing order of n s ,f μ ∈ {0,..., N slot frame,μ - 1} in a radio frame. One slot is configured with N symb slot OFDM symbols consecutively, and N symb slot is determined depending on the CP. The beginning of the slot n s μ in a subframe is aligned in time with the beginning of the OFDM symbol n s μ N symb slot in the same subframe. All terminals can not perform transmission and reception at the same time, which means that all OFDM symbols of a downlink slot or an uplink slot can not be used.

[0098] Table 3 represents the number of OFDM symbols per slot (N symb slot ), the number of slots per radio frame (N slot frame,μ ), and the number of slots per subframe (N slot subframe,μ ) in normal CP, and Table 4 represents the number of OFDM symbols per slot, the number of slots per radio frame, and the number of slots per subframe in extended CP.

[0099] [Table 3]

[0100] μ N symb slot ]]> N slot frame,μ ]]> N slot subframe,μ ]]> 0 14 10 1 1 14 20 2 2 14 40 4 3 14 80 8 4 14 160 16

[0101] [Table 4]

[0102] μ N symb slot ]]> N slot frame,μ ]]> N slot subframe,μ ]]> 2 12 40 4

[0103] Figure 2 is an example of μ = 2 (SCS of 60 kHz), see Table 3, 1 subframe can include 4 slots. As Figure 2The number of slots that can be included in one subframe is defined as in Table 3 or Table 4. In addition, the mini-slot can include 2, 4, or 7 symbols or more or less symbols.

[0104] With respect to physical resources in the NR system, an antenna port, a resource grid, a resource element, a resource block, a carrier part, etc. can be considered. Hereinafter, the physical resources that can be considered in the NR system will be described in detail.

[0105] First, with respect to the antenna port, the antenna port is defined such that a channel carrying a symbol in the antenna port can be inferred from a channel carrying other symbols in the same antenna port. When a large-scale property of a channel in which a symbol in one antenna port is carried can be inferred from a channel in which a symbol of another antenna port is carried, it can be said that 2 antenna ports are in a QC / QCL (Quasi Co-Location or Quasi Co-located) relationship. In this case, the large-scale property includes at least one of a delay spread, a Doppler spread, a frequency shift, an average received power, and a reception timing.

[0106] Figure 3 A resource grid in a wireless communication system to which the disclosure can be applied is exemplified.

[0107] Referring to Figure 3 , a resource grid is exemplarily described as being configured with N RB μ N sc RB subcarriers in the frequency domain, and one subframe is configured with 14·2 μ OFDM symbols, but is not limited thereto. In the NR system, a transmitted signal is described by one or more resource grids of N μ N symb (μ) OFDM symbols and N RB μ N sc RB subcarriers. Here, N RB μ ≤ N RB max,μ . N RB max,μ denotes a maximum transmission bandwidth, which can be different between uplink and downlink and between numerologies. In this case, one resource grid can be configured per μ and antenna port p. Each element of the resource grid for μ and antenna port p is called a resource element and is uniquely identified by an index pair (k, l'). Here, k = 0,..., N RB μ N sc RB-1 is an index in the frequency domain and l' = 0,..., 2 μ N symb (μ) -1 refers to a symbol position in a subframe. When referring to a resource element in a time slot, the index pair (k, l) is used. Here, l = 0,..., N symb μ -1. The resource element pair (k, l') for μ and antenna port p corresponds to the complex value a k,l' (p,μ) When there is no risk of confusion or when no specific antenna port or numerology is specified, the indices p and μ can be dropped, and the complex value can be a k,l' (p) or a k,l' In addition, a resource block (RB) is defined as N sc RB = 12 consecutive subcarriers in the frequency domain.

[0108] A point serves as a common reference point for the resource block grid and is obtained as follows.

[0109] - offsetToPointA for a primary cell (PCell) downlink indicates a frequency offset between point A and the lowest subcarrier of the lowest resource block overlapping with an SS / PBCH block used by the terminal for initial cell selection. It is expressed in units of resource blocks, assuming a subcarrier spacing of 15 kHz for FR1 and 60 kHz for FR2.

[0110] - absoluteFrequencyPointA indicates the frequency location of point A, expressed in ARFCN (absolute radio frequency channel number).

[0111] For a subcarrier spacing configuration μ, the common resource blocks are numbered from 0 upwards in the frequency domain. The center of subcarrier 0 of common resource block 0 for subcarrier spacing configuration μ is the same as "point A". The common resource block number n CRB μ The relationship between the subcarrier spacing configuration μ and the resource element (k, l) is given as Equation 1 below.

[0112] [Equation 1]

[0113]

[0114] In Equation 1, k is defined with respect to point A such that k = 0 corresponds to the subcarrier centered at point A. The physical resource blocks are numbered from 0 to N BWP,i size,μ - 1 in the frequency domain and i is the number of the BWP. The physical resource block n PRBand public resource block n CRB The relationship between them is given by Equation 2.

[0115] [Equation 2]

[0116]

[0117] N BWP,i start,μ It is a public resource block relative to public resource block 0 in BWP.

[0118] Figure 4 Examples of physical resource blocks in wireless communication systems that can utilize this disclosure are provided. Furthermore, Figure 5 The time slot structure in a wireless communication system to which this disclosure can be applied is illustrated.

[0119] Reference Figure 4 and Figure 5 A time slot comprises multiple symbols in the time domain. For example, for a normal CP, one time slot includes 7 symbols, but for an extended CP, one time slot includes 6 symbols.

[0120] A carrier comprises multiple subcarriers in the frequency domain. An RB (Resource Block) is defined as multiple (e.g., 12) consecutive subcarriers in the frequency domain. A BWP (Bandwidth Component) is defined as multiple consecutive (physical) resource blocks in the frequency domain and can correspond to a set of parameters (e.g., SCS, CP length, etc.). A carrier can include up to N (e.g., 5) BWPs. Data communication can be performed through active BWPs, and only one BWP can be active for a single terminal. In the resource grid, each element is called a resource element (RE) and can be mapped to a complex number of symbols.

[0121] In NR systems, each component carrier (CC) can support up to 400MHz. If a terminal operating in such a wideband CC always operates with the radio frequency (FR) chip turned on for the entire CC, terminal battery consumption may increase. Alternatively, when considering multiple application scenarios operating in a wideband CC (e.g., eMBB, URLLC, Mmtc, V2X, etc.), different sets of parameters (e.g., subcarrier spacing, etc.) can be supported in each band of the corresponding CC. Alternatively, each terminal may have different capabilities for the maximum bandwidth. With this in mind, the base station can instruct the terminal to operate only in a portion of the bandwidth, rather than in the full bandwidth of the wideband CC, and for convenience, the corresponding portion of the bandwidth is defined as the bandwidth portion (BWP). The BWP can be configured with consecutive RBs on the frequency axis and can correspond to a set of parameters (e.g., subcarrier spacing, CP length, slot / microslot duration).

[0122] Meanwhile, even in one CC configured to a terminal, the base station can configure multiple BWPs. For example, a BWP occupying a relatively small frequency domain can be configured in a PDCCH monitoring slot, and a PDSCH indicated by a PDCCH can be scheduled in a larger BWP. Alternatively, when UEs are congested in a specific BWP, other BWPs can be configured for some terminals for load balancing. Alternatively, considering frequency domain inter-cell interference cancellation between neighboring cells, etc., some middle frequency spectrum of the full bandwidth can be excluded, and BWPs on both edges can be configured in the same slot. In other words, the base station can configure at least one DL / UL BWP to a terminal associated with a wideband CC. The base station can activate at least one of the configured DL / UL BWPs at a specific time (through L1 signaling or MAC CE (Control Element) or RRC signaling, etc.). In addition, the base station can indicate switching to other configured DL / UL BWPs (through L1 signaling or MAC CE or RRC signaling, etc.). Alternatively, based on a timer, when the timer value expires, switching to a determined DL / UL BWP can be made. Here, the activated DL / UL BWP is defined as an active DL / UL BWP. However, before the terminal performs an initial access procedure or sets up an RRC connection, a configuration on the DL / UL BWP can not be received, so the DL / UL BWP assumed by the terminal in these cases is defined as an initial active DL / UL BWP.

[0123] Figure 6 A physical channel used in a wireless communication system to which the disclosure can be applied and a general signal transmission and reception method using the same are exemplified.

[0124] In a wireless communication system, a terminal receives information from a base station through a downlink and transmits information to the base station through an uplink. The information transmitted and received by the base station and the terminal includes data and various control information, and there are various physical channels according to the type / use of the information they transmit and receive.

[0125] When a terminal is turned on or newly enters a cell, it performs an initial cell search including synchronization with a base station, etc. (S601). For the initial cell search, the terminal can synchronize with the base station by receiving a primary synchronization signal (PSS) and a secondary synchronization signal (SSS) from the base station and acquire information such as a cell identifier (ID), etc. Then, the terminal can acquire broadcast information in the cell by receiving a physical broadcast channel (PBCH) from the base station. Meanwhile, the terminal can check a downlink channel state by receiving a downlink reference signal (DL RS) in the initial cell search stage.

[0126] The terminal that completes the initial cell search can acquire more detailed system information by receiving a physical downlink control channel (PDCCH) and a physical downlink shared channel (PDSCH) according to information carried in the PDCCH (S602).

[0127] Meanwhile, when the terminal first accesses to the base station or does not have a radio resource for signal transmission, it can perform a random access (RACH) procedure to the base station (S603 to S606). For the random access procedure, the terminal can transmit a specific sequence as a preamble through a physical random access channel (PRACH) (S603 and S605), and can receive a response message to the preamble through a PDCCH and a corresponding PDSCH (S604 and S606). A contention-based RACH can additionally perform a contention resolution procedure.

[0128] The terminal that performs the above-described procedure can perform PDCCH / PDSCH reception (S607) and PUSCH (physical uplink shared channel) / PUCCH (physical uplink control channel) transmission (S608) as a general uplink / downlink signal transmission procedure. Specifically, the terminal receives downlink control information (DCI) through a PDCCH. Here, the DCI includes control information such as resource allocation information for the terminal, and the format varies according to its use purpose.

[0129] Meanwhile, control information transmitted by the terminal to the base station through an uplink or received by the terminal from the base station includes a downlink / uplink ACK / NACK (acknowledgement / non-acknowledgement) signal, a CQI (channel quality indicator), a PMI (precoding matrix indicator), an RI (rank indicator), etc. For a 3GPP LTE system, the terminal can transmit the above-described control information of CQI / PMI / RI, etc. through a PUSCH and / or a PUCCH.

[0130] Table 5 represents an example of a DCI format in an NR system.

[0131] [Table 5]

[0132]

[0133] Referring to Table 5, the DCI formats 0_0, 0_1, and 0_2 can include resource information (e.g., UL / SUL (Supplementary UL), frequency resource allocation, time resource allocation, frequency hopping, etc.), information related to a transport block (TB) (e.g., MCS (Modulation Coding and Scheme), NDI (New Data Indicator), RV (Redundancy Version), etc.), information related to HARQ (Hybrid- Automatic Repeat and Request) (e.g., process number, DAI (Downlink Assignment Index), PDSCH-HARQ feedback timing, etc.), information related to multiple antennas (e.g., DMRS sequence initialization information, antenna port, CSI request, etc.), power control information related to scheduling of PUSCH (e.g., PUSCH power control, etc.), and control information included in each DCI format can be predefined.

[0134] The DCI format 0_0 is used to schedule PUSCH in one cell. Information included in the DCI format 0_0 is CRC (Cyclic Redundancy Check) scrambled by C-RNTI (Cell Radio Network Temporary Identifier) or CS-RNTI (Configured Scheduling RNTI) or MCS-C-RNTI (Modulation Coding Scheme Cell RNTI) and transmitted.

[0135] The DCI format 0_1 is used to indicate scheduling of one or more PUSCHs or to configure grant (CG) downlink feedback information to a terminal in one cell. Information included in the DCI format 0_1 is scrambled by C-RNTI or CS-RNTI or SP-CSI-RNTI (Semi-Persistent CSI RNTI) or MCS-C-RNTI and transmitted.

[0136] The DCI format 0_2 is used to schedule PUSCH in one cell. Information included in the DCI format 0_2 is scrambled by C-RNTI or CS-RNTI or SP-CSI-RNTI or MCS-C-RNTI and transmitted.

[0137] Next, the DCI formats 1_0, 1_1, and 1_2 can include resource information (e.g., frequency resource allocation, time resource allocation, VRB (Virtual Resource Block)-PRB (Physical Resource Block) mapping, etc.), information related to a transport block (TB) (e.g., MCS, NDI, RV, etc.), information related to HARQ (e.g., process number, DAI, PDSCH-HARQ feedback timing, etc.), information related to multiple antennas (e.g., antenna port, TCI (Transmission Configuration Indicator), SRS (Sounding Reference Signal) request, etc.), information related to PUCCH with respect to scheduling of PDSCH (e.g., PUCCH power control, PUCCH resource indicator, etc.), and control information included in each DCI format can be predefined.

[0138] The DCI format 1_0 is used to schedule a PDSCH in one DL cell. The information included in the DCI format 1_0 is CRC scrambled by a C-RNTI or a CS-RNTI or a MCS-C-RNTI and transmitted.

[0139] The DCI format 1_1 is used to schedule a PDSCH in one cell. The information included in the DCI format 1_1 is CRC scrambled by a C-RNTI or a CS-RNTI or a MCS-C-RNTI and transmitted.

[0140] The DCI format 1_2 is used to schedule a PDSCH in one cell. The information included in the DCI format 1_2 is CRC scrambled by a C-RNTI or a CS-RNTI or a MCS-C-RNTI and transmitted.

[0141] Artificial intelligence (AI) operations

[0142] With the advancement of artificial intelligence / machine learning (AI / ML) technology, nodes and UEs in a wireless communication network are becoming more intelligent / advanced. Specifically, due to the intelligence of the network / base station, it is expected that various network / base station decision parameter values (e.g., transmission / reception power of each base station, transmission power of each UE, precoder / beam of the base station / UE, time / frequency resource allocation for each UE, duplex method of each base station, etc.) will be quickly optimized and derived / applied according to various environmental parameters (e.g., distribution / location of base stations, distribution / location / materials of buildings / furniture, etc., location / moving direction / speed of UEs, climate information, etc.). Following this trend, many standardization organizations (e.g., 3GPP, O-RAN) are considering introducing, and are also actively conducting research on this.

[0143] The AI-related descriptions and operations described below can be applied in conjunction with the methods proposed in the present disclosure described later, or can be supplemented to clarify the technical features of the methods proposed in the present disclosure.

[0144] Figure 7 A classification of artificial intelligence is exemplified.

[0145] Referring to Figure 7 Artificial intelligence (AI) corresponds to all automation in which a machine can replace work that should be done by a human.

[0146] Machine learning (ML) refers to a technology in which a machine learns a pattern for decision-making from its own data without explicit programming rules.

[0147] Deep learning is a model based on artificial neural networks that allows a machine to perform feature extraction and decision making from unstructured data at one time. The algorithm relies on a multi-layered network of interconnected nodes for feature extraction and transformation, inspired by biological nervous systems or neural networks. Common deep learning network architectures include deep neural networks (DNNs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs).

[0148] AI (or AI / ML) can be narrowly referred to as artificial intelligence based on deep learning, but the present disclosure is not limited thereto. That is, in the present disclosure, AI (or AI / ML) can collectively refer to an automated technology applied to an intelligent machine (e.g., a UE, a RAN, a network node, etc.) that can perform a task like a human.

[0149] AI (or AI / ML) can be classified according to various criteria as follows.

[0150] 1. Offline / online learning

[0151] a) Offline learning

[0152] Offline learning follows a sequential process of database collection, learning, and prediction. In other words, collection and learning can be performed offline, and a completed program can be installed in the field and used for prediction work. For offline learning, the system does not perform incremental learning, performs learning using all available collected data, and is applied to the system without further learning. If learning about new data is necessary, learning can be started again using all new data.

[0153] b) Online learning

[0154] This refers to a method of gradually improving performance by incrementally adding learning using recently generated real-time data, which takes advantage of the fact that data that can be used for learning continues to be generated through the Internet, performing learning in real time for each (bundle) specific data collected online, thereby allowing the system to quickly adapt to changing data.

[0155] An AI system is constructed using only online learning, so learning can be performed using only real-time generated data, or after offline learning is performed using a predetermined data set, additional learning can be performed using additional real-time data (online + offline learning).

[0156] 2. Classification according to AI / ML framework concepts

[0157] a) Centralized learning

[0158] In centralized learning, training data collected from multiple different nodes is reported to a centralized node, where all data resources / storage / learning (e.g., supervised learning, unsupervised learning, reinforcement learning, etc.) are performed in one centralized node.

[0159] b) Federated learning

[0160] Federated learning is a collective model built on data existing across distributed data owners. Instead of collecting data into a model, an AI / ML model is imported into a data source, thereby allowing local nodes / individual devices to collect data and train their own model copies, thus eliminating the need to report source data to a central node. In federated learning, the parameters / weights of the AI / ML model can be sent back to the centralized node to support general model training. Federated learning has advantages in improved computing speed and information security. In other words, the process of uploading personal data to a central server is unnecessary, thereby preventing the leakage and misuse of personal information.

[0161] c) Distributed learning

[0162] Distributed learning refers to the concept of scaling and distributing the machine learning process across a cluster of nodes. A training model is split and shared across multiple nodes operating simultaneously to accelerate model training.

[0163] 3. Classification according to learning method

[0164] a) Supervised learning

[0165] Supervised learning is a machine learning task that aims to learn a mapping function from input to output given a labeled dataset. The input data is called training data and has known labels or results. Examples of supervised learning are as follows.

[0166] - Regression: Linear regression, logistic regression

[0167] - Distance-based algorithm: k-Nearest Neighbors (KNN)

[0168] - Decision tree algorithm: Classification and Regression Tree (CART)

[0169] - Support Vector Machine (SVM)

[0170] - Bayesian algorithm: Naive Bayes

[0171] - Ensemble algorithm: Extreme Gradient Boosting, Label: Random Forest

[0172] Supervised learning can be further grouped into regression and classification problems, where classification is to predict a label and regression is to predict a quantity.

[0173] b) Unsupervised learning

[0174] Unsupervised learning is a machine learning task aimed at learning features that describe hidden structure in unlabelled data. The input data is unlabelled and there is no known outcome. Some examples of unsupervised learning include K-means clustering, Principal Component Analysis (PCA), Nonlinear Independent Component Analysis (ICA), and Long Short-Term Memory (LSTM).

[0175] c) Reinforcement Learning (RL)

[0176] In reinforcement learning (RL), an agent aims to optimize a long-term goal by interacting with an environment based on a trial-and-error process, and is goal-oriented learning based on interaction with an environment. Examples of RL algorithms are as follows.

[0177] - Q-learning

[0178] - Multi-armed bandit learning

[0179] - Deep Q-network

[0180] - State-Action-Reward-State-Action (SARSA)

[0181] - Temporal difference learning

[0182] - Actor-Critic reinforcement learning

[0183] - Deep Deterministic Policy Gradient (DDPG)

[0184] - Monte Carlo Tree Search

[0185] In addition, reinforcement learning can be grouped into model-based reinforcement learning and model-free reinforcement learning as follows.

[0186] - Model-based reinforcement learning: refers to RL algorithms that use a predictive model. Using a model of various dynamic states of the environment and which states lead to rewards, probabilities of transitions between states are obtained.

[0187] - Model-free reinforcement learning: refers to RL algorithms based on values or policies that achieve maximum future rewards. Multi-agent environments / states are less computationally complex, and an accurate representation of the environment is not required.

[0188] In addition, RL algorithms can also be classified as value-based RL vs. policy-based RL, policy-based RL vs. non-strategic RL, etc.

[0189] Hereinafter, representative models of deep learning will be exemplified.

[0190] Figure 8 A feedforward neural network is exemplified.

[0191] A feedforward neural network (FFNN) consists of an input layer, a hidden layer, and an output layer.

[0192] In the FFNN, information is sent only from the input layer to the output layer, and if there is a hidden layer, through it.

[0193] Figure 9 A recurrent neural network is exemplified.

[0194] A recurrent neural network (RNN) is an artificial neural network in which hidden nodes are connected to directed edges to form directed cycles. This model is suitable for processing data that occurs sequentially (e.g., speech and text).

[0195] In the RNN, Figure 9 A denotes a neural network, xt denotes an input value, and ht denotes an output value. Here, ht can refer to a state value representing a current state based on time, and ht-1 can represent a previous state value.

[0196] One type of RNN is LSTM (Long Short Term Memory), which has a structure that adds a cell state to the hidden state of the RNN. LSTM can erase unnecessary memories by adding an input gate, a forget gate, and an output gate to the RNN cell (memory cell of the hidden layer). LSTM adds a cell state compared to the RNN.

[0197] Figure 10 A convolutional neural network is exemplified.

[0198] A convolutional neural network (CNN) is used for two purposes: to reduce model complexity and extract good features by applying a convolution operation commonly used in the field of image processing or image processing.

[0199] - Kernel or filter: refers to a unit / structure that applies weights to inputs of a specific range / unit. The kernel (or filter) can be changed through learning.

[0200] - Stride: refers to the range of movement of the kernel moving within the input.

[0201] - Feature map: refers to the result of applying a kernel to an input. Several feature maps can be extracted to ensure stability against distortion, variation, etc.

[0202] - Padding: refers to values added to adjust the size of a feature map.

[0203] - Pooling: refers to an operation that reduces the size of a feature map by down-sampling the feature map (e.g., max pooling, average pooling).

[0204] Figure 11 An autoencoder is exemplified.

[0205] An autoencoder refers to a neural network that receives a feature vector x (x1, x2, x3,...) as input and outputs the same or similar vector x' (x'1, x'2, x'3,...)'.

[0206] An autoencoder has the same characteristics as an input node and an output node. Since the autoencoder reconstructs the input, the output can be referred to as reconstruction. In addition, the autoencoder is a kind of unsupervised learning.

[0207] Figure 11 The loss function of the illustrated autoencoder is calculated based on the difference between the input and the output, and based on this, the degree of input loss is identified, and an optimization process is performed in the autoencoder to minimize the loss.

[0208] Hereinafter, in order to explain AI (or AI / ML) more specifically, the terms can be defined as follows.

[0209] - Data collection: data collected from network nodes, management entities, or UEs as a basis for AI model training, data analysis, and inference.

[0210] - AI model: a data-driven algorithm that applies AI technology, which generates a set of outputs consisting of prediction information and / or decision parameters based on a set of inputs.

[0211] - AI / ML training: an online or offline process of training an AI model by learning features and patterns that best represent data and obtaining a trained AI / ML model for inference.

[0212] - AI / ML inference: a process of using a trained AI / ML model to make predictions or guide decisions based on collected data and the AI / ML model.

[0213] Figure 12 A functional framework for AI operations is exemplified.

[0214] Referring to Figure 12 , the data collection function (10) is a function of collecting input data and providing processed input data to the model training function (20) and the model inference function (30).

[0215] Examples of input data can include measurement results from UEs or different network entities, feedback from executors, outputs from AI models.

[0216] The data collection function (10) performs data preparation based on the input data and provides input data processed through data preparation. Here, the data collection function (10) does not perform specific data preparation (e.g., data preprocessing and cleaning, formatting, and transformation) for each AI algorithm, and can perform data preparation common to AI algorithms.

[0217] After performing the data preparation process, the model training function (10) provides the training data (11) to the model training function (20) and the inference data (12) to the model inference function (30). Here, the training data (11) is data required as an input to the AI model training function (20). The inference data (12) is data required as an input to the AI model inference function (30).

[0218] The data collection function (10) can be performed by a single entity (e.g., a UE, a RAN node, a network node, etc.), but can also be performed by multiple entities. In this case, the training data (11) and the inference data (12) can be provided from the multiple entities to the model training function (20) and the model inference function (30), respectively.

[0219] The model training function (20) is a function that performs AI model training, validation, and testing, which can generate a model performance metric as part of a model testing process. If necessary, the model training function (20) is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on the training data (11) passed by the data collection function (10).

[0220] Here, the model deployment / update (13) is used to initially deploy a trained, validated, and tested AI model to the model inference function (30) or to pass an updated model to the model inference function (30).

[0221] The model inference function (30) is a function that provides an AI model inference output (16) (e.g., a prediction or a decision). The model inference function (30) can provide a model performance feedback (14) to the model training function (20) as applicable. If necessary, the model inference function (30) is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on the inference data (12) passed by the data collection function (10).

[0222] Here, the output (16) refers to an inference output of the AI model produced by the model inference function (30), and the details of the inference output can be use case specific.

[0223] The model performance feedback (14) can be used to monitor the performance of the AI model when available, and this feedback can be omitted.

[0224] The enforcer function (40) is a function that receives the output (16) from the model inference function (30) and triggers or performs a corresponding action. The enforcer function (40) can trigger an action for other entities (e.g., one or more UEs, one or more RAN nodes, one or more network nodes, etc.) or for itself.

[0225] The feedback (15) can be used to derive training data (11), infer data (12), or monitor the performance of the AI model and its impact on the network, etc.

[0226] Furthermore, the definition of training / validation / testing in the dataset used in AI / ML can be divided as follows.

[0227] - Training data: refers to a dataset used to learn a model.

[0228] - Validation data: this refers to a dataset used to validate a model that has completed learning. In other words, it generally refers to a dataset used to prevent overfitting of the training dataset.

[0229] It also refers to a dataset used to select the best model among various models learned during the learning process. Therefore, it can also be considered as a kind of learning.

[0230] - Test data: refers to a dataset used for final evaluation. This data is unrelated to learning.

[0231] In the case of a dataset, if the training set is generally divided, the training data and the validation data can be divided into 8:2 or 7:3 within the entire training set, and if the test is included, 6:2:2 (training: validation: test) can be used.

[0232] According to the capability of the AI / ML function between the base station and the UE, the level of cooperation can be defined as follows, and can be modified by combining the following multiple levels or separating any one level.

[0233] Cat 0a) No cooperation framework: the AI / ML algorithm is purely based on the implementation, and does not require any air interface change.

[0234] Cat 0b) This level corresponds to a framework without cooperation, but with a modified air interface customized for efficient implementation-based AI / ML algorithms.

[0235] Cat 1) This involves inter-node support to improve the AI / ML algorithm of each node. This applies if the UE receives support (for training, adaptation, etc.) from the gNB, and vice versa. At this level, no model exchange between network nodes is required.

[0236] Cat 2) Joint ML tasks between the UE and the gNB can be performed. This level requires AI / ML model commands and exchange between network nodes.

[0237] Previously in Figure 12The exemplified functions can be implemented in a RAN node (e.g., a base station, a TRP, a base station central unit (CU), etc.), a network node, an operation administration maintenance (OAM) of a network operator, or a UE.

[0238] Alternatively, Figure 12 The exemplified functions can be implemented by cooperation of two or more entities among a RAN, a network node, an OAM of a network operator, or a UE. For example, one entity can perform some of the functions of Figure 12 and the other entity can perform the remaining functions. Thus, since some of the functions shown in Figure 12 are performed by a single entity (e.g., a UE, a RAN node, a network node, etc.), the transmission / provision of data / information between each function can be omitted. For example, if the model training function (20) and the model inference function (30) are performed by the same entity, the delivery / provision of the model deployment / update (13) and the model performance feedback (14) can be omitted.

[0239] Alternatively, Figure 13 Any of the functions shown in

[0240] Figure 13 is a diagram exemplifying split AI inference.

[0241] Figure 14 Exemplified is a case where a model inference function is performed in cooperation with a terminal device such as a UE and a network AI / ML endpoint among split AI operations.

[0242] In addition to the model inference function, the model training function, the execution body, and the data collection function are respectively split into multiple parts according to the current task and environment, and can be performed by cooperation of multiple entities.

[0243] For example, a computation- and energy-intensive part can be performed at a network endpoint, while a part sensitive to personal information and a part sensitive to delay can be performed at a terminal device. In this case, the terminal device can perform a task / model from input data to a certain part / layer, and then transmit intermediate data to the network endpoint. The network endpoint performs the remaining part / layer and provides an inference output to one or more devices that perform an action / task.

[0244] Figure 14 Exemplified is an application of the functional framework in a wireless communication system.

[0245] Figure 15The case where the AI model training function is performed by a network node (e.g., a core network node, an OAM of a network operator, etc.) and the AI model inference function is performed by a RAN node (e.g., a base station, a TRP, a CU of a base station, etc.) is exemplified.

[0246] Step 1: RAN node 1 and RAN node 2 send input data (i.e., training data) for AI model training to the network node. Here, RAN node 1 and RAN node 2 can send data collected from UEs (e.g., UE measurement results related to RSRP, RSRQ, SINR of a serving cell and neighboring cells, UE location, speed, etc.) to the network node.

[0247] Step 2: The network node trains an AI model using the received training data.

[0248] Step 3: The network node distributes / updates the AI model to RAN node 1 and / or RAN node 2. RAN node 1 (and / or RAN node 2) can continue to perform model training based on the received AI model.

[0249] For ease of explanation, it is assumed that the AI model is distributed / updated only to RAN node 1.

[0250] Step 4: RAN node 1 receives input data (i.e., inference data) for AI model inference from UEs and RAN node 2.

[0251] Step 5: RAN node 1 performs AI model inference using the received inference data to generate output data (e.g., a prediction or a decision).

[0252] Step 6: If applicable, RAN node 1 can send model performance feedback to the network node.

[0253] Step 7: RAN node 1, RAN node 2, and UEs (or “RAN node 1 and UEs”, or “RAN node 1 and RAN node 2”) perform an action based on the output data. For example, in the case of a load balancing operation, UEs can move from RAN node 1 to RAN node 2.

[0254] Step 8: RAN node 1 and RAN node 2 send feedback information to the network node.

[0255] Figure 15 The application of the functional framework in a wireless communication system is exemplified.

[0256] Figure 16 The case where both the AI model training function and the AI model inference function are performed by a RAN node (e.g., a base station, a TRP, a CU of a base station, etc.) is exemplified.

[0257] Step 1: The UE and RAN node 2 send input data for AI model training (i.e., training data) to RAN node 1.

[0258] Step 2: RAN node 1 trains an AI model using the received training data.

[0259] Step 3: RAN node 1 receives input data for AI model inference (i.e., inference data) from the UE and RAN node 2.

[0260] Step 4: RAN node 1 performs AI model inference using the received inference data to generate output data (e.g., a prediction or a decision).

[0261] Step 5: RAN node 1, RAN node 2, and the UE (or “RAN node 1 and the UE,” or “RAN node 1 and RAN node 2”) perform an action based on the output data. For example, in the case of a load balancing operation, the UE can move from RAN node 1 to RAN node 2.

[0262] Step 6: RAN node 2 sends feedback information to RAN node 1.

[0263] Figure 16 An application of the functional framework in a wireless communication system is exemplified.

[0264] CSI (channel state information) related operations A case where the AI model training function is performed by a RAN node (e.g., a base station, a TRP, a CU of a base station, etc.) and the AI model inference function is performed by a UE is exemplified.

[0265] Step 1: The UE sends input data for AI model training (i.e., training data) to a RAN node. Here, the RAN node can collect data from various UEs and / or from other RAN nodes (e.g., measurement results of a UE related to RSRP, RSRQ, SINR of a serving cell and neighboring cells, location, speed of the UE, etc.).

[0266] Step 2: The RAN node trains an AI model using the received training data.

[0267] Step 3: The RAN node distributes / updates the AI model to the UE. The UE can continue to perform model training based on the received AI model.

[0268] Step 4: The UE receives input data for AI model inference (i.e., inference data) from the RAN node (and / or from other UEs).

[0269] Step 5: The UE performs AI model inference using the received inference data to generate output data (e.g., a prediction or a decision).

[0270] Step 6: If applicable, the UE can send model performance feedback to the RAN node.

[0271] Step 7: The UE and the RAN node perform an action based on the output data.

[0272] Step 8: The UE sends feedback information to the RAN node.

[0273] 5) CSI reporting

[0274] In an NR (New Radio) system, a CSI-RS (Channel State Information Reference Signal) is used for time and / or frequency tracking, CSI computation, L1 (Layer 1)-RSRP (Reference Signal Received Power) computation, and mobility. Here, the CSI computation is related to CSI acquisition, and the L1-RSRP computation is related to beam management (BM).

[0275] CSI (Channel State Information) collectively refers to information that can represent the quality of a radio channel (or also referred to as a link) formed between a terminal and an antenna port.

[0276] - To perform one of the purposes of the CSI-RS, a terminal (e.g., a user equipment, UE) receives configuration information related to CSI from a base station (e.g., a general node B, gNB) through RRC (Radio Resource Control) signaling.

[0277] The configuration information related to CSI can include at least one of information related to a CSI-IM (Interference Management) resource, information related to a CSI measurement configuration, information related to a CSI resource configuration, information related to a CSI-RS resource, and information related to a CSI reporting configuration.

[0278] i) The information related to the CSI-IM resource can include CSI-IM resource information, CSI-IM resource set information, etc. A CSI-IM resource set is identified by a CSI-IM resource set ID (Identifier), and one resource set includes at least one CSI-IM resource. Each CSI-IM resource is identified by a CSI-IM resource ID.

[0279] ii) Information related to CSI resource configuration can be expressed as a CSI-ResourceConfig IE. Information related to CSI resource configuration defines a group including at least one of a NZP (non-zero power) CSI-RS resource set, a CSI-IM resource set, or a CSI-SSB resource set. In other words, information related to CSI resource configuration can include a CSI-RS resource set list, and the CSI-RS resource set list can include at least one of a NZP CSI-RS resource set list, a CSI-IM resource set list, or a CSI-SSB resource set list. A CSI-RS resource set is identified by a CSI-RS resource set ID, and one resource set includes at least one CSI-RS resource. Each CSI-RS resource is identified by a CSI-RS resource ID.

[0280] A parameter indicating the use of a CSI-RS (e.g., a "repetition" parameter related to BM, a "trs-Info" parameter related to tracking) can be configured per NZP CSI-RS resource set.

[0281] iii) Information related to CSI report configuration includes a report configuration type (reportConfigType) parameter indicating a time domain behavior and a report quantity (reportQuantity) parameter indicating a CSI-related quantity of a report. The time domain behavior can be periodic, aperiodic, or semi-persistent.

[0282] - The terminal measures CSI based on the configuration information related to CSI.

[0283] The CSI measurement can include (1) a process in which the terminal receives a CSI-RS and (2) a process of calculating CSI through the received CSI-RS, and is described in detail later.

[0284] For a CSI-RS, the RE (resource element) mapping of a CSI-RS resource in the time domain and the frequency domain is configured through a higher layer parameter CSI-RS-ResourceMapping.

[0285] - The terminal reports the measured CSI to the base station.

[0286] In this case, when the number of CSI-ReportConfig is configured as "none (or no report)", the terminal can omit the report. However, the terminal can perform a report to the base station even though the number is configured as "none (or no report)". When the number is configured as "none", aperiodic TRS or configured repetition is triggered. In this case, the report of the terminal can be omitted only when the repetition is configured as "on".

[0287] 1) CSI measurement

[0288] NR systems support more flexible and dynamic CSI measurement and reporting. Here, CSI measurement can include the process of receiving CSI-RS and obtaining CSI by calculating the received CSI-RS.

[0289] As a time-domain behavior of CSI measurement and reporting, aperiodic / semi-persistent / periodic CM (channel measurement) and IM (interference measurement) are supported. A 4-port NZP CSI-RS RE pattern is used for CSI-IM configuration.

[0290] The IMR of CSI-IM based on NR has a similar design to that of CSI-IM of LTE and is configured independently of the ZP CSI-RS resource for PDSCH rate matching. In addition, each port emulates an interference layer of NZP CSI-RS with (desired channel and) precoding in NZP CSI-RS based IMR. It is mainly targeted at MU interference due to intra-cell interference measurement for multi-user cases.

[0291] The base station transmits a precoded NZP CSI-RS to the terminal on each port of the configured NZP CSI-RS based IMR.

[0292] The terminal assumes a channel / interference layer and measures interference for each port in the resource set.

[0293] When there is no PMI and RI feedback for the channel, multiple resources are configured in a set, and the base station or network indicates a subset of NZP CSI-RS resources through DCI for channel / interference measurement.

[0294] The resource setting and resource setting configuration are described in more detail.

[0295] 2) Resource setting

[0296] Each CSI resource setting "CSI-ResourceConfig" includes a configuration for S≥1 CSI resource sets (given by the higher layer parameter csi-RS-ResourceSetList). The CSI resource configuration corresponds to the CSI-RS-resource set list. Here, S denotes the number of configured CSI-RS resource sets. Here, the configuration for S≥1 CSI resource sets includes each CSI resource set, and each CSI resource set includes a CSI-RS resource (configured with NZP CSI-RS or CSI-IM) and an SS / PBCH block (SSB) resource for L1-RSRP calculation.

[0297] Each CSI resource setting is located at a DL BWP (Bandwidth Part) identified by a higher layer parameter bwp-id. In addition, all CSI resource settings linked to a CSI report setting have the same DL BWP.

[0298] The time domain behavior of the CSI-RS resources included in a CSI resource setting in the CSI-ResourceConfig IE can be indicated by the higher layer parameter resourceType and can be configured as aperiodic, periodic, or semi-persistent. For periodic and semi-persistent CSI resource configuration, the number of configured CSI-RS resource sets (S) is limited to "1". For periodic and semi-persistent CSI resource settings, the configured periodicity and slot offset are given by the numerology of the associated DL BWP given by bwp-id.

[0299] When a UE is configured with multiple CSI-ResourceConfig including the same NZP CSI-RS resource ID, the same time domain behavior is configured for the CSI-ResourceConfig.

[0300] When a UE is configured with multiple CSI-ResourceConfig including the same CSI-IM resource ID, the same time domain behavior is configured for the CSI-ResourceConfig.

[0301] One or more CSI resource settings for channel measurement (CM) and interference measurement (IM) are configured by higher layer signaling as follows.

[0302] - CSI-IM resource for interference measurement

[0303] - NZP CSI-RS resource for interference measurement

[0304] - NZP CSI-RS resource for channel measurement

[0305] In other words, CMR (Channel Measurement Resource) can be NZP CSI-RS for CSI acquisition, and IMR (Interference Measurement Resource) can be NZP CSI-RS for CSI-IM and IM.

[0306] In this case, CSI-IM (or ZP CSI-RS for IM) is mainly used for inter-cell interference measurement.

[0307] In addition, NZP CSI-RS for IM is mainly used for intra-cell interference measurement from multi-users.

[0308] The UE can assume that the CSI-RS resources for channel measurement and the CSI-IM / NZP CSI-RS resources for interference measurement configured for one CSI report configuration are "QCL-TypeD" per resource.

[0309] 3) Resource setting configuration

[0310] As described, a resource setting can mean a list of resource sets.

[0311] For aperiodic CSI, each trigger state configured by using the higher layer parameter CSI-AperiodicTriggerState is associated with one or more CSI-ReportConfigs linked to periodic, semi-persistent or aperiodic resource settings per CSI-ReportConfig.

[0312] One report setting can be connected to up to 3 resource settings.

[0313] - When one resource setting is configured, the resource setting (given by the higher layer parameter resourcesForChannelMeasurement) is about channel measurement for L1-RSRP computation.

[0314] - When two resource configurations are configured, the first resource setting (given by the higher layer parameter resourcesForChannelMeasurement) is used for channel measurement, and the second resource setting (given by csi-IM-ResourcesForInterference or nzp-CSI-RS-ResourcesForInterference) is used for interference measurement performed in CSI-IM or NZP CSI-RS.

[0315] - When three resource settings are configured, the first resource setting (given by resourcesForChannelMeasurement) is used for channel measurement, the second resource setting (given by csi-IM-ResourcesForInterference) is used for CSI-IM based interference measurement, and the third resource setting (given by nzp-CSI-RS-ResourcesForInterference) is used for NZP CSI-RS based interference measurement.

[0316] For semi-persistent or periodic CSI, each CSI-ReportConfig is linked to periodic or semi-persistent resource settings.

[0317] - When one resource setting (given by resourcesForChannelMeasurement) is configured, the resource setting is about channel measurement for L1-RSRP computation.

[0318] - When two resource settings are configured, the first resource setting (given by resourcesForChannelMeasurement) is used for channel measurement, and the second resource setting (given by the higher layer parameter csi-IM-ResourcesForInterference) is used for interference measurement performed in CSI-IM.

[0319] 4) CSI computation

[0320] When interference measurement is performed in CSI-IM, each CSI-RS resource for channel measurement is associated with each CSI-IM resource in the order of CSI-RS resource and CSI-IM resource in the corresponding resource set. The number of CSI-RS resources for channel measurement is the same as the number of CSI-IM resources.

[0321] In addition, when interference measurement is performed in NZP CSI-RS, the UE does not expect to be configured with one or more NZP CSI-RS resources in the associated resource set in the resource setting for channel measurement.

[0322] A terminal configured with the higher layer parameter nzp-CSI-RS-ResourcesForInterference does not expect to be configured with 18 or more NZP CSI-RS ports in the NZP CSI-RS resource set.

[0323] For CSI measurement, the terminal assumes the following.

[0324] - Each NZP CSI-RS port configured for interference measurement corresponds to an interference transmission layer.

[0325] - All interference transmission layers of NZP CSI-RS ports for interference measurement consider the EPRE (Energy Per Resource Element) ratio.

[0326] - Different interference signals in REs of NZP CSI-RS resources for channel measurement, NZP CSI-RS resources for interference measurement, or CSI-IM resources for interference measurement

[0327] Uplink control information reporting method for dynamic function switching

[0328] For CSI reporting, the time and frequency resources that can be used by the UE are controlled by the base station.

[0329] CSI (Channel State Information) can include at least one of Channel Quality Indicator (CQI), Precoding Matrix Indicator (PMI), CSI-RS Resource Indicator (CRI), SS / PBCH Block Resource Indicator (SSBRI), Layer Indicator (LI), Rank Indicator (RI), or L1-RSRP.

[0330] For CQI, PMI, CRI, SSBRI, LI, RI, L1-RSRP, the higher layer configures the terminal with N≥1 CSI-ReportConfig reporting settings, M≥1 CSI-ResourceConfig resource settings, and a list of one or two trigger states (provided by aperiodicTriggerStateList and semiPersistentOnPUSCH-TriggerStateList). Each trigger state in aperiodicTriggerStateList includes a list of associated CSI-ReportConfigs, which indicates the channel and optional resource set ID for interference. In semiPersistentOnPUSCH-TriggerStateList, one associated CSI-ReportConfig is included in each trigger state.

[0331] In addition, the time domain behavior of the CSI report supports periodic, semi-persistent, aperiodic.

[0332] i) Perform periodic CSI reporting in short PUCCH, long PUCCH. The periodicity and slot offset of the periodic CSI reporting can be configured by RRC, and refer to the CSI-ReportConfig IE.

[0333] ii) Perform SP (semi-periodic) CSI reporting in short PUCCH, long PUCCH, or PUSCH.

[0334] For SP CSI in short / long PUCCH, the periodicity and slot offset are configured by RRC, and the CSI reporting is activated / deactivated by a separate MAC CE / DCI.

[0335] For SP CSI in PUSCH, the periodicity of the SP CSI reporting is configured by RRC, but the slot offset is not configured by RRC, and the SP CSI reporting is activated / deactivated by DCI (format 0_1). For SP CSI reporting in PUSCH, a separate RNTI (SP-CSI C-RNTI) is used.

[0336] The initial CSI reporting timing follows the PUSCH time domain allocation value indicated by DCI, and the subsequent CSI reporting timing follows the periodicity configured by RRC.

[0337] DCI format 0_1 can include a CSI request field and activate / deactivate a specific configured SP-CSI trigger state. SP CSI reporting has an activation / deactivation equal to or similar to a mechanism with data transmission in SPS PUSCH.

[0338] iii) Aperiodic CSI reporting is performed in PUSCH and triggered by DCI. In this case, information related to the trigger of aperiodic CSI reporting can be delivered / indicated / configured by MAC-CE (Media Access Control-Control Element).

[0339] For AP CSI with AP CSI-RS, AP CSI-RS timing is configured by RRC, and timing for AP CSI reporting is dynamically controlled by DCI.

[0340] In NR, a method of dividing and reporting CSI in multiple reporting instances applied to PUCCH-based CSI reporting (e.g., sequentially transmitted by RI, WB PMI / CQI, SB PMI / CQI) applied in LTE is not applied. Instead, in NR, there is a restriction that a specific CSI report is not configured in short / long PUCCH and a CSI omission rule is defined. In addition, regarding AP CSI reporting timing, PUSCH symbol / slot position is dynamically indicated by DCI. In addition, a candidate slot offset is configured by RRC. For CSI reporting, a configuration slot offset (Y) is set per report. For UL-SCH, a slot offset K2 is separately configured.

[0341] 2. A CSI latency class (low latency class, high latency class) is defined with respect to CSI computation complexity. Low latency CSI is WB CSI including up to 4-port Type I codebook or up to 4-port non-PMI feedback CSI. High latency CSI refers to CSI other than low latency CSI. For a normal terminal, (Z, Z') is defined in units of OFDM symbols. Here, Z denotes the minimum CSI processing time before performing CSI reporting after receiving aperiodic CSI trigger DCI. In addition, Z' refers to the minimum CSI processing time before performing CSI reporting after receiving CSI-RS for channel / interference.

[0342] In addition, the terminal reports the number of CSIs that can be simultaneously calculated.

[0343] Figure 17

[0344] In the disclosure, " / " represents "and", "or", or "and / or" according to the context. In the disclosure, "terminal" and "UE" can be used interchangeably and have the same or similar meanings from an air interface perspective, and "base station", "network", and "transmission reception point (TRP)" can also be used interchangeably and have the same or similar meanings.

[0345] 3GPP Rel-18 AI / ML study item started the research on applying AI / ML techniques to air interface between terminals and networks. The study considers beam management (BM), CSI acquisition, and positioning as key use cases to integrate AI / ML into air interface. For this, various terminologies are defined and discussed as follows. Specifically, the methods for managing AI / ML models (e.g., life cycle management (LCM): model activation / deactivation, model switching / selection, model monitoring, model update / fine-tuning, fallback, etc.) are started to be discussed.

[0346] Table 6 provides an example of the terminologies to be used in the AI / ML study item.

[0347] [Table 6]

[0348]

[0349]

[0350] The explanation of the terminologies exemplified in Table 6 above can be applied to describe the methods proposed in the disclosure. Table 7 exemplifies agreements discussed in the AI / ML study item.

[0351] [Table 7]

[0352]

[0353]

[0354] Referring to Table 7, for the UE part / UE side model (where the UE part model refers to the UE part in the two-sided model, and the UE side model refers to the one-sided model where AI is implemented only in the UE), agreement has been reached on the mechanism of the LCM procedure. For the function-based LCM procedure: indication based on activation / deactivation / switching / fallback of each AI / ML function. Here, the UE can have more than one AI / ML model for the function. For the model-ID-based LCM procedure: indication based on model selection / activation / deactivation / switching / fallback of each model ID.

[0355] Furthermore, for the UE-side model and the UE part of the dual-side model, the following agreements were made regarding the AI / ML model identification. For AI / ML function identification, the framework of legacy 3GPP features is reused, and the UE indicates the supported functions for a given sub-use case. For AI / ML model identification, the model is identified by model ID in the network, and the UE indicates the supported AI / ML models. For function-based LCM, the network indicates the activation / deactivation / fallback / switching of AI / ML functions via 3GPP signaling (e.g., RRC, MAC-CE, DCI). Additionally, the network can not identify the model, and the UE can perform model-level LCM. In model ID (identifier) based LCM, the model is identified in the network, and the network / UE can activate / deactivate / fallback / switch individual AI / ML models by model ID.

[0356] Additionally, AI / ML-enabled features are referred to as features that can be used with AI / ML. For function identification, one or more functions can be defined within an AI / ML-enabled feature.

[0357] As mentioned above, two ways / methods for model management are being discussed for the UE-side model and the UE part of the dual-side model.

[0358] First, the function-based way focuses on the functions or performance supported by applying AI / ML models. The signaling related to these functions, such as activation / deactivation / switching / fallback, is currently being discussed as a standardization topic.

[0359] Second, the model-based way focuses on the signaling related to the activation / deactivation / switching / fallback of AI / ML models as a standardization topic.

[0360] The biggest difference between the above two approaches is as follows. When the terminal implements multiple AI / ML models for the same function according to various environments / scenarios to which the function can be applied, related base station settings, etc., i) in the case of model-based LCM, which model is activated / deactivated / selected / switched is directly managed, while ii) in the case of function-based LCM, since activation / deactivation / monitoring, etc. are performed from the perspective of the function / performance of the corresponding model, the network (NW: network) can not necessarily know how many models the terminal has implemented and how they are implemented. The entity that monitors the model / function of the UE part / side model can be the UE and / or the NW, and the entity that decides / performs the activation / deactivation / selection / switching / update of the model / function can also be the UE and / or the NW. As an example of a joint UE-NW approach, a method can be considered in which the UE recommends / reports its model selection / update decision to the NW, and then the NW performs the final confirmation / decision on these decisions. Involving the NW in the LCM procedure in this way inevitably leads to signaling between the UE and the NW.

[0361] The following illustrates the LCM signaling between the UE and the network.

[0362] 1) UE to network signaling

[0363] - Model / function performance (monitoring) related information / measurement

[0364] - Recommendations / preferences related to selection / activation / deactivation / switching / update / monitoring / fallback, etc. of the model / function

[0365] - Confirmation of NW command / recommendation related to selection / activation / deactivation / switching / update / monitoring / fallback, etc. of the model / function

[0366] 2) Network to UE signaling

[0367] - Information / measurement related to model / function performance (monitoring)

[0368] - Command / recommendation related to selection / activation / deactivation / switching / update / monitoring / fallback, etc. of the model / function

[0369] In addition, the terminal can implement a single AI / ML enabled feature or sub-use case through multiple functions / models. For example, in order to implement a single AI / ML enabled feature, CSI reporting based on time domain prediction, different AI / ML models can be implemented for each operating environment / scenario. In addition, the AI / ML models implemented in different environments / scenarios can have different functions / performance / functionalities, thereby defining multiple functions.

[0370] Here, there can be various aspects regarding criteria by which the functions can be divided. For example, the functions can be divided based on criteria such as feature enabling / triggering conditions, feature performance, and granularity / concept of feature groups defined within 3GPP UE features, etc.

[0371] Hereinafter, in the description of the disclosure, for ease of explanation, it is assumed that any AI / ML-enabled feature is implemented with M AI / ML models and N functions. Here, M ≥ N ≥ 1, and one or more models can be mapped to / correspond to one function. For example, for time-domain prediction-based CSI / beam reporting, it can be divided into N functions based on a maximum predictable time instance or supported CSI / beam reporting settings (parameters). As another example, for spatial domain beam prediction, it can be divided into N functions based on granularity / size / configuration of a supported set A (granularity / size / configuration of a given set B). Here, set A refers to a set of beam RSs for DL beam prediction, and set B refers to a set of beam RSs for DL beam measurement. As another example, it can be divided into N functions based on supported positioning accuracy / configuration. As another example, it can be divided into N functions related to supported CSI codebook construction / configuration for CSI compression. Further, in the above examples, one function can be configured / implemented by multiple AI / ML models each trained according to an operating scenario / environment, etc. Alternatively, it can be configured / implemented by a single model trained in various scenarios / environments.

[0372] When a single AI / ML-enabled feature is implemented with multiple functions (or multiple models), the UCI (uplink control information) reporting method supported by the current 3GPP standard has the following limitations / disadvantages. According to the current 3GPP standard, UCI reporting values and reporting operations (e.g., periodicity, time) are mostly configured through RRC messages. Therefore, considering the case where the functions / models change, (1) separate reporting is configured for each function / model through RRC (e.g., multiple CSI / beam reports are configured), and then a specific report (e.g., aperiodic / semi-persistent CSI / beam report) can be selectively activated / triggered through MAC-CE / DCI, or (2) the reporting-related configuration must be changed (e.g., periodic CSI / beam report) through RRC reconfiguration. In the case of the above (1), there is a disadvantage of large signaling overhead, and in the case of the above (2), there is a disadvantage of not only large signaling overhead due to RRC reconfiguration, but also large delay (latency) until the reconfiguration is completed.

[0373] The disclosure proposes the following methods to solve the above problems.

[0374] Embodiment 1: For one UCI report configured by higher layer signaling (e.g., RRC signaling) (i.e., one UCI report configuration), some of the reporting configurations (set values) in the reporting configuration (set values) can be changed to lower layer signaling (e.g., MAC-CE and / or DCI) (according to the change of function / model).

[0375] Embodiment 1-1: For one or more specific configuration parameters of a single UCI report, multiple configuration candidate values (e.g., candidate values to be applied for each configuration / function / model ID) or a range of corresponding set values can be configured by higher layer signaling (e.g., RRC signaling), and a specific set value (or corresponding ID) can be specified by lower layer signaling (e.g., MAC-CE and / or DCI).

[0376] Here, if there is no lower layer signaling (e.g., MAC-CE and / or DCI) for the corresponding reporting parameter, a specific default set value (or ID) to be used by the UE can be additionally configured / specified.

[0377] Embodiment 1-2: The set value of one or more specific configuration parameters of a UCI report can be indicated by lower layer signaling (e.g., MAC-CE and / or DCI).

[0378] Here, when the configuration is performed for a UCI report by higher layer signaling (e.g., RRC signaling), the set value of the one or more parameters can not be indicated / configured, or a default / initial set value (or ID) (to be applied before the first lower layer signaling (e.g., MAC-CE and / or DCI) indication is applied) can be indicated / configured.

[0379] In the above embodiment 1, the time point at which the value specified by the lower layer signaling (e.g., MAC-CE and / or DCI) is applied can be agreed between the NW and the UE, or can be defined / configured in advance.

[0380] For example, the time point at which the value specified by the lower layer signaling (e.g., MAC-CE and / or DCI) is applied can be i) a time point that elapses a specific time offset (e.g., T slots / symbols / milliseconds, etc.) from when the lower layer signaling (e.g., MAC-CE and / or DCI) is transmitted / received, or ii) a time point that elapses a specific time offset (e.g., T slots / symbols / milliseconds, etc.) from when the UE transmits / receives a response message / signal (e.g., HARQ-ACK for a PDSCH carrying a MAC-CE, ACK for a PDCCH carrying a DCI, etc.) to the lower layer signaling (e.g., MAC-CE and / or DCI).

[0381] Here, the T value can be a specific value defined as a general value for the UE (e.g., T = 0, T = x symbols, T = y ms), or a value that the base station can specifically configure for the UE. If configured as a UE-specific value, the (minimum) T value supported by the UE can be reported by the UE to the base station (e.g., in the form of a UE capability report).

[0382] In addition, the set value indicated by the low-layer signaling (e.g., MAC-CE and / or DCI) in Embodiment 1 can be a direct indication / configuration of the value, or an indirectly set value (i.e., linked to a specific ID (identifier) defined / configured / reported). For example, if an ID is assigned for a function or model, the set value of the parameter associated with the ID can be linked / mapped. In this case, the indication for the corresponding ID can be performed by the low-layer signaling (e.g., MAC-CE and / or DCI) to perform the parameter set change for the corresponding reported value. The ID can be understood / interpreted as a configured ID (e.g., configuration ID) or an ID related to UE capability.

[0383] In addition, in Embodiment 1, the information related to the set value indicated by the low-layer signaling (e.g., MAC-CE and / or DCI) (e.g., function / feature / configuration ID) can be information indicated separately from the trigger / activation / deactivation indication of the UCI report, or can be information indicated together with the UCI report. As an example of the latter, the information related to the set value can be included and indicated in the MAC-CE for the activation of the semi-persistent CSI report on the PUCCH. As another example, the information related to the set value can be included and indicated in the DCI for the triggering of the aperiodic CSI report.

[0384] Embodiment 1 can be applied when the NW dynamically changes the function / feature / model for a single feature according to the NW-side AI / ML implementation, and can also be applied when the UE dynamically changes the function / feature / model according to the UE-side AI / ML implementation. Similarly, for the bilateral AI / ML implementation, Embodiment 1 can be applied when the UCI report is changed according to the change of the function / feature / model of the NW and / or UE. By applying Embodiment 1, instead of configuring multiple UCI reports, a single UCI report is configured, and one or more parameters of the single UCI report are dynamically changed, thereby significantly reducing the signaling overhead, and in particular for the periodic UCI report, the parameters can be adaptively changed without RRC reconfiguration.

[0385] Furthermore, with application of embodiment 1, due to the dynamic change of the reporting configuration (i.e., the configuration of some parameters within the UCI reporting configuration), the required (reported) (maximum) UCI payload size can change. For example, in the case of spatial beam prediction, the size of set A and / or set B can change, and thus the required UCI payload size can change. As another example, in the case of CSI compression, the compression ratio can change, and thus the required UCI payload size can change.

[0386] As a solution to this, the UCI bits can be configured to match the worst case (i.e., the maximum UCI payload size), and then when UCI with a smaller payload is needed to be sent, some bits or codepoints can not be used. For example, in the case of spatial beam prediction, the size of the CRI / SSB resource indicator (SSBRI) field can be configured to match the possible maximum set A size.

[0387] As another solution, separate rules related to UCI priority partitioning and / or omission can be defined / configured. For example, due to parameter change indication (e.g., by MAC-CE / DCI), the UCI payload can exceed the maximum size that can be transmitted on the allocated PUCCH / PUSCH resource. In this case, the UE can omit some information in the UCI, or perform a specific UCI reporting (e.g., perform a fallback reporting, report that the UCI payload capacity is exceeded). As another example, in the case of CSI reporting based on CSI compression, as the compression ratio decreases and / or the CSI payload increases due to other requirements (e.g., rank increase), the UCI payload can exceed the maximum size that can be transmitted on the allocated PUCCH / PUSCH resource. In this case, the UE can report that the UCI payload capacity is exceeded by a specific field or field combination in the UCI, and / or operate in a specified fallback reporting mode (e.g., legacy CSI reporting with a specific configuration (e.g., Type-1 codebook)), and / or transmit by omitting some information to be included in the UCI (e.g., omit information about specific subbands, time instances, and / or spatial / temporal basis).

[0388] Furthermore, embodiments are proposed that can be applied separately or together with embodiment 1 (for UE side / partial AI / ML).

[0389] Embodiment 2: The UE can report the change of capability / function / model to the network (e.g., base station). Here, the configuration value of some UCI reporting parameters can change together with the reported information.

[0390] Embodiment 2-1: The parameter configuration can change from a specific negotiated / configured time point after the reporting time for the change of capability / function / model.

[0391] Embodiment 2-2: UCI reporting and reporting for capability / function / model change can be performed simultaneously.

[0392] In Embodiment 2, reporting related to capability / function / model change of the UE can be performed in various forms. For example, the UE can report capability / function / model supported by the UE (e.g., ID of capability / function / model) and / or set value of reporting parameter (for each capability / function / model) to the NW. Then, when the ID is assigned for each capability / function / model, periodic / aperiodic / semi-persistent reporting triggered by the base station related to the corresponding ID (or change of the corresponding ID) can be performed, or event-based reporting (e.g., reported to MAC-CE only when the ID is changed) can be performed. As another example, for reporting related to capability / function / model change of the UE, direct reporting on change of reporting parameter value / information supported by the UE (e.g., change of maximum / minimum value / range, change of granularity, change of field size, RS set related change, etc.) can be performed.

[0393] In Embodiment 2, set value of UCI reporting can be automatically changed by change of function / capability / functionalities / model of UE side / partial AI / ML. Here, set value to be applied for each capability / function / model (e.g., ID of capability / function / model) for corresponding parameter of UCI reporting can be prescribed / defined, or configured by the base station. Alternatively, the value can not be prescribed / set, and value reported by the UE (e.g., set value to be applied for each capability / function / model (e.g., ID of capability / function / model) is reported through UE capability reporting) can be directly applied. In this case, the base station can not indicate set value of corresponding parameter, or can indicate only default / initial set value (to be applied before applying set value of parameter related to reporting of the first capability / function / model change).

[0394] Embodiment 2-1 corresponds to a method in which the UE reporting related to capability / feature / model change is performed separately from the UCI reporting according to Embodiment 1. In this case, the specific agreement / configuration time point (i.e., the time point of the parameter setting value change of the UCI reporting) can be i) a time point after a specific time offset (e.g., T slots / symbols / milliseconds, etc.) from the reporting time point related to the capability / feature / model change, or ii) a time point after a specific time offset (e.g., T slots / symbols / milliseconds, etc.) from the time point when the base station transmits / receives a response message / signal for the UE reporting related to the capability / feature / model change (e.g., an indication from the network for switching / indicating the ID of the capability / feature / model configuration, an indication from the network for switching / indicating the specific value (range) of the reported parameter, scheduling a PUSCH transmission with the same HARQ process number as the PUSCH transmission for reporting the capability / feature / model, and receiving a PDCCH carrying a DCI format with a new data indicator (NDI) field value switched, etc.).

[0395] Here, the T value can be a specific value defined as a general value for the UE (e.g., T=0, T=x symbols, T=y milliseconds), or a value that the base station can configure specifically for the UE. When configured as a UE-specific value, the (minimum) T value supported by the UE can be reported by the UE to the base station (e.g., in the form of a UE capability report).

[0396] Here, in Embodiment 2-1, the information related to the capability / feature / model change (or the channel carrying the information) can have a higher priority than the UL data (e.g., UL-SCH (uplink shared channel)) and / or the CSI / beam information (or the channel carrying the CSI / beam information). Here, this priority can be used for the operation of preferentially dropping / omitting the information / channels with lower priority when channel / symbol overlap occurs.

[0397] Embodiment 2-2 corresponds to a method in which the UCI reporting and the reporting related to the capability / feature / model change are performed together. The parameter setting value corresponding to the UCI reporting can be automatically applied and reported together with the reporting information related to the capability / feature / model change. To this end, a reporting field related to the capability / feature / model change (e.g., a capability / feature / model ID field, a parameter setting ID field, etc.) can be added to the UCI information configuration. In this case, since the configuration and / or size of another UCI payload can be changed by this field, when the UCI is encoded in two parts, this field can be configured to be included in the first part.

[0398] Meanwhile, similar to Embodiment 2, in Embodiment 1 (i.e., according to the combination of Embodiment 1 and Embodiment 2), the UE can report the report related to the capability / function / model change to the base station. In this case, the UCI report setting value can be changed to a lower layer signal (e.g., MAC-CE and / or DCI) according to the report related to the capability / function / model change. If an ID related to the capability / function / model / configuration is introduced / defined, and the UCI report setting value is linked / mapped to the corresponding ID, the capability / function / model change report of the UE can be performed based on the ID, and the operation of Embodiment 1 (i.e., the change of the related UCI report setting value) can be performed by the base station for the confirmation / ACK indication of the corresponding report. Alternatively, the operation of Embodiment 1 (i.e., the change of the related UCI report setting value) can also be performed by the corresponding ID indication from the base station.

[0399] Meanwhile, in Embodiment 1 and / or Embodiment 2 described above, the UCI report can be a UCI report on PUCCH / PUSCH, or can be a report via MAC-CE.

[0400] Figure 17 A signaling procedure between a network and a UE for a method of transmitting and receiving uplink control information according to an embodiment of the disclosure is exemplified.

[0401] Figure 20 A signaling between a network (e.g., TRP 1, TRP 2) and a terminal (i.e., UE) in a multiple TRP (i.e., M-TRP, or multiple cells, hereinafter, all TRPs can be replaced with cells) scenario to which the method proposed in the disclosure can be applied is exemplified.

[0402] Here, the UE / network is only an example, and can be replaced with various devices as described below in Figure 17 Figure 17 This is only for convenience of explanation, and does not limit the scope of the disclosure. In addition, Figure 17 Some steps shown in the above can be omitted according to circumstances and / or settings.

[0403] ​In the following description, a network can be one base station including a plurality of TRPs, and can be one cell including a plurality of TRPs. For example, an ideal / non-ideal backhaul can be configured between TRP 1 and TRP 2 constituting a network. In addition, the following description is based on a plurality of TRPs, but this can be equally extended and applied to transmission through a plurality of panels. In addition, in the present disclosure, the operation of the UE receiving a signal from the TRP 1 / TRP 2 can also be interpreted / described as (or can be) the operation of the UE receiving a signal from the network (via / using TRP 1 / 2), and the operation of the UE transmitting a signal to the TRP 1 / TRP 2 can also be interpreted / described as (or can be) the operation of the UE transmitting a signal to the network (via / using TRP 1 / TRP 2), and vice versa.

[0404] In addition, as described above, the "TRP" can be applied by replacing with expressions such as a panel, an antenna array, a cell (e.g., a macro cell / small cell / pico cell, etc.), a transmission point (TP), a base station (gNB, etc.). As described above, the TRP can be distinguished according to information (e.g., an index, an identifier (ID)) about a CORESET group (or a CORESET pool). For example, when one UE is configured to perform transmission and reception with a plurality of TRPs (or cells), this can mean that a plurality of CORESET groups (or CORESET pools) are configured for one UE. Such configuration of the CORESET group (or CORESET pool) can be performed through higher layer signaling (e.g., RRC signaling, etc.). Additionally, the base station can be a general term for an object to which a UE transmits and receives data. For example, the base station can be a concept including one or more transmission points (TP), one or more transmission and reception points (TRP), etc. Additionally, the TP and / or TRP can include a panel, a transmission and reception unit, etc. of the base station.

[0405] Referring to Figure 17 For convenience of explanation, signaling between a single network (e.g., a base station) and a UE is considered, but the signaling method can be extended and applied to signaling between a plurality of TRPs and a plurality of UEs.

[0406] Referring to Figure 17 The UE can transmit a UE report to the network (S1701).

[0407] Here, according to the embodiment 2, the UE can perform the operation of transmitting information on the capability of the UE and / or information on the AI / ML model (related to the UCI report) and / or information on the related feature / function to the network, and such information can be collectively referred to as UE information. For example, the UE can report the supported set value for a specific parameter of the corresponding feature, the range of the set value, and / or the related ID as the UE information. Such UE reporting can be reported periodically / non-periodically / semi-persistently, or can be reported based on an event (i.e., whenever the information changes).

[0408] Further, according to the embodiment 2, based on (e.g., along with) the UE report on the change of the capability of the UE and / or the AI / ML function and / or model related to the UCI report, the set value of one or more parameters among the plurality of parameters related to the UCI report (i.e., the operation of the UCI report and / or the content of the UCI report, etc.) can be changed.

[0409] Here, the configuration for one or more parameters of the UCI report can be automatically applied based on (e.g., along with) the UE report on the change of the capability of the UE and / or the AI / ML function and / or model related to the UCI report. For example, according to the embodiment 2-1, the set value of the one or more parameters can be changed from the reporting time of the UE report or from a specific offset after the UE receives a response to the UE report from the network (e.g., the base station). Alternatively, according to the embodiment 2-2, the UE report and the UCI report can be transmitted together.

[0410] Further, according to the combination of the embodiment 1 and the embodiment 2, the UE performs the operation of transmitting the UE report, and based on (e.g., along with) the UE report on the change of the capability of the UE and / or the AI / ML function and / or model related to the UCI report, the network (e.g., the base station) can transmit low-layer signaling to the UE to change the set value of one or more parameters related to the UCI report.

[0411] In the following Figure 17 description, for ease of explanation, it is assumed that the operation of transmitting the UE report by the UE and the operation of transmitting the low-layer signaling for changing the set value of one or more parameters related to the UCI report by the network are performed according to the combination of the embodiment 1 and the embodiment 2.

[0412] However, according to the embodiment 1, the operation of the UE report by the UE can not be performed, and according to the embodiment 2, the set value of one or more parameters related to the UCI report can be automatically changed according to the UE report of the UE.

[0413] The network transmits configuration information to the UE (S1702).

[0414] Here, the configuration information can be transmitted via higher layer signaling (e.g., RRC signaling).

[0415] Additionally, the configuration information can include the configuration information related to the UCI reporting according to Embodiment 1. For example, it can include the CSI reporting configuration information.

[0416] Further, according to Embodiment 1, the configuration information can include the set values of the plurality of parameters related to the UCI reporting (e.g., per ID (e.g., AI / ML function ID and / or model ID)), a plurality of configuration candidate values, a range of the set values, and / or a default / initial set value. Further, based on the UE report reported in step S1701, the configuration information of the related AI / ML function and / or model can be included.

[0417] The UE can transmit the UCI report to the network based on the configuration information (S1703).

[0418] Here, the UCI refers to the control information that the UE transmits to the network (e.g., base station). For example, it can include, but is not limited to, at least one of SR, HARQ-ACK, and / or CSI.

[0419] Further, the UCI can be transmitted via a physical channel (e.g., PUCCH and / or PUSCH) or via a MAC CE.

[0420] Further, although not exemplified in Figure 17 , the network can instruct the UE to trigger / enable / deactivate the UCI reporting before transmitting the UCI report.

[0421] The network transmits a report update to the UE (S1704).

[0422] Here, the term report update is for convenience of explanation, and the disclosure is not limited thereto. That is, the report update can refer to information for changing the set values of one or more parameters among the plurality of parameters related to the UCI reporting in the configuration information of step S1702, and can be transmitted via lower layer signaling (e.g., MAC-CE and / or DCI). In other words, the set values of one or more parameters among the plurality of parameters related to the UCI reporting can be changed by the lower layer signaling (e.g., MAC-CE and / or DCI) without resetting the configuration information by the higher layer signaling (e.g., RRC signaling). That is, for one UCI report set by the configuration information, the set values of some parameters of the UCI report can be set / indicated by the lower layer signaling without reconfiguring other UCI reports by the higher layer signaling.

[0423] Here, according to the embodiment 1, the configuration information can include a plurality of candidate setting values or a range of setting values of the one or more parameters, and the setting value of the one or more parameters (i.e., the report update) can be indicated by the low layer signaling within the plurality of candidate setting values or the range of setting values.

[0424] Alternatively, according to the embodiment 1, the configuration information can not include the setting value of the one or more parameters, or can include a default value, and the setting value of the one or more parameters can be indicated by the low layer signaling.

[0425] Further, according to the embodiment 1, the setting value of the one or more parameters (i.e., the report update) can be applied from a time point after receiving (or transmitting by the network) the low layer signaling or after transmitting (or receiving by the network) the response of the UE to the low layer signaling with a specific offset.

[0426] Further, according to the embodiment 1, the setting value of the one or more parameters can be explicitly indicated by the low layer signaling, or indirectly indicated by indicating a specific identifier (ID) associated with the UCI report (e.g., a function ID or a model ID associated with the UCI report).

[0427] The UE transmits the UCI report to the network based on the configuration information (S1705).

[0428] Here, as described above, the UCI refers to control information transmitted by the UE to the network (e.g., a base station), and can include, but is not limited to, at least one of an SR, a HARQ-ACK, and / or a CSI.

[0429] Additionally, the UCI can be transmitted via a physical channel (e.g., a PUCCH and / or a PUSCH) or via a MAC CE.

[0430] Additionally, although not exemplified in the Figure 18 , the network can indicate the UE to trigger / enable / deactivate the UCI report before transmitting the UCI report. Further, according to the above-described embodiment 1, the UCI report can be triggered / activated in the low layer signaling for indicating / changing the setting value of one or more parameters among a plurality of parameters related to the UCI report. Alternatively, the low layer signaling for triggering / activating the UCI report can be separately transmitted to the UE from the low layer signaling for indicating / changing the setting value of one or more parameters among a plurality of parameters associated with the UCI report.

[0431] Here, according to Embodiment 1, the UCI report can be performed based on the setting values ​​of parameters configured by the configuration information, and also based on the setting values ​​of parameters set by report updates (i.e., indication / change of one or more parameter setting values ​​via low-layer signaling). Furthermore, according to Embodiment 2 (i.e., step S1704 omitted), the UCI report can be performed based on the setting values ​​of parameters set by the configuration information, and also based on the setting values ​​of parameters determined according to changes in UE capabilities / functions / models in the UE's UE report.

[0432] Furthermore, according to Implementation 1, the payload size of the UCI report can be configured to a maximum value, and some bits or code points in the payload of the UCI report can be left unused based on changes in the settings of one or more parameters. Alternatively, if a change in the settings of one or more parameters causes the payload size of the UCI report to exceed the resources allocated for transmitting the UCI report, some information in the UCI report can be omitted, or the UCI report can include specific predefined information (e.g., fallback report, UCI payload capacity exceeded status).

[0433] Furthermore, according to the combination of implementation methods 1 and 2, the network can change the setting value of one or more parameters based on the UE report regarding changes in AI / ML functions and / or models related to the UE's capabilities and / or UCI reports. In this case, the network can instruct the UE to change the setting value of one or more parameters through low-level signaling (i.e., report update).

[0434] Figure 18 This is a schematic diagram illustrating UE operation of a method for sending and receiving uplink control information according to an embodiment of the present disclosure.

[0435] Figure 18 An example of UE operation based on the previously proposed method is shown. Figure 18 The examples provided are for illustrative purposes only and do not limit the scope of this disclosure. Figure 18 Some steps shown can be omitted depending on the situation and / or settings. Furthermore, Figure 20 The UE in the example is only, and can be implemented as follows Figure 20 The apparatus illustrated in the example. For example, Figure 20 The processor (102 / 202) can control the transceiver (106 / 206) to send and receive channels / signals / data / information, and can also control... Figure 18 The processor (102 / 202) stores the transmitted or received channels / signals / data / information, etc., in the memory (104 / 204).

[0436] Here, although in Figure 18The UE can transmit information on the capability of the UE and / or information on the AI / ML model (related to the UCI reporting) and / or information on the related feature / functionality to the base station, although not illustrated in the above, and such information can be collectively referred to as UE information. For example, the UE can report the supported set value of a specific parameter for the feature, the range of the set value, and / or the related ID as the UE information. Such UE reporting can be reported periodically / aperiodically / semi-persistently, or can be reported based on an event (i.e., whenever the information changes).

[0437] Referring to Figure 18 , the UE receives configuration information related to the UCI reporting from the base station (S1801).

[0438] Here, the configuration information can be transmitted via higher layer signaling (e.g., RRC signaling). For example, it can include CSI reporting configuration information.

[0439] In addition, according to Embodiment 1, the configuration information can include a set value, a plurality of set candidate values, a range of set values, and / or a default / initial set value, etc., of a plurality of parameters related to the UCI reporting (e.g., operation of the UCI reporting and / or content within the UCI reporting). In addition, based on the UE report reported in step S1701, configuration information of the related AI / ML function and / or model can be included.

[0440] The UE transmits the UCI report to the base station based on the configuration information (S1802).

[0441] Here, the UCI refers to control information that the UE transmits to the base station. For example, it can include, but is not limited to, at least one of SR, HARQ-ACK, and / or CSI.

[0442] In addition, the UCI can be transmitted via a physical channel (e.g., PUCCH and / or PUSCH) or via a MAC CE.

[0443] Here, according to Embodiment 1 and / or Embodiment 2 described above, the set value for one or more parameters among the plurality of parameters related to the UCI reporting in the configuration information can be changed without reconfiguring the configuration information. That is, for one UCI report configured by the configuration information, the set value of some parameters of the UCI report can be changed without reconfiguring other UCI reports through higher layer signaling.

[0444] Here, according to Embodiment 1, although not exemplified in Figure 18 , the UE can receive information for changing the set value for one or more parameters among the plurality of parameters related to the UCI reporting in the configuration information from the base station via lower layer signaling.

[0445] For example, the configuration information can include a plurality of candidate setting values or a range of setting values for the one or more parameters, and can indicate the setting value for the one or more parameters via the low layer signaling (i.e., the report update) among the plurality of candidate setting values or the range of setting values.

[0446] Alternatively, the configuration information can not include the setting value for the one or more parameters, or can include a default value, and the setting value for the one or more parameters can be indicated by the low layer signaling.

[0447] Further, the setting value for the one or more parameters can be applied from a specific offset point in time after receiving (or by the base station transmitting) the low layer signaling for the setting value for the one or more parameters or after transmitting (or by the base station receiving) the UE's response to the low layer signaling.

[0448] Additionally, the setting value for the one or more parameters can be explicitly indicated by the low layer signaling for the setting value for the one or more parameters, or indirectly indicated by indicating a specific identifier (ID) associated with the UCI report (e.g., a function ID or a model ID associated with the UCI report).

[0449] Further, although not shown in Figure 18 , the UE can receive the low layer signaling triggering the UCI report from the base station before transmitting the UCI report. In this case, the low layer signaling for the setting value for the one or more parameters can trigger the UCI report, or the low layer signaling triggering the UCI report can be separately transmitted.

[0450] Additionally, although not exemplified in Figure 19 , the UE can transmit a UE report to the base station regarding the UE's capability and / or a change in an artificial intelligence (AI) / machine learning (ML) function and / or model related to the UCI report.

[0451] In this case, the setting value for the one or more parameters can be changed based on the UE report.

[0452] For example, according to a combination of Embodiment 1 and Embodiment 2, the UE transmits the UE report, and based on the UE report regarding the change in the AI / ML function and / or model related to the UE's capability and / or the UCI report (e.g., along with the UE report), the base station can transmit the low layer signaling to the UE to change the setting value for the one or more parameters related to the UCI report.

[0453] As another example, according to implementation 2, based on (e.g., along with) the UE report on the capability of the UE and / or the change of the AI / ML function and / or model related to the UCI reporting, the set value of one or more parameters among the plurality of parameters related to the UCI reporting (i.e., the operation of the UCI reporting and / or the content of the UCI reporting, etc.) can be changed.

[0454] Here, based on (e.g., along with) the UE report on the capability of the UE and / or the change of the AI / ML function and / or model related to the UCI reporting, the set value of one or more parameters of the corresponding UCI reporting can be automatically applied. For example, according to implementation 2-1, the set value for one or more parameters can be changed from a specific time point after the reporting time of the UE report. In other words, the set value for one or more parameters can be changed from the reporting time of the UE report or from a specific offset after the response of the base station to the UE report. Alternatively, according to implementation 2-2, the UE report and the UCI report can be transmitted together.

[0455] In addition, the UCI reporting is performed based on the configuration information. Here, according to implementation 1, it can be performed based on the set value of the parameter configured by the configuration information, and also based on the set value of the parameter configured by the low layer signaling for the set value of one or more parameters. In addition, according to implementation 2, the UCI reporting can be performed based on the set value of the parameter set by the configuration information, and also based on the set value of the parameter determined according to the change of the UE capability / function / model of the UE in the UE report of the UE.

[0456] Here, the payload size of the UCI reporting is configured as a maximum value, and some bits or code points in the payload of the UCI reporting can not be used based on the change of the set value of the one or more parameters.

[0457] In addition, based on the change of the set value for the one or more parameters, the payload size of the UCI reporting exceeds the resources allocated for the transmission of the UCI reporting, some information within the UCI reporting can be omitted, or specific predefined information can be included in the UCI reporting.

[0458] Figure 19 is a schematic diagram illustrating a base station operation for a method of transmitting and receiving uplink control information according to an implementation of the disclosure.

[0459] Figure 19 The base station operation based on the previously proposed method is illustrated. Figure 19 The examples of are provided for ease of explanation and do not limit the scope of the disclosure. Figure 19 Some of the steps shown in can be omitted according to circumstances and / or settings. In addition, Figure 20The base station in FIG. 1 is merely an example and can be implemented as the following Figure 20 The processor (102 / 202) of FIG. 1 can control the transceiver (106 / 206) to transmit and receive a channel / signal / data / information, etc., and can also control Figure 20 The processor (102 / 202) of FIG. 1 stores the transmitted or received channel / signal / data / information, etc., in the memory (104 / 204). Figure 19

[0460] Here, although not shown in FIG. 1, the base station can receive information on the capability of the UE and / or information on the AI / ML model (related to the UCI report) and / or information on the related feature / function from the UE, and such information can be collectively referred to as UE information. For example, the UE can report the supported set value for a specific parameter for the feature, the range of the set value, and / or the related ID as the UE information. Such UE reporting can be reported periodically / non-periodically / semi-persistently, or can be reported based on an event (i.e., whenever the information changes). Figure 19 Referring to FIG. 19,

[0461] The base station transmits configuration information related to the UCI report to the UE (S1901). Figure 19 Here, the configuration information can be transmitted via higher layer signaling (e.g., RRC signaling). For example, it can include CSI report configuration information.

[0462] In addition, according to Embodiment 1, the configuration information can include a set value, a plurality of set candidate values, a range of set values, and / or a default / initial set value, etc., for a plurality of parameters related to the UCI report (e.g., operation of the UCI report and / or content within the UCI report). In addition, based on the UE report reported in step S1701, configuration information of the related AI / ML function and / or model can be included.

[0463] The base station receives the UCI report from the UE based on the configuration information (S1902).

[0464] Here, the UCI refers to control information transmitted by the UE to the base station. For example, it can include, but is not limited to, at least one of SR, HARQ-ACK, and / or CSI.

[0465] In addition, the UCI can be transmitted via a physical channel (e.g., PUCCH and / or PUSCH) or via a MAC CE.

[0466]

[0467] ​​Here, according to the above-described Embodiment 1 and / or Embodiment 2, the set value of one or more parameters among the plurality of parameters related to the UCI report in the configuration information can be changed without reconfiguring the configuration information. That is, for one UCI report configured by the configuration information, the set value of some parameters of the UCI report can be changed without reconfiguring other UCI reports by higher layer signaling.

[0468] Here, according to Embodiment 1, although not illustrated in Figure 19 , the base station can transmit, to the UE via the lower layer signaling, information for changing the set value of one or more parameters among the plurality of parameters related to the UCI report in the configuration information.

[0469] For example, the configuration information can include a plurality of candidate set values or a range of set values for the one or more parameters, and the set value for the one or more parameters can be indicated within the plurality of candidate set values or the range of set values via the lower layer signaling (i.e., report update).

[0470] Alternatively, the configuration information can not include the set value for the one or more parameters, or can include a default value, and the set value for the one or more parameters can be indicated by the lower layer signaling.

[0471] Further, the set value for the one or more parameters can be applied from a time point after a certain offset from when the lower layer signaling for the set value for the one or more parameters is received (or transmitted by the base station) or from when a response of the UE to the lower layer signaling is transmitted (or received by the base station).

[0472] Additionally, the set value for the one or more parameters can be explicitly indicated by the lower layer signaling for the set value for the one or more parameters, or indirectly indicated by indicating a specific identifier (ID) associated with the UCI report (e.g., a function ID or a model ID associated with the UCI report).

[0473] Further, although not shown in Figure 19 , the base station can transmit, to the UE, lower layer signaling triggering the UCI report before transmitting the UCI report. In this case, the lower layer signaling for the set value for the one or more parameters can trigger the UCI report, or the lower layer signaling triggering the UCI report can be separately transmitted.

[0474] Additionally, although not illustrated in General apparatus to which the present disclosure can be applied , the base station can receive, from the UE, a UE report on a change in capability of the UE and / or artificial intelligence (AI) / machine learning (ML) function and / or model related to the UCI report.

[0475] In this case, the setting value for one or more parameters can be changed based on the UE report.

[0476] For example, according to a combination of Embodiment 1 and Embodiment 2, the base station receives the UE report from the UE, and based on the UE report about the change in the capability of the UE and / or the AI / ML function and / or model related to the UCI report (e.g., along with the UE report), the base station can transmit low-layer signaling to the UE to change the setting value for one or more parameters related to the UCI report.

[0477] As another example, according to Embodiment 2, based on the UE report about the change in the capability of the UE and / or the AI / ML function and / or model related to the UCI report (e.g., along with), the setting value for one or more parameters among the plurality of parameters related to the UCI report (i.e., operation of the UCI report and / or content of the UCI report, etc.) can be changed.

[0478] Here, based on the UE report about the change in the capability of the UE and / or the AI / ML function and / or model related to the UCI report (e.g., along with), the setting value for one or more parameters corresponding to the UCI report can be automatically applied. For example, according to Embodiment 2-1, the setting value for one or more parameters can be changed from a specific time point after the reporting time of the UE report. In other words, the setting value for one or more parameters can be changed from the reporting time of the UE report or from a specific offset after receiving the response of the base station to the UE report. Alternatively, according to Embodiment 2-2, the UE report and the UCI report can be transmitted together.

[0479] In addition, the UCI report is performed based on the configuration information. Here, according to Embodiment 1, it can be performed based on the setting value of the parameter configured by the configuration information, and also based on the setting value of the parameter configured by the low-layer signaling of the setting value for one or more parameters. In addition, according to Embodiment 2, the UCI report can be performed based on the setting value of the parameter set by the configuration information, and also based on the setting value of the parameter determined according to the change in the UE capability / function / model of the UE in the UE report of the UE.

[0480] Here, the payload size of the UCI report is configured as a maximum value, and some bits or code points within the payload of the UCI report can not be used based on the change in the setting value of the one or more parameters.

[0481] In addition, based on the change in the setting value of the one or more parameters, the payload size of the UCI report exceeds the resources allocated for the transmission of the UCI report, and some information in the UCI report can be omitted, or specific predefined information can be included in the UCI report.

[0482] Figure 20

[0483] Figure 20 is a schematic diagram illustrating a block diagram of a wireless communication device according to an embodiment of the disclosure.

[0484] Referring to ​ , the first wireless device 100 and the second wireless device 200 can transmit and receive wireless signals through various radio access technologies (e.g., LTE, NR).

[0485] The first wireless device 100 can include one or more processors 102 and one or more memories 104, and can additionally include one or more transceivers 106 and / or one or more antennas 108. The processor 102 can control the memory 104 and / or the transceiver 106, and can be configured to implement the descriptions, functions, procedures, suggestions, methods, and / or operational flowcharts disclosed in the disclosure. For example, the processor 102 can generate first information / signal by processing information in the memory 104, and then transmit a wireless signal including the first information / signal through the transceiver 106. In addition, the processor 102 can receive a wireless signal including second information / signal through the transceiver 106, and then store information obtained by signal processing of the second information / signal in the memory 104. The memory 104 can be connected to the processor 102 and can store various information related to the operation of the processor 102. For example, the memory 104 can store software code including commands for performing all or part of the processes controlled by the processor 102 or for performing the descriptions, functions, procedures, suggestions, methods, and / or operational flowcharts disclosed in the disclosure. Here, the processor 102 and the memory 104 can be a part of a communication modem / circuit / chip designed to implement a wireless communication technology (e.g., LTE, NR). The transceiver 106 can be connected to the processor 102 and can transmit and / or receive wireless signals through one or more antennas 108. The transceiver 106 can include a transmitter and / or a receiver. The transceiver 106 can be used together with an RF (Radio Frequency) unit. In the disclosure, a wireless device can mean a communication modem / circuit / chip.

[0486] The second wireless device 200 can include one or more processors 202 and one or more memories 204, and can additionally include one or more transceivers 206 and / or one or more antennas 208. The processor(s) 202 can control the memory(s) 204 and / or the transceiver(s) 206, and can be configured to implement the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure. For example, the processor(s) 202 can generate third information / signal by processing information in the memory(s) 204, and then transmit a wireless signal including the third information / signal through the transceiver(s) 206. Also, the processor(s) 202 can receive a wireless signal including fourth information / signal through the transceiver(s) 206, and then store information obtained by signal processing of the fourth information / signal in the memory(s) 204. The memory(s) 204 can be connected to the processor(s) 202 and can store various information related to operations of the processor(s) 202. For example, the memory(s) 204 can store software code including commands for performing all or part of processes controlled by the processor(s) 202 or for performing the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure. Here, the processor(s) 202 and the memory(s) 204 can be part of a communication modem / circuitry / chip designed to implement a wireless communication technology (e.g., LTE, NR). The transceiver(s) 206 can be connected to the processor(s) 202 and can transmit and / or receive a wireless signal through the one or more antennas 208. The transceiver(s) 206 can include a transmitter and / or a receiver. The transceiver(s) 206 can be used together with an RF unit. In the present disclosure, a wireless device can mean a communication modem / circuitry / chip.

[0487] Hereinafter, the hardware elements of the wireless devices 100, 200 will be described in more detail. Without limitation, one or more protocol layers can be implemented by the one or more processors 102, 202. For example, the one or more processors 102, 202 can implement one or more layers (e.g., functional layers such as PHY, MAC, RLC, PDCP, RRC, SDAP). The one or more processors 102, 202 can generate one or more PDUs (Protocol Data Units) and / or one or more SDUs (Service Data Units) according to the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts included in the present disclosure. The one or more processors 102, 202 can generate messages, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in the present disclosure. The one or more processors 102, 202 can generate signals (e.g., baseband signals) including the PDUs, SDUs, messages, control information, data, or information according to the functions, procedures, proposals, and / or methods disclosed in the present disclosure to provide the same to the one or more transceivers 106, 206. The one or more processors 102, 202 can receive signals (e.g., baseband signals) from the one or more transceivers 106, 206 and obtain the PDUs, SDUs, messages, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in the present disclosure.

[0488] The one or more processors 102, 202 can be referred to as controllers, microcontrollers, microprocessors, or microcomputers. The one or more processors 102, 202 can be implemented by hardware, firmware, software, or a combination thereof. For example, one or more ASICs (Application Specific Integrated Circuits), one or more DSPs (Digital Signal Processors), one or more DSPDs (Digital Signal Processors Devices), one or more PLDs (Programmable Logic Devices), or one or more FPGAs (Field Programmable Gate Arrays) can be included in the one or more processors 102, 202. The descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in the present disclosure can be implemented by using firmware or software, and the firmware or software can be implemented as including modules, procedures, functions, and the like. Firmware or software configured to perform the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in the present disclosure can be included in the one or more processors 102, 202, or can be stored in the one or more memories 104, 204 and driven by the one or more processors 102, 202. The descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in the present disclosure can be implemented by using firmware or software in the form of codes, commands, and / or command sets.

[0489] One or more memories 104, 204 can be connected to one or more processors 102, 202 and can store data, signals, messages, information, programs, codes, instructions, and / or commands in various forms. One or more memories 104, 204 can be configured with ROM, RAM, EPROM, flash memory, hard drives, registers, cache memories, computer-readable storage media, and / or combinations thereof. One or more memories 104, 204 can be located internal and / or external to one or more processors 102, 202. In addition, one or more memories 104, 204 can be connected to one or more processors 102, 202 by various technologies such as wired or wireless connections.

[0490] The one or more transceivers 106, 206 can transmit user data, control information, wireless signals / channels, etc. mentioned in the methods and / or operational flowcharts, etc. of the disclosure to one or more other apparatuses. The one or more transceivers 106, 206 can receive user data, control information, wireless signals / channels, etc. mentioned in the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts, etc. disclosed in the disclosure from one or more other apparatuses. For example, the one or more transceivers 106, 206 can be connected to the one or more processors 102, 202 and can transmit and receive wireless signals. For example, the one or more processors 102, 202 can control the one or more transceivers 106, 206 to transmit user data, control information, or wireless signals to one or more other apparatuses. In addition, the one or more processors 102, 202 can control the one or more transceivers 106, 206 to receive user data, control information, or wireless signals from one or more other apparatuses. In addition, the one or more transceivers 106, 206 can be connected to the one or more antennas 108, 208, and the one or more transceivers 106, 206 can be configured to transmit and receive user data, control information, wireless signals / channels, etc. mentioned in the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts, etc. disclosed in the disclosure through the one or more antennas 108, 208. In the disclosure, the one or more antennas can be a plurality of physical antennas or a plurality of logical antennas (for example, antenna ports). The one or more transceivers 106, 206 can convert received wireless signals / channels, etc. from RF band signals to baseband signals to process received user data, control information, wireless signals / channels, etc. by using the one or more processors 102, 202. The one or more transceivers 106, 206 can convert user data, control information, wireless signals / channels, etc. processed by using the one or more processors 102, 202 from baseband signals to RF band signals. Accordingly, the one or more transceivers 106, 206 can include (analog) oscillators and / or filters.

[0491] The above-described embodiments are combinations of elements and features of the disclosure in a predetermined form. Each of the elements or features should be considered selectively unless explicitly mentioned otherwise. Each of the elements or features can be implemented in a form not combined with other elements or features. In addition, embodiments of the disclosure can include a combination of some elements and / or features. The order of the operations described in the embodiments of the disclosure can be changed. Some elements or features of one embodiment can be included in other embodiments, or can be replaced with corresponding elements or features of other embodiments. It is obvious that the embodiments can include claims not explicitly mentioned in relation to other claims, or can be included as new claims by modification after the application.

[0492] It will be apparent to those skilled in the relevant arts that the present disclosure can be implemented in other specific forms without departing from the spirit or essential character of the disclosure. The detailed description is, therefore, not to be taken in a limiting sense. The scope of the present disclosure is to be determined solely by the appended claims, construed in accordance with the full range of equivalents, and modifications to the disclosed implementations are possible and within the scope of the present disclosure.

[0493] The scope of the present disclosure includes software or machine-executable commands (e.g., operating systems, applications, firmware, programs, etc.) that perform operations according to the methods of various embodiments in an apparatus or computer, and non-transitory computer-readable media that cause the software or commands, etc., to be stored and executable in the apparatus or computer. Commands that can be used to program a processing system to perform the features described in the present disclosure can be stored in a storage medium or computer-readable storage medium and the features described in the present disclosure can be implemented by using a computer program product including such a storage medium. The storage medium can include a high-speed random access memory such as a DRAM, SRAM, DDR RAM, or other random access solid state storage device, but is not limited thereto, and it can include a non-volatile memory such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. The memory optionally includes one or more storage devices that are located remotely from the processor. The memory, or alternatively the non-volatile memory device in the memory, includes a non-transitory computer-readable storage medium. The features described in the present disclosure can be stored in any one of machine-readable media to control the hardware of the processing system, and can be integrated into software and / or firmware that allows the processing system to interact with other mechanisms with results from the embodiments of the present disclosure. Such software or firmware can include application code, device drivers, operating systems, and execution environments / containers, but is not limited thereto.

[0494] Here, the wireless communication technology implemented in the wireless device 100, 200 of the disclosure can include narrowband Internet of Things for low-power communication and LTE, NR, and 6G. Here, for example, the NB-IoT technology can be an example of LPWAN (Low Power Wide Area Network) technology, can be implemented in standards such as LTE Cat NB1 and / or LTE Cat NB2, and is not limited to the above-mentioned names. Additionally or alternatively, the wireless communication technology implemented in the wireless device 100, 200 of the disclosure can perform communication based on LTE-M technology. Here, for example, the LTE-M technology can be an example of LPWAN technology and can be referred to by various names such as eMTC (enhanced Machine Type Communication) or the like. For example, the LTE-M technology can be implemented in at least any of various standards including 1) LTE CAT 0; 2) LTE Cat M1; 3) LTE Cat M2; 4) LTE non-BL (non-bandwidth limited); 5) LTE-MTC; 6) LTE Machine Type Communication; and / or 7) LTE M or the like, and is not limited to the above-mentioned names. Additionally or alternatively, the wireless communication technology implemented in the wireless device 100, 200 of the disclosure can include at least any of ZigBee, Bluetooth, and Low Power Wide Area Network (LPWAN) considering low-power communication, and is not limited to the above-mentioned names. For example, the ZigBee technology can generate a PAN (Personal Area Network) related to small / low-power digital communication based on various standards such as IEEE 802.15.4 or the like, and can be referred to by various names.

[0495] Industrial applicability

[0496] The method proposed by the disclosure is mainly explained based on examples applied to 3GPP LTE / LTE-A, 5G system, but can also be applied to various wireless communication systems other than 3GPP LTE / LTE-A, 5G system.

Claims

1. A method performed by a user equipment (UE) in a wireless communication system, the method comprising: Receive configuration information related to the uplink control information (UCI) report from the base station; as well as The UCI report is sent to the base station based on the configuration information. Among the multiple parameters related to the UCI report in the configuration information, the setting value of at least one parameter can be changed without reconfiguring the configuration information.

2. The method according to claim 1, wherein, The configuration information includes multiple candidate settings or a range of settings for the at least one parameter, and The setting value for at least one parameter is indicated by low-level signaling within the plurality of candidate setting values ​​or within the range of the setting values.

3. The method according to claim 1, wherein, The configuration information does not include the set value for the at least one parameter or includes a default value, and The setting value for the at least one parameter is indicated by lower-level signaling.

4. The method according to claim 1, wherein, The setting value for the at least one parameter is applied starting from a specific offset after receiving low-level signaling for the setting value for the at least one parameter or after the UE sends a response to the low-level signaling.

5. The method according to claim 1, wherein, The setting value for the at least one parameter is explicitly indicated by low-level signaling for the setting value for the at least one parameter, or indirectly indicated by indicating a specific identifier ID associated with the UCI report.

6. The method according to claim 1, wherein, The low-level signaling used to trigger the UCI report for the set value of the at least one parameter, or the low-level signaling that triggers the UCI report is sent separately.

7. The method according to claim 1, wherein, The payload size of the UCI report is configured to the maximum value, and some bits or code points in the payload of the UCI report are not used based on changes to the set value for the at least one parameter.

8. The method according to claim 1, wherein, Based on the fact that the payload size of the UCI report exceeds the resources allocated for the transmission of the UCI report due to a change in the set value of the at least one parameter, some information in the UCI report is omitted, or specific predefined information is included in the UCI report.

9. The method according to claim 1, further comprising: Send to the base station a UE report relating to changes in the UE's capabilities and / or artificial intelligence (AI) / machine learning (ML) functions and / or models related to the UCI report.

10. The method according to claim 9, wherein, The setting value for at least one parameter is changed based on the UE report.

11. The method according to claim 10, wherein, The setting value for the at least one parameter changes starting from a specific point in time after the UE report is reported.

12. The method according to claim 10, wherein, The UE report and the UCI report are sent together.

13. A user equipment (UE) operating in a wireless communication system, the UE comprising: At least one transceiver, the at least one transceiver being used to transmit and receive wireless signals; as well as At least one processor, the at least one processor being used to control the at least one transceiver, Wherein, the at least one processor is configured to: Receive configuration information related to the uplink control information (UCI) report from the base station; and The UCI report is sent to the base station based on the configuration information. Among the multiple parameters related to the UCI report in the configuration information, the setting value of at least one parameter can be changed without reconfiguring the configuration information.

14. At least one non-transitory computer-readable medium, said at least one non-transitory computer-readable medium storing at least one instruction, wherein, The at least one instruction can be executed by at least one processor to control the user equipment (UE): Receive configuration information related to the uplink control information (UCI) report from the base station; as well as The UCI report is sent to the base station based on the configuration information. Among the multiple parameters related to the UCI report in the configuration information, the setting value of at least one parameter can be changed without reconfiguring the configuration information.

15. A processing apparatus configured to control a user equipment (UE) in a wireless communication system, the processing apparatus comprising: At least one processor; as well as At least one computer memory, operatively connected to the at least one processor and storing instructions that perform operations based on execution by the at least one processor, the operations including: Receive configuration information related to the uplink control information (UCI) report from the base station; and The UCI report is sent to the base station based on the configuration information. Among the multiple parameters related to the UCI report in the configuration information, the setting value of at least one parameter can be changed without reconfiguring the configuration information.

16. A method performed by a base station in a wireless communication system, the method comprising: Send configuration information related to the uplink control information (UCI) report to the user equipment (UE); as well as The UCI report is received from the UE based on the configuration information. Among the multiple parameters related to the UCI report in the configuration information, the setting value of at least one parameter can be changed without reconfiguring the configuration information.

17. A base station operating in a wireless communication system, the base station comprising: At least one transceiver, the at least one transceiver being used to transmit and receive wireless signals; as well as At least one processor, the at least one processor being used to control the at least one transceiver, Wherein, the at least one processor is configured to: Send configuration information related to the uplink control information (UCI) report to the user equipment (UE); and The UCI report is received from the UE based on the configuration information. Among the multiple parameters related to the UCI report in the configuration information, the setting value of at least one parameter can be changed without reconfiguring the configuration information.

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