Method and device for transmitting / receiving channel state information in wireless communication system

Abstract

A method and a device for transmitting / receiving channel state information (CSI) in a wireless communication system are disclosed. The method according to an embodiment of the present disclosure may comprise the steps of: receiving CSI-related configuration information from a base station, the configuration information including information associated with a plurality of time instances; receiving a CSI-RS on K (K is an integer greater than 0) CSI-RS resources from the base station; and transmitting CSI to the base station, the CSI including a PMI corresponding to codebook indexes.

Description

Method and device for transmitting and receiving channel state information in a wireless communication system

[0001] The present disclosure relates to a wireless communication system, and more particularly, to a method and device for transmitting and receiving channel state information (CSI) in a wireless communication system.

[0002] Mobile communication systems were developed to provide voice services while ensuring user activity. However, they have expanded beyond voice to include data services. Currently, explosive growth in traffic is leading to resource shortages and users' demand for higher-speed services, necessitating a more advanced mobile communication system.

[0003] Next-generation mobile communication systems must support explosive data traffic growth, dramatically increasing data rates per user, a vastly increased number of connected devices, ultra-low end-to-end latency, and high energy efficiency. To achieve these goals, various technologies are being studied, including dual connectivity, massive multiple input multiple output (MIMO), in-band full duplex, non-orthogonal multiple access (NOMA), super wideband support, and device networking.

[0004] The technical problem of the present disclosure is to provide a method and device for transmitting and receiving channel state information for multiple time instances.

[0005] The technical problems to be achieved in the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned can be clearly understood by a person having ordinary skill in the technical field to which the present disclosure belongs from the description below.

[0006] According to one aspect of the present disclosure, a method performed by a user equipment (UE) in a wireless communication system may include: receiving configuration information related to channel state information (CSI) from a base station, wherein the configuration information includes information related to a plurality of time instances; receiving a CSI-reference signal (CSI-RS) from the base station on K (K is an integer greater than 0) CSI-RS resources; and transmitting the CSI to the base station, wherein the CSI includes a precoding matrix indicator (PMI) corresponding to codebook indices. The PMI may indicate precoding matrices for each of the remaining time instances excluding one or more time instances among the plurality of time instances.

[0007] In accordance with an additional aspect of the present disclosure, a method performed by a base station in a wireless communication system may include: transmitting configuration information related to channel state information (CSI) to a user equipment (UE), wherein the configuration information includes information related to a plurality of time instances; transmitting a CSI-reference signal (CSI-RS) to the UE on K (K being an integer greater than 0) CSI-RS resources; and receiving the CSI from the UE, wherein the CSI includes a precoding matrix indicator (PMI) corresponding to codebook indices. The PMI may indicate precoding matrices for each of the remaining time instances excluding one or more time instances among the plurality of time instances.

[0008] According to an embodiment of the present disclosure, accurate channel status can be obtained by reporting channel status information for multiple time instances.

[0009] In addition, according to an embodiment of the present disclosure, by excluding channel state information for time instances with relatively low accuracy for channel prediction, a more accurate channel state can be obtained and an increase in processing complexity can be prevented.

[0010] Additionally, according to an embodiment of the present disclosure, information loss due to compression can be minimized by considering the correlation between the length of the basis vector and the number of basis vectors in a codebook related to CSI reporting for multiple time instances.

[0011] In addition, according to an embodiment of the present disclosure, by considering the correlation between the number of CSI-RS resources for channel measurement and the length of the basis vector in a codebook related to CSI reporting for multiple time instances, it is possible to prevent an increase in processing complexity.

[0012] The effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects that are not mentioned will be clearly understood by a person having ordinary skill in the art to which the present disclosure pertains from the description below.

[0013] The accompanying drawings, which are incorporated in and are part of the detailed description to aid in understanding the present disclosure, provide embodiments of the present disclosure and, together with the detailed description, describe the technical features of the present disclosure.

[0014] Figure 1 illustrates the structure of a wireless communication system to which the present disclosure can be applied.

[0015] FIG. 2 illustrates a frame structure in a wireless communication system to which the present disclosure can be applied.

[0016] FIG. 3 illustrates a resource grid in a wireless communication system to which the present disclosure can be applied.

[0017] FIG. 4 illustrates a physical resource block in a wireless communication system to which the present disclosure can be applied.

[0018] FIG. 5 illustrates a slot structure in a wireless communication system to which the present disclosure can be applied.

[0019] FIG. 6 illustrates physical channels used in a wireless communication system to which the present disclosure can be applied and a general signal transmission and reception method using the same.

[0020] Figure 7 illustrates the classification of artificial intelligence.

[0021] Figure 8 illustrates a feed-forward neural network.

[0022] Figure 9 illustrates a recurrent neural network.

[0023] Figure 10 illustrates a convolutional neural network.

[0024] Figure 11 illustrates an auto encoder.

[0025] Figure 12 illustrates a functional framework for AI operation.

[0026] Figure 13 is a diagram illustrating segmentation AI inference.

[0027] Figure 14 illustrates the application of a functional framework in a wireless communication system.

[0028] Figure 15 illustrates the application of a functional framework in a wireless communication system.

[0029] Figure 16 illustrates the application of a functional framework in a wireless communication system.

[0030] FIG. 17 illustrates PMI for multiple time instances that are not faster than a CSI reference resource in a wireless communication system to which the present disclosure may be applied.

[0031] FIG. 18 illustrates PMI for multiple time instances not later than a CSI reference resource in a wireless communication system to which the present disclosure can be applied.

[0032] FIG. 19 illustrates PMI information indicated by a codebook for a specific codebook parameter in a wireless communication system to which the present disclosure can be applied.

[0033] FIG. 20 is a diagram illustrating a signaling procedure between a network and a UE for a method of transmitting and receiving channel state information according to one embodiment of the present disclosure.

[0034] FIG. 21 is a diagram illustrating the operation of a UE for a method for transmitting and receiving channel state information according to one embodiment of the present disclosure.

[0035] FIG. 22 is a diagram illustrating the operation of a base station for a method for transmitting and receiving channel state information according to one embodiment of the present disclosure.

[0036] FIG. 23 is a block diagram illustrating a wireless communication device according to one embodiment of the present disclosure.

[0037] Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. The detailed description set forth below, together with the accompanying drawings, is intended to explain exemplary embodiments of the present disclosure and is not intended to represent the only embodiments in which the present disclosure may be practiced. The following detailed description includes specific details to provide a thorough understanding of the present disclosure. However, one of ordinary skill in the art will appreciate that the present disclosure may be practiced without these specific details.

[0038] In some cases, to avoid obscuring the concepts of the present disclosure, known structures and devices may be omitted or illustrated in block diagram form focusing on the core functions of each structure and device.

[0039] In the present disclosure, when a component is said to be "connected," "coupled," or "connected" to another component, this may include not only a direct connection but also an indirect connection in which another component exists between them. Furthermore, the terms "comprises" or "has" in the present disclosure specify the presence of the mentioned features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0040] In this disclosure, terms such as “first,” “second,” etc. are used only to distinguish one component from another and are not used to limit the components, and do not limit the order or importance between the components unless specifically stated otherwise. Accordingly, within the scope of this disclosure, a first component in one embodiment may be referred to as a second component in another embodiment, and similarly, a second component in one embodiment may be referred to as a first component in another embodiment.

[0041] The terminology used herein is for the purpose of describing particular embodiments and is not intended to limit the scope of the claims. As used in the description of the embodiments and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. The term "and / or" as used herein may refer to any one of the associated enumerated items, or is meant to refer to and encompass any and all possible combinations of two or more of them. Furthermore, the use of " / " between words in this disclosure has the same meaning as "and / or" unless otherwise stated.

[0042] The present disclosure describes a wireless communication network or a wireless communication system, and an operation performed in a wireless communication network may be performed in a process of controlling the network and transmitting or receiving a signal from a device (e.g., a base station) that manages the wireless communication network, or may be performed in a process of transmitting or receiving a signal to or between terminals connected to the wireless network.

[0043] In the present disclosure, transmitting or receiving a channel means transmitting or receiving information or a signal through the channel. For example, transmitting a control channel means transmitting control information or a signal through the control channel. Similarly, transmitting a data channel means transmitting data information or a signal through the data channel.

[0044] Hereinafter, downlink (DL) refers to communication from a base station to a terminal, and uplink (UL) refers to communication from a terminal to a base station. In downlink, a transmitter may be part of a base station, and a receiver may be part of a terminal. In uplink, a transmitter may be part of a terminal, and a receiver may be part of a base station. A base station may be expressed as a first communication device, and a terminal may be expressed as a second communication device. A base station (BS) may be replaced by terms such as a fixed station, Node B, eNB (evolved-NodeB), gNB (Next Generation NodeB), BTS (base transceiver system), access point (AP: Access Point), network (5G network), AI (Artificial Intelligence) system / module, RSU (road side unit), robot, drone (UAV: Unmanned Aerial Vehicle), AR (Augmented Reality) device, VR (Virtual Reality) device, etc.In addition, the terminal may be fixed or mobile, and may be replaced with terms such as UE (User Equipment), MS (Mobile Station), UT (user terminal), MSS (Mobile Subscriber Station), SS (Subscriber Station), AMS (Advanced Mobile Station), WT (Wireless terminal), MTC (Machine-Type Communication) device, M2M (Machine-to-Machine) device, D2D (Device-to-Device) device, vehicle, RSU (road side unit), robot, AI (Artificial Intelligence) module, UAV (Unmanned Aerial Vehicle), AR (Augmented Reality) device, VR (Virtual Reality) device, etc.

[0045] The following technologies can be used in various wireless access systems, such as CDMA, FDMA, TDMA, OFDMA, and SC-FDMA. CDMA can be implemented using wireless technologies such as UTRA (Universal Terrestrial Radio Access) or CDMA2000. TDMA can be implemented using wireless technologies such as GSM (Global System for Mobile communications) / GPRS (General Packet Radio Service) / EDGE (Enhanced Data Rates for GSM Evolution). OFDMA can be implemented using wireless technologies such as IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802-20, and E-UTRA (Evolved UTRA). UTRA is part of UMTS (Universal Mobile Telecommunications System). 3GPP (3rd Generation Partnership Project) LTE (Long Term Evolution) is a part of E-UMTS (Evolved UMTS) that uses E-UTRA, and LTE-A (Advanced) / LTE-A pro is an evolved version of 3GPP LTE. 3GPP NR (New Radio or New Radio Access Technology) is an evolved version of 3GPP LTE / LTE-A / LTE-A pro.

[0046] For clarity, the description is based on the 3GPP communication system (e.g., LTE-A, NR), but the technical idea of ​​the present disclosure is not limited thereto. LTE refers to technology after 3GPP TS (Technical Specification) 36.xxx Release 8. Specifically, LTE technology after 3GPP TS 36.xxx Release 10 is referred to as LTE-A, and LTE technology after 3GPP TS 36.xxx Release 13 is referred to as LTE-A pro. 3GPP NR refers to technology after TS 38.xxx Release 15. LTE / NR may be referred to as a 3GPP system. "xxx" refers to a standard document detail number. LTE / NR may be collectively referred to as a 3GPP system. For background technology, terms, abbreviations, etc. used in the description of the present disclosure, reference may be made to matters described in standard documents published prior to the present disclosure. For example, reference may be made to the following documents.

[0047] For 3GPP LTE, see TS 36.211 (Physical channels and modulation), TS 36.212 (Multiplexing and channel coding), TS 36.213 (Physical layer procedures), TS 36.300 (General description), and TS 36.331 (Radio resource control).

[0048] For 3GPP NR, see TS 38.211 (Physical channels and modulation), TS 38.212 (Multiplexing and channel coding), TS 38.213 (Physical layer procedures for control), TS 38.214 (Physical layer procedures for data), TS 38.300 (Overall description of NR and New Generation-Radio Access Network (NG-RAN)), and TS 38.331 (Radio Resource Control Protocol Specification).

[0049] Abbreviations for terms that may be used in this disclosure are defined as follows.

[0050] - BM: beam management

[0051] - CQI: Channel Quality Indicator

[0052] - CRI: Channel state information - reference signal resource indicator

[0053] - CSI: Channel State Information

[0054] - CSI-IM: Channel State Information - Interference Measurement

[0055] - CSI-RS: Channel state information - reference signal

[0056] - DMRS: Demodulation Reference Signal

[0057] - FDM: frequency division multiplexing

[0058] - FFT: fast Fourier transform

[0059] - IFDMA: interleaved frequency division multiple access

[0060] - IFFT: inverse fast Fourier transform

[0061] - L1-RSRP: Layer 1 reference signal received power

[0062] - L1-RSRQ: Layer 1 reference signal received quality

[0063] - MAC: Medium Access Control

[0064] - NZP: non-zero power

[0065] - OFDM: orthogonal frequency division multiplexing

[0066] - PDCCH: Physical downlink control channel

[0067] - PDSCH: Physical downlink shared channel

[0068] - PMI: precoding matrix indicator

[0069] - RE: resource element

[0070] - RI: Rank indicator

[0071] - RRC: Radio Resource Control

[0072] - RSSI: Received signal strength indicator

[0073] - Rx: Reception

[0074] - QCL: quasi co-location

[0075] - SINR: signal to interference and noise ratio

[0076] - SSB (or SS / PBCH block): Synchronization signal block (including primary synchronization signal (PSS), secondary synchronization signal (SSS), and physical broadcast channel (PBCH))

[0077] - TDM: Time Division Multiplexing

[0078] - TRP: transmission and reception point

[0079] - TRS: Tracking Reference Signal

[0080] - Tx: transmission

[0081] - UE: user equipment

[0082] - ZP: Zero Power

[0083] System General

[0084] As more and more communication devices demand greater communication capacity, the need for improved mobile broadband communications compared to existing radio access technologies (RATs) is emerging. Furthermore, massive Machine Type Communications (MTC), which connects numerous devices and objects to provide diverse services anytime, anywhere, is also a key issue to be considered in next-generation communications. Furthermore, communication system design that considers reliability and latency-sensitive services / terminals is being discussed. Accordingly, the introduction of next-generation RATs that consider enhanced mobile broadband communication (eMBB), massive MTC (MMTC), and Ultra-Reliable and Low Latency Communication (URLLC) is being discussed. For convenience, these technologies are referred to as NR in this disclosure. NR is an expression representing an example of 5G RAT.

[0085] A new RAT system, including NR, uses OFDM or a similar transmission scheme. The new RAT system may follow OFDM parameters different from those of LTE. Alternatively, the new RAT system may follow the existing LTE / LTE-A numerology but support a larger system bandwidth (e.g., 100 MHz). Alternatively, a single cell may support multiple numerologies. That is, terminals operating under different numerologies can coexist within a single cell.

[0086] A numerology corresponds to a single subcarrier spacing in the frequency domain. Different numerologies can be defined by scaling the reference subcarrier spacing by an integer N.

[0087] Figure 1 illustrates the structure of a wireless communication system to which the present disclosure can be applied.

[0088] Referring to Fig. 1, the NG-RAN consists of gNBs that provide 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 control plane (RRC) protocol termination for UE. The gNBs are interconnected via Xn interfaces. The gNBs are also connected to the NGC (New Generation Core) via the NG interface. More specifically, the gNBs are connected to the AMF (Access and Mobility Management Function) via the N2 interface and to the UPF (User Plane Function) via the N3 interface.

[0089] FIG. 2 illustrates a frame structure in a wireless communication system to which the present disclosure can be applied.

[0090] NR systems can support multiple numerologies. Numerologies can be defined by subcarrier spacing and cyclic prefix (CP) overhead. Multiple subcarrier spacings can be derived by scaling the base (reference) subcarrier spacing by an integer N (or μ). Furthermore, even if it is assumed that very low subcarrier spacing is not used at very high carrier frequencies, the numerology used can be selected independently of the frequency band. Furthermore, NR systems can support various frame structures corresponding to multiple numerologies.

[0091] Below, we examine OFDM numerologies and frame structures that can be considered in NR systems. The various OFDM numerologies supported in NR systems can be defined as shown in Table 1 below.

[0092] μΔf=2 μ ·15 [kHz]CP015 Normal 130 Normal 260 Normal, Extended 3120 Normal 4240 Normal

[0093] NR supports multiple numerologies (or subcarrier spacings (SCS)) to support various 5G services. For example, an SCS of 15 kHz supports wide areas in traditional cellular bands; an SCS of 30 kHz / 60 kHz supports dense urban areas, lower latency, and wider carrier bandwidth; and an SCS of 60 kHz or higher supports bandwidths greater than 24.25 GHz to overcome phase noise.

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

[0095] Frequency Range Designation Corresponding Frequency Range Subcarrier Spacing FR1410MHz - 7125MHz 15, 30, 60kHz FR224250MHz - 52600MHz 60, 120, 240kHz

[0096] Regarding the frame structure in the NR system, the sizes of the various fields in the time domain are T c =1 / (Δf max ·N f ) is expressed as a multiple of the time unit. Here, Δf max =480·10 3 Hz and N f =4096. Downlink and uplink transmissions are T f =1 / (Δf max N f / 100)·T c = It is organized into radio frames with a duration of 10ms. Here, each radio frame is T sf =(Δf max N f / 1000)·T c =1ms It consists of 10 subframes with an interval of . In this case, there may be one set of frames for uplink and one set of frames for downlink. In addition, transmission in uplink frame number i from a terminal is T earlier than the start of the corresponding downlink frame from the terminal.TA =(N TA +N TA,offset )T c It should start before. For the subcarrier spacing configuration μ, slots are n within a subframe. s μ ∈{0,..., N slot subframe,μ-1} are numbered in increasing order, and n within a radio frame. s,f μ ∈{0,..., N slot frame,μ -1} are numbered in increasing order. One slot is N symb slot It consists of consecutive OFDM symbols, and N symb slot is determined by CP. Slot n in subframe s μ The start of OFDM symbol n in the same subframe s μ N symb slot are aligned temporally with the start of the OFDM signal. Not all terminals can transmit and receive simultaneously, which means that not all OFDM symbols in a downlink slot or uplink slot can be utilized.

[0097] Table 3 shows the number of OFDM symbols per slot in a general CP (N symb slot ), the number of slots per wireless frame (N slot frame,μ ), number of slots per subframe (N slot subframe,μ), and Table 4 shows the number of OFDM symbols per slot in the extended CP, the number of slots per radio frame, and the number of slots per subframe.

[0098] μN symb slot N slot frame,μ N slotsubframe,μ01410111420221440431480841416016

[0099] μN symb slot N slot frame,μ N slot subframe,μ212404

[0100] Fig. 2 is an example when μ=2 (SCS is 60 kHz), and referring to Table 3, 1 subframe can include 4 slots. 1 subframe={1,2,4} slot shown in Fig. 2 is an example, and the number of slot(s) that can be included in 1 subframe is defined as in Table 3 or Table 4. In addition, a mini-slot can include 2, 4, or 7 symbols, or more or fewer symbols.

[0101] Regarding physical resources in an NR system, antenna ports, resource grids, resource elements, resource blocks, carrier parts, etc. can be considered. Below, the physical resources that can be considered in an NR system will be examined in detail.

[0102] First, with respect to antenna ports, antenna ports are defined such that the channel through which a symbol on an antenna port is carried can be inferred from the channel through which another symbol on the same antenna port is carried. Two antenna ports are said to be in a QC / QCL (quasi co-located or quasi co-location) relationship if the large-scale properties of the channel through which a symbol on one antenna port is carried can be inferred from the channel through which a symbol on another antenna port is carried. Here, the large-scale properties include one or more of delay spread, Doppler spread, frequency shift, average received power, and received timing.

[0103] FIG. 3 illustrates a resource grid in a wireless communication system to which the present disclosure can be applied.

[0104] Referring to Figure 3, the resource grid is N in the frequency domain. RB μ N sc RB It consists of subcarriers, and one subframe is 14·2 μ It is described as an example, but not limited to, that it consists of OFDM symbols. In an NR system, the transmitted signal is N RB μ N sc RB One or more resource grids consisting of subcarriers and 2 μ N symb (μ) is described by OFDM symbols. Here, N RB μ≤ N RB max,μ is. The above N RB max,μrepresents the maximum transmission bandwidth, which may vary between uplink and downlink as well as between numerologies. In this case, one resource grid may be configured for μ and each antenna port p. Each element of the resource grid for μ and each antenna port p is referred to as a resource element and is uniquely identified by an index pair (k, l'). Here, k=0,...,N RB μ N sc RB -1 is the index in the frequency domain, and l'=0,...,2 μ N symb (μ) -1 indicates the position of the symbol within the subframe. When referring to a resource element in a slot, an index pair (k,l) is used. Here, l=0,...,N symb μ -1. The resource element (k,l') for μ and antenna port p is a complex value a k,l' (p,μ) . If there is no risk of confusion or if a particular antenna port or numerology is not specified, the indices p and μ can be dropped, resulting in a complex value of a k,l' (p) or a k,l' This can be. Also, a resource block (RB) is N in the frequency domain. sc RB =12 is defined as consecutive subcarriers.

[0105] Point A serves as a common reference point of the resource block grid and is obtained as follows.

[0106] - offsetToPointA for the Primary Cell (PCell) downlink represents the frequency offset between point A and the lowest subcarrier of the lowest resource block overlapping the SS / PBCH block used by the UE for initial cell selection. It is expressed in resource block units assuming 15 kHz subcarrier spacing for FR1 and 60 kHz subcarrier spacing for FR2.

[0107] - absoluteFrequencyPointA represents the frequency-position of point A expressed as ARFCN (absolute radio-frequency channel number).

[0108] Common resource blocks (CRBs) are numbered from 0 upwards in the frequency domain for a subcarrier spacing setting μ. The center of subcarrier 0 of CRB 0 for a subcarrier spacing setting μ coincides with 'point A'. CRB number n in the frequency domain CRB μ The relationship between the resource elements (k,l) and the subcarrier spacing setting μ is given by the following mathematical expression 1.

[0109]

[0110] In Equation 1, k is defined relative to point A such that k = 0 corresponds to the subcarrier centered at point A. Physical resource blocks are numbered from 0 to N within the bandwidth part (BWP). BWP,i size,μ - Numbered from -1, where i is the number of the BWP. Physical resource block n in BWP i PRB and common resource block n CRB The relationship between them is given by the mathematical formula 2 below.

[0111]

[0112] N BWP,i start,μ is a common resource block where BWP starts relative to common resource block 0.

[0113] FIG. 4 illustrates a physical resource block in a wireless communication system to which the present disclosure can be applied. FIG. 5 illustrates a slot structure in a wireless communication system to which the present disclosure can be applied.

[0114] Referring to FIGS. 4 and 5, a slot includes multiple symbols in the time domain. For example, in the case of a normal CP, one slot includes seven symbols, but in the case of an extended CP, one slot includes six symbols.

[0115] 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 Part) is defined as multiple consecutive (physical) resource blocks in the frequency domain, and can correspond to a single numerology (e.g., SCS, CP length, etc.). A carrier can comprise up to N (e.g., 5) BWPs. Data communication is performed through activated BWPs, and only one BWP can be activated for a single terminal. Each element in the resource grid is referred to as a Resource Element (RE), to which one complex symbol can be mapped.

[0116] The NR system can support up to 400 MHz per component carrier (CC). If a terminal operating in such a wideband CC always operates with the radio frequency (RF) chip for the entire CC turned on, the terminal battery consumption may increase. Alternatively, when considering multiple use cases operating within a single wideband CC (e.g., eMBB, URLLC, Mmtc, V2X, etc.), different numerologies (e.g., subcarrier spacing, etc.) may be supported for each frequency band within the CC. Alternatively, each terminal may have different maximum bandwidth capabilities. Considering this, the base station can instruct the terminal to operate only on a portion of the bandwidth rather than the entire bandwidth of the wideband CC, and this portion of bandwidth is conveniently defined as the bandwidth part (BWP). A BWP can be composed of consecutive RBs on the frequency axis and can correspond to a single numerology (e.g., subcarrier spacing, CP length, slot / mini-slot interval).

[0117] Meanwhile, the base station can configure multiple BWPs even within a single CC configured for a terminal. For example, in the PDCCH monitoring slot, a BWP occupying a relatively small frequency range can be configured, and the PDSCH indicated by the PDCCH can be scheduled on a larger BWP. Alternatively, if UEs are concentrated on a specific BWP, some terminals can be configured to a different BWP for load balancing. Alternatively, considering frequency domain inter-cell interference cancellation between neighboring cells, a portion of the spectrum in the middle of the entire bandwidth can be excluded and both BWPs can be configured within the same slot. In other words, the base station can configure at least one DL / UL BWP for a terminal associated with a wideband CC. The base station can activate at least one DL / UL BWP(s) among the configured DL / UL BWP(s) at a specific time (via L1 signaling, MAC CE (Control Element), RRC signaling, etc.). Additionally, the base station can instruct switching to another configured DL / UL BWP (e.g., via L1 signaling or MAC CE or RRC signaling). Alternatively, switching to a configured DL / UL BWP can be performed based on a timer when the timer value expires. In this case, the activated DL / UL BWP is defined as the active DL / UL BWP. However, in situations such as when the terminal is performing the initial access process or before the RRC connection is set up, the configuration for the DL / UL BWP may not be received. Therefore, in these situations, the DL / UL BWP assumed by the terminal is defined as the initially active DL / UL BWP.

[0118] FIG. 6 illustrates physical channels used in a wireless communication system to which the present disclosure can be applied and a general signal transmission and reception method using the same.

[0119] In wireless communication systems, terminals receive information from a base station via the downlink and transmit it to the base station via the uplink. The information transmitted and received between the base station and terminals includes data and various control information, and various physical channels exist depending on the type and purpose of the information being transmitted and received.

[0120] When a terminal is powered on or enters a new cell, it performs an initial cell search operation, such as synchronizing with the base station (S601). To this end, the terminal receives a primary synchronization signal (PSS) and a secondary synchronization signal (SSS) from the base station to synchronize with the base station and obtain information such as a cell identifier (ID). Afterwards, the terminal can receive a physical broadcast channel (PBCH) from the base station to obtain broadcast information within the cell. Meanwhile, the terminal can receive a downlink reference signal (DL RS) during the initial cell search phase to check the downlink channel status.

[0121] A terminal that has completed initial cell search can obtain more specific system information by receiving a physical downlink control channel (PDCCH) and a physical downlink shared channel (PDSCH) according to information carried on the PDCCH (S602).

[0122] Meanwhile, when accessing a base station for the first time or when there are no radio resources for signal transmission, the terminal may perform a random access procedure (RACH) for the base station (steps S603 to S606). To this end, the terminal may transmit a specific sequence as a preamble via the Physical Random Access Channel (PRACH) (steps S603 and S605) and receive a response message to the preamble via the Physical Data Channel Control Channel (PDCCH) and the corresponding PDSCH (steps S604 and S606). In the case of a contention-based RACH, a contention resolution procedure (Contention Resolution Procedure) may additionally be performed.

[0123] The terminal that has performed the procedure described above can then perform PDCCH / PDSCH reception (S607) and physical uplink shared channel (PUSCH) / physical uplink control channel (PUCCH) transmission (S608) as general uplink / downlink signal transmission procedures. In particular, the terminal receives downlink control information (DCI) through the PDCCH. Here, DCI includes control information such as resource allocation information for the terminal, and its format varies depending on the purpose of use.

[0124] Meanwhile, the control information that the terminal transmits to the base station via the uplink or that the terminal receives from the base station includes downlink / uplink ACK / NACK (Acknowledgement / Non-Acknowledgement) signals, CQI (Channel Quality Indicator), PMI (Precoding Matrix Indicator), RI (Rank Indicator), etc. In the case of the 3GPP LTE system, the terminal can transmit the above-described control information such as CQI / PMI / RI via PUSCH and / or PUCCH.

[0125] Table 5 shows an example of the DCI format in the NR system.

[0126] DCI Format Utilization 0_0 Scheduling of PUSCH within a cell 0_1 Scheduling of one or multiple PUSCH within a cell, or indicating cell group (CG: cell group) downlink feedback information to the UE 0_2 Scheduling of PUSCH within a cell 1_0 Scheduling of PDSCH within a DL cell 1_1 Scheduling of PDSCH within a cell 1_2 Scheduling of PDSCH within a cell

[0127] Referring to Table 5, DCI formats 0_0, 0_1, and 0_2 may include resource information related to scheduling of PUSCH (e.g., UL / SUL (Supplementary UL), frequency resource allocation, time resource allocation, frequency hopping, etc.), transport block (TB) related information (e.g., MCS (Modulation Coding and Scheme), NDI (New Data Indicator), RV (Redundancy Version), etc.), HARQ (Hybrid - Automatic Repeat and request) related information (e.g., process number, DAI (Downlink Assignment Index), PDSCH-HARQ feedback timing, etc.), multi-antenna related information (e.g., DMRS sequence initialization information, antenna port, CSI request, etc.), power control information (e.g., PUSCH power control, etc.), and the control information included in each DCI format may be predefined.

[0128] DCI format 0_0 is used for scheduling PUSCH in a cell. The information contained in DCI format 0_0 is transmitted after being scrambled with a CRC (cyclic redundancy check) by a C-RNTI (Cell RNTI: Cell Radio Network Temporary Identifier), a CS-RNTI (Configured Scheduling RNTI), or a MCS-C-RNTI (Modulation Coding Scheme Cell RNTI).

[0129] DCI format 0_1 ​​is used to indicate scheduling of one or more PUSCHs in a single cell, or configure grant (CG: configure grant) downlink feedback information to the UE. The information contained in DCI format 0_1 ​​is CRC-scrambled and transmitted using the C-RNTI, CS-RNTI, SP-CSI-RNTI (Semi-Persistent CSI RNTI), or MCS-C-RNTI.

[0130] DCI format 0_2 is used for scheduling PUSCH in a cell. The information contained in DCI format 0_2 is CRC-scrambled and transmitted using C-RNTI, CS-RNTI, SP-CSI-RNTI, or MCS-C-RNTI.

[0131] Next, DCI formats 1_0, 1_1, and 1_2 may include resource information related to scheduling of PDSCH (e.g., frequency resource allocation, time resource allocation, virtual resource block (VRB)-physical resource block (PRB) mapping, etc.), transport block (TB) related information (e.g., MCS, NDI, RV, etc.), HARQ related information (e.g., process number, DAI, PDSCH-HARQ feedback timing, etc.), multi-antenna related information (e.g., antenna port, transmission configuration indicator (TCI), sounding reference signal (SRS) request, etc.), PUCCH related information (e.g., PUCCH power control, PUCCH resource indicator, etc.), and control information included in each DCI format may be predefined.

[0132] DCI format 1_0 is used for scheduling PDSCH in a DL cell. The information contained in DCI format 1_0 is CRC-scrambled and transmitted using C-RNTI, CS-RNTI, or MCS-C-RNTI.

[0133] DCI format 1_1 is used for scheduling PDSCH in a single cell. The information contained in DCI format 1_1 is CRC-scrambled and transmitted using C-RNTI, CS-RNTI, or MCS-C-RNTI.

[0134] DCI format 1_2 is used for scheduling PDSCH in a single cell. The information contained in DCI format 1_2 is CRC-scrambled and transmitted using C-RNTI, CS-RNTI, or MCS-C-RNTI.

[0135] Artificial Intelligence (AI) Operation

[0136] Advances in artificial intelligence / machine learning (AI / ML) technology are leading to the intelligence / advanced development of nodes and terminals that make up wireless communication networks. In particular, the intelligence of networks / base stations will enable rapid optimization and derivation / application of various network / base station decision parameter values ​​(e.g., transmit / receive power of each base station, transmit power of each terminal, precoders / beams of base stations / terminals, time / frequency resource allocation for each terminal, duplex mode of each base station, etc.) based on various environmental parameters (e.g., distribution / location of base stations, distribution / location / material of buildings / furniture, etc., location / movement direction / speed of terminals, climate information, etc.). In line with this trend, many standardization organizations (e.g., 3GPP, O-RAN) are considering its introduction, and active studies are also underway on this topic.

[0137] The AI-related descriptions and operations described below can be applied in combination 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.

[0138] Figure 7 illustrates the classification of artificial intelligence.

[0139] Referring to Figure 7, artificial intelligence (AI) corresponds to all automation in which machines can replace tasks that humans should do.

[0140] Machine learning (ML) refers to a technology that allows machines to learn patterns for decision-making from data without explicitly programming rules.

[0141] Deep learning is an artificial neural network-based model that allows machines to simultaneously extract features and make judgments from unstructured data. The algorithm relies on multilayer networks of interconnected nodes, inspired by the biological nervous system, or neural networks, for feature extraction and transformation. Common deep learning network architectures include deep neural networks (DNNs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs).

[0142] AI (or AI / ML) can be narrowly defined as deep learning-based artificial intelligence, but the present disclosure is not limited thereto. That is, AI (or AI / ML) in the present disclosure can collectively refer to automation technologies applied to intelligent machines (e.g., UEs, RANs, network nodes, etc.) capable of performing tasks like humans.

[0143] AI (or AI / ML) can be classified according to various criteria as follows:

[0144] 1. Offline / Online Learning

[0145] a) Offline Learning

[0146] Offline learning follows a sequential process of database collection, training, and prediction. In other words, collection and training are performed offline, and the completed program can be installed on-site for prediction purposes. In offline learning, the system does not learn incrementally; learning is performed using all available collected data and applied to the system without further training. If training on new data is required, training can be restarted using the entire new data set.

[0147] b) Online Learning

[0148] This refers to a method that utilizes the continuous availability of data available for learning via the Internet, gradually improving performance through additional learning using real-time data. Learning is performed in real time on specific data (sets) collected online, enabling the system to rapidly adapt to changing data.

[0149] To build an AI system, only online learning may be used, so that learning is performed using only real-time data, or offline learning may be performed using a predetermined data set, and then additional learning may be performed using additional real-time data (online + offline learning).

[0150] 2. Classification by AI / ML Framework Concept

[0151] a) Centralized Learning

[0152] In centralized learning, training data collected from multiple different nodes are reported to a centralized node, and all data resources / storage / learning (e.g., supervised learning, unsupervised learning, reinforcement learning, etc.) are performed in a single centralized node.

[0153] b) Federated Learning

[0154] Federated learning builds a collective model based on data across distributed data owners. Instead of collecting data as a model, AI / ML models are imported into the data source, allowing local nodes / individual devices to collect data and train their own copies of the model, 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 offers advantages in terms of increased computational speed and information security. This eliminates the need to upload personal data to a central server, preventing personal information leaks and misuse.

[0155] c) Distributed Learning

[0156] Distributed learning refers to the concept of machine learning processes being scaled and distributed across a cluster of nodes. Training models are split and shared across multiple nodes, operating simultaneously to accelerate model training.

[0157] 3. Classification by learning method

[0158] a) Supervised Learning

[0159] Supervised learning is a machine learning task that aims to learn a mapping function from input to output, given a labeled data set. The input data is called training data and has known labels or outcomes. Examples of supervised learning include:

[0160] - Regression: Linear Regression, Logistic Regression

[0161] - Instance-based algorithms: k-Nearest Neighbor (KNN)

[0162] - Decision Tree Algorithms: Classification and Regression Tree (CART)

[0163] - Support Vector Machines (SVM)

[0164] - Bayesian Algorithms: Naive Bayes

[0165] - Ensemble Algorithms: Extreme Gradient Boosting, Bagging: Random Forest

[0166] Supervised learning can be further grouped into regression and classification problems, where classification is about predicting labels and regression is about predicting quantities.

[0167] b) Unsupervised Learning

[0168] Unsupervised learning is a machine learning task that aims to learn features that describe hidden structures in unlabeled data. The input data is unlabeled and there is no known outcome. Some examples of unsupervised learning include K-means clustering, principal component analysis (PCA), nonlinear independent component analysis (ICA), and long short-term memory (LSTM).

[0169] c) Reinforcement Learning (RL)

[0170] In reinforcement learning (RL), an agent interacts with its environment through a trial-and-error process, aiming to optimize a long-term goal. This is goal-directed learning based on interaction with the environment. Examples of RL algorithms include:

[0171] - Q-learning

[0172] - Multi-armed bandit learning

[0173] - Deep Q Network

[0174] - State-Action-Reward-State-Action (SARSA)

[0175] - Temporal Difference Learning

[0176] Actor-critic reinforcement learning

[0177] - Deep deterministic policy gradient (DDPG)

[0178] - Monte-Carlo tree search

[0179] Additionally, reinforcement learning can be grouped into model-based reinforcement learning and model-free reinforcement learning.

[0180] Model-based reinforcement learning: This refers to RL algorithms that use predictive models. Using models of the environment's various dynamic states and how these states lead to rewards, the probabilities of transitions between states are obtained.

[0181] Model-free reinforcement learning: This refers to RL algorithms based on values ​​or policies that maximize future rewards. In multi-agent environments / states, it is computationally less complex and does not require an accurate representation of the environment.

[0182] Additionally, RL algorithms can also be classified into value-based RL vs. policy-based RL, policy-based RL vs. off-policy RL, etc.

[0183] Below, we provide examples of representative deep learning models.

[0184] Figure 8 illustrates a feed-forward neural network.

[0185] A feed-forward neural network (FFNN) consists of an input layer, a hidden layer, and an output layer.

[0186] In FFNN, information is only transmitted from the input layer to the output layer, passing through the hidden layer if present.

[0187] Figure 9 illustrates a recurrent neural network.

[0188] A recurrent neural network (RNN) is a type of artificial neural network in which hidden nodes are connected by directed edges, forming a directed cycle. It is a model suitable for processing sequential data, such as speech and text.

[0189] In Fig. 9, A is a neural network, x t is the input value, h t represents the output value. Here, h t can mean a status value that represents the present based on time, and h t-1 can represent the previous state value.

[0190] One type of RNN is Long Short-Term Memory (LSTM), which adds a cell state to the hidden state of an RNN. LSTM adds input gates, forget gates, and output gates to RNN cells (memory cells in the hidden layer), allowing it to erase unnecessary memories. Compared to RNNs, LSTM adds a cell state.

[0191] Figure 10 illustrates a convolutional neural network.

[0192] Convolutional neural networks (CNNs) are used for two purposes: reducing model complexity and extracting good features by applying convolution operations commonly used in the fields of image processing and video processing.

[0193] - Kernel or filter: refers to a unit / structure that applies weights to inputs within a specific range / unit. The kernel (or filter) can be modified through learning.

[0194] - Stride: This refers to the range of movement of the kernel within the input.

[0195] - Feature map: This refers to the result of applying a kernel to the input. Multiple feature maps can be extracted to induce robustness against distortion and changes.

[0196] - Padding: This refers to the value added to adjust the size of the feature map.

[0197] - Pooling: refers to an operation to reduce the size of the feature map by downsampling the feature map (e.g., max pooling, average pooling).

[0198] Figure 11 illustrates an auto encoder.

[0199] Auto encoder 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, ...)'.

[0200] An autoencoder has input and output nodes with the same characteristics. Because it reconstructs the input, the output can be referred to as reconstruction. Furthermore, an autoencoder is a type of unsupervised learning.

[0201] The loss function of the auto encoder exemplified in Fig. 11 is calculated based on the difference between the input and the output, and based on this, the degree of loss of the input is identified, and the auto encoder performs an optimization process to minimize the loss.

[0202] Below, to explain AI (or AI / ML) more specifically, the terms can be defined as follows.

[0203] - Data collection: Data collected from network nodes, management entities, or UEs as a basis for AI model training, data analysis, and inference.

[0204] - AI Model: A data-driven algorithm that applies AI technology to generate a set of outputs containing predictive information and / or decision parameters based on a set of inputs.

[0205] - AI / ML Training: An online or offline process of training an AI model by learning features and patterns that best represent the data and obtain a trained AI / ML model for inference.

[0206] - AI / ML Inference: The process of making predictions or inducing decisions based on collected data and the AI ​​model using a trained AI model.

[0207] Figure 12 illustrates a functional framework for AI operation.

[0208] Referring to FIG. 12, the Data Collection function (10) is a function that collects input data and provides processed input data to the Model Training function (20) and the Model Inference function (30).

[0209] Examples of input data may include measurements from UEs or other network entities, feedback from actors, and output from AI models.

[0210] The Data Collection function (10) performs data preparation based on input data and provides input data processed through data preparation. Here, the Data Collection function (10) does not perform data preparation specific to each AI algorithm (e.g., data pre-processing and cleaning, formatting, and transformation), but can perform data preparation common to AI algorithms.

[0211] After the data preparation process is performed, the Model Training function (10) provides training data (11) to the Model Training function (20) and provides inference data (Inference Data) (12) to the Model Inference function (30). Here, the Training Data (11) is data required as input for the AI ​​Model Training function (20). The Inference Data (12) is data required as input for the AI ​​Model Inference function (30).

[0212] The Data Collection function (10) may be performed by a single entity (e.g., UE, RAN node, network node, etc.) or may be performed by multiple entities. In this case, Training Data (11) and Inference Data (12) may be provided to the Model Training function (20) and Model Inference function (30), respectively, from multiple entities.

[0213] The Model Training function (20) is a function that performs AI model training, validation, and testing, which can generate model performance metrics as part of the AI ​​model testing process. If necessary, the Model Training function (20) also handles data preparation (e.g., data pre-processing and cleaning, forming, and transformation) based on the Training Data (11) provided by the Data Collection function (10).

[0214] Here, Model Deployment / Update (13) is used to initially deploy the trained, verified, and tested AI model to the Model Inference function (30) or to provide the updated model to the Model Inference function (30).

[0215] The Model Inference function (30) is a function that provides AI model inference output (16) (e.g., prediction or decision). If applicable, the Model Inference function (30) may provide model performance feedback (14) to the Model Training function (20). In addition, the Model Inference function (30) is also responsible for data preparation (e.g., data pre-processing and cleaning, forming, and transformation) based on the Inference Data (12) provided by the Data Collection function (10), if necessary.

[0216] Here, Output (16) refers to the inference output of the AI ​​model generated by the Model Inference function (30), and the details of the inference output may vary depending on the use case.

[0217] Model Performance Feedback (14) can be used to monitor the performance of the AI ​​model if available, and this feedback may be omitted.

[0218] The actor function (40) is a function that receives the output (16) from the model inference function (30) and triggers or performs a corresponding task / action. The actor function (40) can trigger tasks / actions for other entities (e.g., one or more UEs, one or more RAN nodes, one or more network nodes, etc.) or for itself.

[0219] Feedback (15) can be used to derive training data (11), inference data (12), or to monitor the performance of the AI ​​model, its impact on the network, etc.

[0220] Meanwhile, the definitions of training / validation / test in the data set used in AI / ML can be distinguished as follows.

[0221] - Training data: This refers to the data set for learning the model.

[0222] - Validation data: This refers to a data set used to validate a model that has already completed training. In other words, it refers to a data set typically used to prevent overfitting of the training data set.

[0223] It also refers to a data set for selecting the best model among the various models learned during the learning process. Therefore, it can be viewed as a type of learning.

[0224] - Test data: This refers to the data set for final evaluation. This data is unrelated to learning.

[0225] In the case of the above data set, if the training set is generally divided, the training data and validation data can be divided and used in a ratio of 8:2 or 7:3 within the entire training set, and if the test is included, it can be divided and used in a ratio of 6:2:2 (training: validation: test).

[0226] Depending on the capability of the AI / ML function between the base station and the terminal, the level of cooperation can be defined as follows, and variations due to combination of multiple levels or separation of any one level are also possible.

[0227] Cat 0a) No collaboration framework: AI / ML algorithms are purely implementation-based and do not require any changes to the wireless interface.

[0228] Cat 0b) This level corresponds to a framework with a modified wireless interface tailored to efficient implementation-based AI / ML algorithms, but without collaboration.

[0229] Category 1) involves inter-node support to improve the AI / ML algorithms of each node. This applies when the UE receives support from the gNB (for training, adaptation, etc.) and vice versa. At this level, model exchange between network nodes is not required.

[0230] Category 2) Joint ML tasks can be performed between the UE and gNB. This level requires the exchange of AI / ML model commands or network nodes.

[0231] The functions exemplified in FIG. 12 above may be implemented in a RAN node (e.g., a base station, a TRP, a central unit (CU) of a base station, etc.), a network node, an operation administration maintenance (OAM) of a network operator, or a UE.

[0232] Alternatively, two or more entities, such as a RAN, a network node, a network operator's OAM, or a UE, may cooperate to implement the functions illustrated in FIG. 12. For example, one entity may perform some of the functions of FIG. 12, and another entity may perform the remaining functions. In this way, since some of the functions illustrated in FIG. 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 may be omitted. For example, if the Model Training function (20) and the Model Inference function (30) are performed by the same entity, the transmission / provision of Model Deployment / Update (13) and Model Performance Feedback (14) may be omitted.

[0233] Alternatively, any one of the functions illustrated in FIG. 12 may be performed collaboratively by two or more entities, including a RAN, a network node, a network operator's OAM, or a UE. This may be referred to as a split AI operation.

[0234] Figure 13 is a diagram illustrating segmentation AI inference.

[0235] Figure 13 illustrates a case where, among split AI operations, the Model Inference function is performed collaboratively by an end device such as a UE and a network AI / ML endpoint.

[0236] In addition to the Model Inference function, the Model Training function, Actor, and Data Collection functions can each be split into multiple parts depending on the current task and environment, and performed by multiple entities collaborating.

[0237] For example, computationally intensive and energy-intensive parts may be performed at the network endpoint, while privacy-sensitive and latency-sensitive parts may be performed at the end device. In this case, the end device may execute the task / model from input data up to a specific part / layer, and then transmit the intermediated data to the network endpoint. The network endpoint then executes the remaining parts / layers and provides the inference outputs to one or more devices that perform the actions / tasks.

[0238] Figure 14 illustrates the application of a functional framework in a wireless communication system.

[0239] Figure 14 illustrates a 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.).

[0240] Step 1: RAN node 1 and RAN node 2 transmit input data (i.e., training data) for AI model training to the network node. Here, RAN node 1 and RAN node 2 can also transmit data collected from the UE (e.g., UE measurements related to RSRP, RSRQ, SINR of the serving cell and neighboring cells, UE location, speed, etc.) to the network node.

[0241] Step 2: Network nodes train the AI ​​model using the received training data.

[0242] Step 3: The network node distributes / updates the AI ​​Model to RAN Node 1 and / or RAN Node 2. RAN Node 1 (and / or RAN Node 2) may continue model training based on the received AI Model.

[0243] For convenience of explanation, we assume that the AI ​​Model is deployed / updated only to RAN node 1.

[0244] Step 4: RAN node 1 receives input data for AI Model Inference (i.e., Inference data) from UE and RAN node 2.

[0245] Step 5: RAN node 1 performs AI Model Inference using the received Inference data to generate output data (e.g., prediction or decision).

[0246] Step 6: If applicable, RAN node 1 may transmit model performance feedback to the network nodes.

[0247] Step 7: RAN node 1, RAN node 2, and the UE (or 'RAN node 1 and the UE', or 'RAN node 1 and the RAN node 2') perform actions based on the output data. For example, in the case of a load balancing operation, the UE may move from RAN node 1 to RAN node 2.

[0248] Step 8: RAN node 1 and RAN node 2 transmit feedback information to the network nodes.

[0249] Figure 15 illustrates the application of a functional framework in a wireless communication system.

[0250] Figure 15 illustrates a case where both the AI ​​Model Training function and the AI ​​Model Inference function are performed by RAN nodes (e.g., base stations, TRPs, CUs of base stations, etc.).

[0251] Step 1: UE and RAN node 2 transmit input data (i.e., training data) for AI model training to RAN node 1.

[0252] Step 2: RAN node 1 trains the AI ​​model using the received training data.

[0253] Step 3: RAN node 1 receives input data for AI Model Inference (i.e., Inference data) from the UE and RAN node 2.

[0254] Step 4: RAN node 1 performs AI Model Inference using the received Inference data to generate output data (e.g., prediction or decision).

[0255] Step 5: RAN node 1, RAN node 2, and the UE (or 'RAN node 1 and the UE', or 'RAN node 1 and the RAN node 2') perform actions based on the output data. For example, in the case of a load balancing operation, the UE may move from RAN node 1 to RAN node 2.

[0256] Step 6: RAN node 2 sends feedback information to RAN node 1.

[0257] Figure 16 illustrates the application of a functional framework in a wireless communication system.

[0258] Figure 16 illustrates a case where the AI ​​Model Training function is performed by a RAN node (e.g., a base station, a TRP, a CU of the base station, etc.) and the AI ​​Model Inference function is performed by a UE.

[0259] Step 1: The UE transmits input data (i.e., training data) for AI model training to the RAN node. Here, the RAN node can collect data (e.g., UE measurements related to RSRP, RSRQ, SINR of the serving cell and neighboring cells, UE location, speed, etc.) from various UEs and / or from other RAN nodes.

[0260] Step 2: The RAN node trains the AI ​​model using the received training data.

[0261] Step 3: The RAN node distributes / updates the AI ​​model to the UE. The UE may also continue model training based on the received AI model.

[0262] Step 4: Receive input data (i.e., Inference data) for AI Model Inference from the UE and RAN nodes (and / or from other UEs).

[0263] Step 5: The UE performs AI Model Inference using the received Inference data to generate output data (e.g., prediction or decision).

[0264] Step 6: If applicable, the UE may send model performance feedback to the RAN node.

[0265] Step 7: The UE and RAN nodes perform actions based on the output data.

[0266] Step 8: The UE transmits feedback information to the RAN node.

[0267] Actions related to channel state information (CSI)

[0268] In NR (New Radio) systems, CSI-RS (channel state information-reference signal) is used for time / frequency tracking, CSI computation, L1 (layer 1)-RSRP (reference signal received power) computation, and mobility. Here, CSI computation is related to CSI acquisition, and L1-RSRP computation is related to beam management (BM).

[0269] CSI (channel state information) is a general term for information that can indicate the quality of the wireless channel (or link) formed between the terminal and the antenna port.

[0270] - In order to perform one of the purposes of the CSI-RS as described above, a terminal (e.g., user equipment, UE) receives configuration information related to CSI from a base station (e.g., general Node B, gNB) through RRC (radio resource control) signaling.

[0271] The configuration information related to the above CSI may include at least one of CSI-IM (interference management) resource related information, CSI measurement configuration related information, CSI resource configuration related information, CSI-RS resource related information, or CSI report configuration related information.

[0272] i) CSI-IM resource-related information may 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.

[0273] ii) CSI resource configuration related information can be expressed as CSI-ResourceConfig IE. The CSI resource configuration related information defines a group including at least one of a non-zero power (NZP) CSI-RS resource set, a CSI-IM resource set, or a CSI-SSB resource set. That is, the CSI resource configuration related information includes a CSI-RS resource set list, and the CSI-RS resource set list can include at least one of an 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.

[0274] Parameters indicating the purpose of CSI-RS (e.g., BM-related 'repetition' parameter, tracking-related 'trs-Info' parameter) can be set for each NZP CSI-RS resource set.

[0275] iii) Information related to the CSI report configuration includes a report configuration type parameter (reportConfigType) indicating time domain behavior and a report quantity parameter (reportQuantity) indicating the CSI-related quantity to be reported. The time domain behavior may be periodic, aperiodic, or semi-persistent.

[0276] - The terminal measures CSI based on configuration information related to the above CSI.

[0277] The above CSI measurement may include (1) a process of receiving a CSI-RS of a terminal, and (2) a process of calculating CSI using the received CSI-RS, which will be described in detail later.

[0278] CSI-RS sets the RE (resource element) mapping of CSI-RS resources in the time and frequency domains by the higher layer parameter CSI-RS-ResourceMapping.

[0279] - The terminal reports the measured CSI to the base station.

[0280] Here, if the quantity of CSI-ReportConfig is set to 'none (or No report)', the terminal can skip the report. However, even if the quantity is set to 'none (or No report)', the terminal can still report to the base station. The case where the quantity is set to 'none' is when aperiodic TRS is triggered or repetition is set. Here, the terminal's report can be skipped only when repetition is set to 'ON'.

[0281] 1) CSI measurement

[0282] The NR system supports more flexible and dynamic CSI measurement and reporting. Here, the CSI measurement may include a procedure for receiving a CSI-RS and computing the received CSI-RS to acquire CSI.

[0283] As a time-domain behavior for CSI measurement and reporting, aperiodic / semi-persistent / periodic channel measurement (CM) and interference measurement (IM) are supported. A 4-port NZP CSI-RS RE pattern is used to configure CSI-IM.

[0284] NR's CSI-IM-based IMR has a similar design to LTE's CSI-IM and is configured independently of the ZP CSI-RS resources for PDSCH rate matching. Furthermore, in the NZP CSI-RS-based IMR, each port emulates an interference layer with (preferred channel and) precoded NZP CSI-RS. This is for intra-cell interference measurement in multi-user cases, primarily targeting MU interference.

[0285] The base station transmits precoded NZP CSI-RS to the terminal on each port of the configured NZP CSI-RS-based IMR.

[0286] The terminal assumes a channel / interference layer for each port in the resource set and measures interference.

[0287] For a channel, if there is no PMI and RI feedback, multiple resources are configured in a set, and the base station or network indicates a subset of NZP CSI-RS resources via DCI for channel / interference measurement.

[0288] Let's take a closer look at resource settings and resource setting configuration.

[0289] 2) Resource setting

[0290] Each CSI resource setting 'CSI-ResourceConfig' contains a configuration for S≥1 CSI resource sets (given by the higher layer parameter csi-RS-ResourceSetList). A CSI resource setting corresponds to a CSI-RS-resourcesetlist, where S represents the number of configured CSI-RS resource sets. Here, the configuration for S≥1 CSI resource sets contains each CSI resource set containing CSI-RS resources (consisting of NZP CSI-RS or CSI-IM) and SS / PBCH block (SSB) resources used for L1-RSRP computation.

[0291] Each CSI resource setting is located in a DL bandwidth part (BWP) identified by the higher layer parameter bwp-id. All CSI resource settings linked to a CSI reporting setting have the same DL BWP.

[0292] The time domain behavior of CSI-RS resources within a CSI resource setting included in the CSI-ResourceConfig IE is indicated by the higher layer parameter resourceType, and can be set to aperiodic, periodic, or semi-persistent. For periodic and semi-persistent CSI resource settings, 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 in the numerology of the associated DL BWP, as given by bwp-id.

[0293] When a UE is configured with multiple CSI-ResourceConfigs containing the same NZP CSI-RS resource ID, the same time domain behavior is configured for the CSI-ResourceConfigs.

[0294] When a UE is configured with multiple CSI-ResourceConfigs containing the same CSI-IM resource ID, the same time domain behavior is configured for the CSI-ResourceConfigs.

[0295] One or more CSI resource settings for channel measurement (CM) and interference measurement (IM) are configured via higher layer signaling.

[0296] - CSI-IM resource for interference measurement.

[0297] - NZP CSI-RS resources for interference measurement.

[0298] - NZP CSI-RS resources for channel measurement.

[0299] That is, the CMR (channel measurement resource) can be NZP CSI-RS for CSI acquisition, and the IMR (Interference measurement resource) can be NZP CSI-RS for CSI-IM and IM.

[0300] Here, CSI-IM (or ZP CSI-RS for IM) is mainly used for inter-cell interference measurement.

[0301] And, NZP CSI-RS for IM is mainly used for intra-cell interference measurement from multi-user.

[0302] The UE may assume that the CSI-RS resource(s) for channel measurement and the CSI-IM / NZP CSI-RS resource(s) for interference measurement configured for one CSI reporting are 'QCL-TypeD' per resource.

[0303] 3) Resource setting configuration

[0304] As we have seen, resource setting can mean a resource set list.

[0305] For aperiodic CSI, each trigger state set using the higher layer parameter CSI-AperiodicTriggerState is associated with one or more CSI-ReportConfigs, each of which links to a periodic, semi-persistent, or aperiodic resource setting.

[0306] One reporting setting can be linked to up to three resource settings.

[0307] - When a resource setting is set, the resource setting (given by the higher layer parameter resourcesForChannelMeasurement) is for channel measurement for L1-RSRP computation.

[0308] - When two resource settings are set, the first resource setting (given by the higher layer parameter resourcesForChannelMeasurement) is for channel measurement, and the second resource setting (given by csi-IM-ResourcesForInterference or nzp-CSI-RS -ResourcesForInterference) is for interference measurement performed on CSI-IM or NZP CSI-RS.

[0309] - When three resource settings are set, the first resource setting (given by resourcesForChannelMeasurement) is for channel measurement, the second resource setting (given by csi-IM-ResourcesForInterference) is for CSI-IM based interference measurement, and the third resource setting (given by nzp-CSI-RS-ResourcesForInterference) is for NZP CSI-RS based interference measurement.

[0310] For semi-persistent or periodic CSI, each CSI-ReportConfig is linked to a periodic or semi-persistent resource setting.

[0311] - When one resource setting (given by resourcesForChannelMeasurement) is set, the resource setting is for channel measurement for L1-RSRP computation.

[0312] - When two resource settings are set, the first resource setting (given by resourcesForChannelMeasurement) is for channel measurement, and the second resource setting (given by the higher layer parameter csi-IM-ResourcesForInterference) is used for interference measurement performed on CSI-IM.

[0313] 4) CSI computation

[0314] When interference measurements are performed on CSI-IM, each CSI-RS resource for channel measurements is associated with a CSI-IM resource in the order of the CSI-RS resources and CSI-IM resources within the corresponding resource set. The number of CSI-RS resources for channel measurements is equal to the number of CSI-IM resources.

[0315] And, if interference measurement is performed on NZP CSI-RS, the UE does not expect to be configured with more than one NZP CSI-RS resource in the associated resource set within the resource setting for channel measurement.

[0316] A terminal with the higher layer parameter nzp-CSI-RS-ResourcesForInterference set does not expect more than 18 NZP CSI-RS ports to be set within the NZP CSI-RS resource set.

[0317] For CSI measurement, the terminal assumes the following:

[0318] - Each NZP CSI-RS port configured for interference measurement corresponds to an interference transport layer.

[0319] - All interference transmission layers of the NZP CSI-RS port for interference measurement consider the EPRE (energy per resource element) ratio.

[0320] - Other interference signals on RE(s) of NZP CSI-RS resource for channel measurement, NZP CSI-RS resource for interference measurement or CSI-IM resource for interference measurement.

[0321] 5) CSI Report

[0322] For CSI reporting, the time and frequency resources available to the UE are controlled by the base station.

[0323] CSI (channel state information) may include at least one of a channel quality indicator (CQI), a precoding matrix indicator (PMI), a CSI-RS resource indicator (CRI), an SS / PBCH block resource indicator (SSBRI), a layer indicator (LI), a rank indicator (RI), or an L1-RSRP.

[0324] For CQI, PMI, CRI, SSBRI, LI, RI, and L1-RSRP, the UE is configured by a higher layer 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 the aperiodicTriggerStateList includes a list of associated CSI-ReportConfigs indicating channel and optionally resource set IDs for interference. Each trigger state in the semiPersistentOnPUSCH-TriggerStateList includes one associated CSI-ReportConfig.

[0325] Additionally, the time domain behavior of CSI reporting supports periodic, semi-persistent, and aperiodic.

[0326] i) Periodic CSI reporting is performed on short PUCCH and long PUCCH. The periodicity and slot offset of periodic CSI reporting can be configured via RRC, and refer to the CSI-ReportConfig IE.

[0327] ii) SP (semi-periodic) CSI reporting is performed on short PUCCH, long PUCCH, or PUSCH.

[0328] In case of SP CSI on short / long PUCCH, the period and slot offset are set by RRC, and CSI reporting is activated / deactivated with a separate MAC CE / DCI.

[0329] In the case of SP CSI on PUSCH, the periodicity of SP CSI reporting is set to RRC, but the slot offset is not set to RRC, and SP CSI reporting is activated / deactivated by DCI (format 0_1). For SP CSI reporting on PUSCH, a separate RNTI (SP-CSI C-RNTI) is used.

[0330] The initial CSI reporting timing follows the PUSCH time domain allocation value indicated in the DCI, and subsequent CSI reporting timing follows the cycle set by RRC.

[0331] DCI format 0_1 ​​contains a CSI request field and can activate / deactivate a specific configured SP-CSI trigger state. SP CSI reporting has the same or similar activation / deactivation mechanism as data transmission on the SPS PUSCH.

[0332] iii) Aperiodic CSI reporting is performed on PUSCH and is triggered by DCI. In this case, information related to the triggering of aperiodic CSI reporting can be transmitted / indicated / configured via MAC-CE.

[0333] For AP CSI with AP CSI-RS, AP CSI-RS timing is set by RRC, and timing for AP CSI reporting is dynamically controlled by DCI.

[0334] NR does not apply the method of dividing CSI into multiple reporting instances (e.g., transmitting in the order of RI, WB PMI / CQI, and SB PMI / CQI) used for PUCCH-based CSI reporting in LTE. Instead, NR restricts specific CSI reporting on short / long PUCCHs and defines CSI omission rules. Furthermore, with respect to AP CSI reporting timing, PUSCH symbol / slot locations are dynamically indicated by DCI. Candidate slot offsets are configured by RRC. For CSI reporting, the slot offset (Y) is configured for each reporting setting. For UL-SCH, the slot offset K2 is configured separately.

[0335] Two CSI latency classes (low latency class, high latency class) are defined from the perspective of CSI computation complexity. Low latency CSI is WB CSI including up to 4 ports Type-I codebook or up to 4-port non-PMI feedback CSI. High latency CSI refers to any CSI other than low latency CSI. For a normal terminal, (Z, Z') is defined in units of OFDM symbols. Here, Z represents the minimum CSI processing time from receiving an aperiodic CSI triggering DCI to performing a CSI report. In addition, Z' represents the minimum CSI processing time from receiving a CSI-RS for channel / interference to performing a CSI report.

[0336] Additionally, the terminal reports the number of CSIs it can calculate simultaneously.

[0337] Codebook

[0338] Codebooks supported in NR are largely divided into two types (e.g., Type 1 CSI and Type 2 CSI). Type 1 CSI can be divided into a single panel codebook (CB) and a multi-panel CB. Type 1 CSI CB can be configured to primarily target single user (SU)-MIMO, and the codebook is configured to select / indicate a single or multiple preferred Discrete Fourier Transform (DFT) vector(s) from a set of oversampled DFT vectors based on the spatial domain (SD) and to indicate co-phase with respect to the cross polarization of the base station antenna. For Type 2 CSI, the UE selects multiple DFT vectors based on SD and linearly combines them to form a high-resolution codebook, which is mainly used for the purpose of improving multi-user (MU)-MIMO performance. To improve the high payload, which was a shortcoming of Type 2 CSI introduced in Release 15 NR, eType 2 (enhanced Type 2) CSI was introduced in Release 16 NR, which reduces the codebook payload by considering the correlation of the frequency axis.

[0339] The Rel-16 (eType II) codebook is configured as follows. W_1 and W_2 are the final precoding matrices corresponding to the Tx antennas of each TRP, and the mathematical expression for this is as shown in Equation 3 below.

[0340]

[0341] Here, H can mean Hermitian operation (conjugate transpose), and depending on the frequency domain (FD) basis design, it can be simply replaced by a transpose operation. is a normalization term.

[0342] The spatial domain (SD) basis and the frequency domain (FD) basis (basis vector) are composed of DFT-based vectors (basis vectors). In particular, the SD basis can be determined as a 2D DFT or 1D DFT depending on the antenna structure of the TRP. Here, the DFT vector is an example, and other basis vectors can be used. In addition, M is the number of FD basis.

[0343] In the Rel-15 Type II codebook, among the oversampled discrete Fourier transform (DFT) vectors, L basis DFT vectors are used per specific pole (polarization). Then, a precoding matrix is ​​constructed by applying wideband (WB) amplitude coefficients and sub-band (SB) amplitude / phase coefficients to the basis DFT vectors. Among the oversampled DFT vectors, a specific vector is v l,mIt is the same as and is defined in the standard as mathematical formula 4 below.

[0344]

[0345] Here, N1 and N2 represent the number of antennas (per polarization) of the first domain and the second domain, respectively, per TRP (i.e., the number of antenna ports), which can be set by the upper layer parameters n1-n2-codebookSubsetRestriction. O1 and O2 represent the oversampling factors of the first domain (or first dimension) and the second domain (or second dimension), respectively. The supported configurations of (N1, N2) and (O1, O2) for a given number of CSI-RS ports can be defined in the standard.

[0346] L represents the number of beams, and the value of L is set by the upper layer parameter numberOfBeams. Here, P CSI-RS When =4, L=2, and P CSI-RS When >4, L ∈{2,3,4} can be.

[0347] Here, the index m1 is as shown in the mathematical expression 5 below. (i) Wow m2 (i) These are applied to l and m of Equation 4 respectively, and a precoding matrix can be constructed based on these DFT basis vectors.

[0348]

[0349] Here, i is 0,1,...,L-1. n1 (i) ∈{0,1,...,N1-1}, and n2 (i) ∈{0,1,...,N2-1}. q1∈{0,1,...,O1-1}, and q2∈{0,1,...,O2-1}. n1 (i) Wow n2 (i) The value is determined according to the algorithm defined in the standard.

[0350] In the Rel-16 Type II codebook, the UE compresses and reports codebook information using frequency domain correlation with respect to the above-described Rel-15 Type II codebook.

[0351] At this time, compressed information can be constructed based on a set of some vectors of an oversampled DFT codebook to compress the codebook information. Here, the set of some vectors of the oversampled DFT codebook can be referred to as 'frequency domain (FD) basis vectors'.

[0352] M υ The FD basis vector of a dog is defined in the standard as shown in Equation 6 below.

[0353]

[0354] Here, f=0,1,...,M υ -1. N3 is the total number of precoding matrices. l=1,...,υ, and υ is the rank indicator (RI) value.

[0355] As above, M υ Among the FD basis vectors, the t-th element of the f-th vector (where t=0,...,N3-1) is y t,l (f) It is defined in the standard as shown in mathematical formula 7 below.

[0356]

[0357] Here, n 3,l is defined in the standard as shown in mathematical formula 8 below.

[0358]

[0359] Here, f=0,1,...,Mυ -1. In the above formula, n 3,l (f) M is selected by the terminal from among the oversampled DFT codebooks of size N3 (i.e., from the total number of precoding matrices) through the value υ A combination of FD basis vectors can be reported to the base station.

[0360] Additionally, in the enhanced Type II codebook, a precoding matrix can be indicated by the PMI that the UE reports to the base station, and the precoding matrix is ​​composed of L vectors (i.e., the L value represents the number of beams and is set by the upper layer parameter numberOfBeams) + M υ can be determined from the L vectors (i.e., the number of FD basis vectors set by the base station). Here, the L vectors are i 1,1 , i 1,2 n1∈{0,1,...,N1-1}(the number of antenna ports in the first dimension) and n2∈{0,1,...,N2-1}(the number of antenna ports in the second dimension) are indicated by . q1∈{0,1,...,O1-1}(the oversampling value in the first dimension) and q2∈{0,1,...,O2-1}(the oversampling value in the second dimension) can be identified by the indices.

[0361] Here, PMI corresponds to codebook indices i1 and i2. i1 and i2 can each be composed of multiple indices depending on the number of ranks (υ). That is, PMI can include indices included in i1 and i2 (or indicators for indicating the corresponding indices). For example, in an enhanced Type II codebook, when υ = 1, i1 corresponds to i 1,1 , i 1,2 , i 1,5 , i 1,6,1 , i 1,7,1 , i 1,8,1can include. As another example, if υ=2, i2 is i 2,3,1 , i 2,4,1 , i 2,5,1, i 2,3,2, i 2,4,2 , i 2,5,2 may include. Here, i 2,4,l (l=1,2,3,4) corresponds to the amplitude coefficient indicator to indicate the amplitude coefficient. Here, i 2,5,l (l=1,2,3,4) corresponds to the phase coefficient indicator for indicating the phase coefficient.

[0362] For the enhanced Type II codebook, it can be largely composed of three parts: the SD basis part, the coefficient matrix part, and the FD basis part.

[0363] First, in SD basis (i.e. W SD ) is described. Hereinafter, for convenience of explanation, it is assumed that two TRPs are associated with each other, but this is only one example and the present disclosure is not limited thereto.

[0364]

[0365] In mathematical expression 9, is the i-th SD basis vector, and its size is N Tx / 2, and v in the above mathematical expression 4 l,m can be replaced by linear combining vectors (b1... b L ) is of size N Tx, where L is the number of basis vectors that are linearly combined. The reason why mathematical expression 9 is configured in the form of a block diagonalization matrix is ​​because it takes into account the cross-polarization (X-pol) antenna that the base station (or TRP) is equipped with. In Fig. 9, the same number of basis vectors and the same basis vector are considered for each polarization, and this is a form suitable for a case where a single TRP is considered. However, this is one example and the present disclosure is not limited thereto. If the above form is configured to be suitable for a multi-TRP form, it can be as shown in mathematical expression 10 below.

[0366]

[0367] In the above mathematical expression 10, is the j-th (j=1,...,L) TRP corresponding to the i-th (i=1,...,M) TRP (if the number of TRPs is M). i ) SD basis vector(L i is the number of basis vectors that are linearly combining for the i-th TRP, and its size is N Tx,i / 2, and v in the above mathematical expression 4 l,m can be replaced by . As can be seen in Equation 10, the SD basis is a basis vector and its number (i.e., L) for each TRP. i ) can be independently selected. For example, L i ∈{2,4,6}. In addition, although Equation 10 exemplifies a case where a common basis vector is selected for each polarization, the present disclosure is not limited thereto. In the case where the above proposal is set most flexibly, the formula can be transformed as in Equation 11 below.

[0368]

[0369] In mathematical expression 11, is the j-th (j=1,...,L) corresponding to the p-th polarization (p=1,2) in the i-th (i=1,...,M) TRP. i,p ) is the SD basis vector, and its size is N Tx,i / 2, and v in the above mathematical expression 4 l,m can be replaced by . As can be seen in Equation 11, the SD basis is the basis vector and its number (i.e., L) for each TRP star and polarization. i,p ) can be independently selected. Alternatively, the oversampling factor and / or type of the basis for constructing the SD basis (e.g., DFT, DCT (discrete cosine transform)) can also be independently set for each TRP.

[0370] In addition, in the above mathematical expressions 10 and 11, the order of each block is exemplified as the 1st polarization of TRP1 -> 1st polarization of TRP2 -> 2nd polarization of TRP1 -> 2nd polarization of TRP2, but this is just one example, and the order may vary depending on the CSI-RS settings and antenna port mapping.

[0371] Referring again to Figure 9, the coefficient matrix (i.e., the combining matrix) ) is described.

[0372] The dimension size of the above combining matrix is ​​2L-by-M (the number of rows is 2L and the number of columns is M) based on the above mathematical expression 9. If the above mathematical expression 10 is considered, the dimension size is It can be, and considering the above mathematical expression 11, the dimension size is It could be.

[0373] In the case of the above combining matrix, it is composed of complex values ​​and can be divided into an amplitude part and a phase part.

[0374] In the existing legacy codebook construction method, the amplitude is composed in two stages, quantized into 4 bits between polarizations (i.e., the strongest coefficient is selected for each polarization, the larger polarization among these two coefficients is assumed to be 1, and the coefficients of the remaining polarizations are quantized into 4 bits), and within the same polarization, it is composed into 3 bits based on the strongest coefficient of each polarization.

[0375] Tables 6 and 7 show the corresponding values ​​for each quantization payload. Table 6 illustrates the case of quantization between polarizations, and Table 7 illustrates the case of quantization within the same polarization.

[0376]

[0377]

[0378] The phase consists of 4 bits (e.g., 16PSK), and 4-bit quantization is performed based on the strongest coefficient.

[0379] To improve the performance of CSI measurement and reporting in the CJT of M-TRP, the following can be considered.

[0380] The Rel-18 CJT codebook can be configured as RRC Mode 1 or Mode 2 via higher layer signaling (e.g., RRC signaling). In Mode 2, the same FD basis is restricted to all TRPs. On the other hand, in Mode 1, the FD basis can be configured differently for each TRP, and each TRP can be represented in the codebook with a different M by N3 (i.e., the number of rows is M and the number of columns is N3) basis matrix.

[0381] CSI transmission and reception method

[0382] According to the Rel-18 MIMO work item, the purpose of the Type II codebook enhancement is to help the base station perform better link level adaptation to time varying channels due to the high mobility of UEs.

[0383] Specifically, an enhanced type II codebook with time domain compression can provide the base station with precoding information for multiple time instances instead of a single time instance that is vulnerable to channel aging, thereby enabling the base station to track and predict time-varying channel directions and ultimately schedule better modulation and coding schemes (MCSs) and precoders.

[0384] - Channel measurements for type II codebook enhancement:

[0385] Type II codebook enhancements through time domain (TD) compression require multi-channel measurements on the UE side, requiring legacy UEs to perform multi-channel measurements. Current specifications allow for time domain channel / interference measurement restrictions to be enabled / disabled by the eNB. When time domain channel / interference measurement restrictions are enabled (timeRestrictionForChannelMeasurements is set), the UE measures the latest channel measurement resource (CMR) / interference measurement resource (IMR) before the CSI reference resource, so a single measurement is used for channel estimation on the CSI reference resource. When time domain channel / interference measurement restrictions are disabled (timeRestrictionForChannelMeasurements is not set), the UE can measure multiple CMR / IMR instances before the CSI reference resource, and it is up to the UE implementation to determine how these measurements are used to estimate the channel on the CSI reference resource. With this unrestricted measurement configuration, the UE can already compute CSI using multiple measurements. When codebook enhancement through TD compression is introduced in Rel-18, such an unrestricted measurement setup is required for the UE to compute PMI for multiple time instances.

[0386] Whether multi-channel measurements are possible depends not only on the time-domain measurement limitations described above, but also on the periodicity of the CMR / IMR. Aperiodic (AP) CMR / IMR has no periodicity and therefore only a single CMR / IMR instance, allowing for a single measurement. On the other hand, periodic (P) / semi-persistent (SP) CMR / IMR can provide multiple opportunities for channel / interference measurements to the UE. However, since the minimum period of CSI-RS resources is 4 slots according to the current specification, it is necessary to consider whether sufficiently frequent measurement instances can be provided for high-speed UEs. If the CSI-RS resources are too sparse, poor TD compression may result in poor PMI accuracy, while if the CSI-RS resources are too dense, CSI-RS overhead may increase.

[0387] - The multiple time instances represented by the enhanced type II codebook using TD compression are as follows.

[0388] Similar to legacy type II codebooks using frequency domain (FD) compression that represent PMIs for multiple subbands, new type II codebooks using TD compression represent PMIs for multiple time instances, and therefore a method for determining these multiple time instances must be discussed. Legacy CSI, including PMI / RI / CQI, represents a channel for a single point in time, referred to as the CSI reference resource. However, type II codebooks applying TD compression must be able to provide PMI not only for the CSI reference resource but also for other slots / symbols. Basically, these time instances can be defined as being behind (i.e., in the future) of the CSI reference resource (as in FIG. 17) or ahead (i.e., in the past) of the CSI reference resource (as in FIG. 18), which may have different impacts on the UE implementation.

[0389] A CSI reference resource may refer to a frequency and time unit (i.e., resource) that a UE assumes is allocated / transmitted a PDSCH when calculating / deriving CSI, and may be defined as follows.

[0390] The CSI reference resource for the serving cell is defined as follows:

[0391] - In the frequency domain, a CSI reference resource is defined as a group of downlink physical resource blocks (PRBs) corresponding to the band in which the derived CSI is related.

[0392] - In the time domain, the CSI reference resource for CSI reporting in uplink slot n' is a single downlink slot. is defined by . Here, K offset is a parameter set by the upper layer as specified in section 4.2 of TS 38.213. And, μ Koffset K as a value of 0 for FR 1 offset This is the subcarrier spacing (SCS) setting.

[0393] Here, and μ DL and μ UL are the SCS settings for DL ​​and UL, respectively. N slot,offset CA and μ offset is determined by the ca-SlotOffset set by the upper layer for cells transmitting uplink and downlink as defined in TS 38.211 section 4.5.

[0394] Here, for periodic and semi-persistent CSI reporting, i) if a single CSI-RS / SSB resource is configured for channel measurement, n CSI_ref 4·2 so that the CSI reference resource corresponds to a valid downlink slot. μDL is greater than or equal to the minimum value, or ii) if multiple CSI-RS / SSB resources are configured for channel measurement, n CSI_ref 5·2 so that the CSI reference resource corresponds to a valid downlink slot. μDL The minimum value greater than or equal to .

[0395] Also, in case of aperiodic CSI reporting, if the UE is instructed by DCI to report CSI within the same slot as the CSI request, n CSI-refIt is determined that the CSI reference resource is within the same valid downlink slot as the corresponding CSI request. Otherwise, n CSI_ref is slot nn CSI_ref To correspond to this valid downlink slot is a minimum value greater than or equal to , where Z' corresponds to the delay requirement as defined in clause 5.4 of TS 38.214.

[0396] FIG. 17 illustrates PMI for multiple time instances that are not faster than a CSI reference resource in a wireless communication system to which the present disclosure may be applied.

[0397] Referring to Figure 17, the UE uses the existing PMI based on the DL channel of the CSI reference resource. ref_rsc , which can be derived from a periodic CMR that is no later than the CSI reference resource. In addition, the UE calculates PMI based on the DL channel of the CSI reference resource+τ slot and the CSI reference resource+2τ slot, respectively. ref_rsc+τ and PMI ref_rsc+2τ , which is predicted from periodic CMRs that are no later than the CSI reference resource. Finally, PMI ref_rsc , PMI ref_rsc+τ and PMI ref_rsc+2τ is compressed to reduce PMI feedback overhead using an enhanced type II codebook with TD compression. This codebook contains future PMIs that require the UE to predict the DL channel after the CSI reference resource, making it robust to channel aging and enabling the base station to utilize it for better link adaptation.

[0398] FIG. 18 illustrates PMI for multiple time instances not later than a CSI reference resource in a wireless communication system to which the present disclosure can be applied.

[0399] Referring to Figure 18, the UE uses the legacy PMI in the same manner as described previously in Figure 17. ref_rsc However, unlike in Fig. 17, the UE calculates PMI based on the DL channel in the CSI reference resource-τ slot and the CSI reference resource-2τ slot, which are periodic CMRs that are not later than the reference resource. ref_rsc-τ and PMIref_rsc-2τ are calculated. Figure 2 shows an example where periodic CMRs are in the CSI reference resource, CSI reference resource-τ slot, and CSI reference resource-2τ slot. Finally, PMI ref_rsc , PMI ref_rsc-τ and PMI ref_rsc-2τ is compressed to reduce the PMI feedback overhead using an enhanced type II codebook with TD compression. This codebook includes past PMIs, so the UE does not need to predict additional DL channels and is less complex than in Fig. 17. However, PMI ref_rsc-τ and PMI ref_rsc-2τ is the existing PMI ref_rsc There is a problem that it is older.

[0400] A time instance can refer to a point in time (e.g., a slot) at which a PMI is calculated. In Fig. 17, a time instance represents a CSI reference resource (slot), a CSI reference resource+τ slot, and a CSI reference resource+2τ slot. In Fig. 18, a time instance represents a CSI reference resource (slot), a CSI reference resource-τ slot, and a CSI reference resource-2τ slot.

[0401] To determine the time instance of the channel represented by the PMI as shown in FIGS. 17 and 18, the base station may perform the following signaling to the UE. For example, it may be indicated as a parameter of RRC signaling for codebook configuration.

[0402] For example, the number of time instances to be expressed as PMI can be indicated / signaled first. In the examples of FIGS. 17 and 18, the number of time instances is exemplified as 3, but the present disclosure is not limited thereto. The number of time instances can be set in various ways considering the time variability of the channel, and for this setting, the UE can report its velocity information, Doppler information (Doppler shift / spread), etc. to the base station. Alternatively, the UE can report its preferred number of time instances based on its velocity or Doppler information, etc. to the base station, and the base station can confirm or make a final selection (e.g., select a different number of time instances). For example, the UE can report candidate values ​​for the number of time instances to the base station as capabilities.

[0403] Also, for example, the interval between each time instance can be indicated / signaled, which indicates the τ value expressed in FIG. 17 and FIG. 18. The τ value can be set in various ways considering the time-varying nature of the channel, and for this setting, the UE can report its velocity information, Doppler information (Doppler shift / spread), etc. to the base station. Alternatively, the UE can report its preferred τ value based on its velocity or Doppler information, etc. to the base station, and the base station can confirm it or make a final selection (e.g., determine a different τ value). For example, the τ value can be expressed in absolute time (e.g., ms, etc.), slot, OFDM symbol, etc. The UE can report candidate values ​​for the τ value to the base station as capabilities.

[0404] Also, for example, it can be indicated / signaled in which slot / symbol the time instances are located. For example, a time instance offset can be signaled, and the time instance offset can be determined based on the CSI reference resource. In other words, it can be indicated / signaled which time instance the CSI reference resource corresponds to among the time instances configured above. In Fig. 17, the CSI reference resource is configured in the first time instance among the three time instances, and the base station can set / indicate the time instance offset of the CSI reference resource to 0 to indicate this. Since the CSI reference resource is configured in the first time instance, the remaining time instances thereafter are configured after the CSI reference resource. In Fig. 18, the CSI reference resource is configured in the last (third) time instance among the three time instances, and the base station can set / indicate the time instance offset of the CSI reference resource to 2 to indicate this. As the CSI reference resource is set to the third time instance, the remaining time instances prior to that time instance are set to the previous CSI reference resource. The time instance offset can be configured in various ways to consider the temporal variation of the channel. To achieve this, the UE can report its velocity information, Doppler shift / spread, etc. to the base station.Alternatively, the UE may report its preferred time instance offset to the base station based on its speed or Doppler information, and the base station may confirm or make a final selection (e.g., determine a different time instance offset value). In addition, depending on the UE implementation, a UE may be divided into a UE that can set the time instance offset (i.e., CSI reference resource) only to the last time instance, as shown in FIG. 18, and a UE that can set the time instance offset (i.e., CSI reference resource) to other time instances other than the last time instance, as shown in FIG. 17, and the UE may report this to the base station as a capability. The former (in the case of FIG. 18) is simple to implement because the UE does not need to perform channel prediction, but the latter (in the case of FIG. 17) may be complex to implement because it needs to perform channel prediction. More specifically, in the latter case (in the case of FIG. 17), the UE may additionally report to the base station the minimum value of the configurable time instance offset or a candidate of the configurable time instance offset values. The smaller the minimum value, the more predictions must be performed, which can make the UE implementation more complex.

[0405] In the above proposal, a grid of time instances is set by the number of time instances and the interval (i.e., τ value), and the slot / symbol in which the grid of the time instance is located can be determined by the time instance offset for the CSI reference resource.

[0406] Similarly, but in a different way of expressing it, the grid of time instances can be determined by setting the window of time instances as slots / symbols and setting the number of time instances within the window. That is, time instances can be set at equal intervals as the number of time instances within the window. For example, if the window (which can be referred to as the CSI reporting window) is set to 3 slots and the number of time instances is set to 3, the grid of time instances is expressed as 3 consecutive slots. Here, if the time instance offset is 0, the final time instance is set to the CSI reference resource slot, the CSI reference resource + 1 slot, and the CSI reference resource + 2 slot.

[0407] The above example illustrates the case where time instances are set at equal intervals. However, if the channel's temporal variation varies over time and is predictable, this equal-spaced distribution may be inefficient. For example, when N time instances are set, if the channel's temporal variation is severe in the early part of the entire time interval represented by the PMI and less severe in the later part, distributing time instances densely in the early part and sparsely in the later part can improve compression efficiency and accuracy for TD compression. For example, if the CSI reference resource is located in the first time instance, the second time instance may be located in the CSI reference resource + 1 slot, the third time instance may be located in the CSI reference resource + 2 slot, and the fourth time instance may be located in the CSI reference resource + 5 slot. Therefore, the spacing between time instances may not be equal. To this end, the UE can report the time instance locations to the eNB (along with the CSI). Alternatively, the eNB can instruct / configure the time instance locations to the UE. For example, it can be indicated in UL DCI that triggers AP CSI reporting.

[0408] Some parameters related to the proposed time instance configuration are fixed (with predefined values), and the remaining parameters can be configured by the base station or reported by the UE.

[0409] In the above proposal, the time instance offset is described based on the CSI reference resource, but this is only one example, and the present disclosure is not limited thereto. For example, the time instance offset may be set based on the CSI reporting time (i.e., the time at which CSI is reported / transmitted) or the last time point of the CSI measurement window. Here, the CSI measurement window refers to a time period during which the channel / interference can be measured for CSI calculation. For example, the offset (e.g., the number of slots) between the first time instance among the time instances from the CSI reporting time or the last time point of the CSI measurement window may be set as the time instance offset.

[0410] In the above proposal, the time instance offset can be set to a negative value or a value greater than or equal to the number of time instances (or the number of time instances * the interval between time instances). For example, when the time instance offset is set with respect to the CSI reference resource, if the time instance offset is negative, time instances can only exist at a point in the future relative to the CSI reference resource. For example, when the number of time instances is 3, the interval between time instances is 1 slot, and the time instance offset set with respect to the CSI reference resource is -1, the time instances can be set to the CSI reference resource + 1 slot, the CSI reference resource + 2 slots, and the CSI reference resource + 3 slots.

[0411] On the other hand, if it is set to a value greater than the number of time instances (or the number of time instances * the interval between time instances), time instances can exist only at a time in the past compared to the CSI reference resource. For example, when the number of time instances is 3, the interval between time instances is 1 slot, and the time instance offset set based on the CSI reference resource is 4, the time instances can be set to the CSI reference resource-3 slot, the CSI reference resource-2 slot, and the CSI reference resource-1 slot.

[0412] The range of values ​​that can be set with this time instance offset can be reported by the UE to the base station (as a capability), and in this case, the base station can indicate / set the time instance offset value within this range.

[0413] When the base station indicates to the UE the parameters / configuration values ​​related to the proposed method, in the case of AP CSI reporting, this may be indicated together with the AP CSI reporting trigger field in the DCI. Alternatively, it may be indicated through configuration information related to CSI reporting (e.g., CSI-ReportConfig IE) or in configuration information related to the PMI codebook.

[0414] In addition, the UE can report (as a capability) the maximum value for each of the number of time instances and / or the time instance interval to the base station. Since the larger the number of time instances and / or the time instance interval, the more calculations the UE may require for CSI calculation, the UE may report the maximum value for this to the base station, and the base station may set the number of time instances and / or the time instance interval to the UE within a range that does not exceed the maximum value. Alternatively, the UE may report (as a capability) a combination of the number of time instances and the interval to the base station. For example, the UE may report to the base station the maximum slot interval when the number of time instances is N1, and the maximum slot interval when the number of time instances is N2. In other words, the UE may report to the base station the maximum time instance interval that can be supported for each number of time instances. Alternatively, conversely, the UE may report to the base station the maximum number of time instances that can be supported for each time instance interval. This reporting method can be effective because as the number of time instances increases, the maximum interval supported by the UE can become smaller.

[0415] This reporting method can also be applied when using time instance windows instead of time instance intervals, replacing the time instance interval with the time instance window. That is, the UE can report to the base station the maximum number of time instance windows it can support for each number of time instances. Alternatively, the UE can report to the base station the maximum number of time instances it can support for each time instance window.

[0416] The time instances for deriving CSI (or PMI) can be finally applied / determined through a combination / combination of the above suggestions.

[0417] In the Rel-18 standard, a standardization is in progress to define an operation to compress PMI information for multiple time instances and express / report it as a single codebook by adding a Doppler domain (DD) (or time domain) basis vector in addition to the spatial domain basis vector and the frequency domain basis vector for the Rel-17 enhanced Type II codebook (i.e., DD (Doppler domain) compression). For this purpose, various codebook parameters have been introduced as shown in Table 8 below, and discussions are underway to determine their values.

[0418] Parameter DescriptionSignalingAgreed valuesFFS (for future study)N4DDBasis vector lengthRRC1,2,4,83,5,6,10,16,32,also supported parameter combination(s)d (slots)Time unit (similar to frequency unit in type II enhanced codebook)For AP CSI-RS CMR, d=m where m={1,2}SP / P For CSI-RS, d<m, d>m, where d=1 or related to the period of the CSI-RSW CSI =N4*d Compression time window δ (within slots) reporting slots n and W CSIOffset between start slots RRC0 and 21, 3, 4, and 5 Number of QDD basis 23 / 4, supported parameter combination(s) Parameter K (Number of AP CSI-RS resources for CMR) RRC4, 85, 12, 16m (Offset between two AP CSI-RS resources for CMR, in slots) RRC1, 2 NZC (non-zero coefficient) bitmap Q different 2-dimensional bitmaps are introduced FFS: Additional overhead reduction for bitmap(s) FFS: Whether the number of NZCs is upper bounded across all basis vectors or per DD basis vector SD (spatial domain) / FD (frequency domain) codebook parameters The definitions and supported values ​​for each of the SD / FD codebook parameters follow the legacy specifications FFS: Supported parameter combinations considering SD, FD, and DD codebook parameters Number of CQIs 1 FFS: Multiple X CQIs 3 alternatives for defining CQIs* If X CQIs are supported, the CSI reporting window W CSI Given (within the slots), the PMI(s) associated with the X value(s) and CQI are also determined.

[0419] FIG. 19 illustrates PMI information indicated by a codebook for a specific codebook parameter in a wireless communication system to which the present disclosure can be applied.

[0420] Figure 19 illustrates a PMI reporting window based on reporting slot n (i.e., mode 1) when d = 1 slot, δ (delta) = 0 slot, N4 = 4, and Q = 2.

[0421] A CSI reporting window starts with an offset of delta slots for CSI reporting slot n. In the example of Fig. 19, since delta = 0, the CSI reporting window starts from CSI reporting slot n. Here, the CSI reporting window consists of d*N4 slots. In the example of Fig. 19, since d = 1, the time unit per PMI is 1 slot, and since N4 = 4, DD domain compression is performed for a total of 4 PMIs.

[0422] In other words, a UE configured with a CSI reporting configuration (CSI-ReportConfig) having a higher layer parameter N4 and a reporting quantity (reportQuantity) set to 'cri-RI-PMI-CQI', according to UE capability, is assumed to support UE-side CSI prediction. The reported PMI indicates predicted precoder matrices associated with N4 consecutive slot intervals, each having a duration of d slots. Here, the value of N4 ∈ {1,2,4,8} is set by the higher layer parameter N4. If the UE is configured with an aperiodic CSI-RS resource set for channel measurement, the value of the time unit d ∈ {1,m} is the number of slots and is set by the higher layer parameter d. If the UE is configured with a periodic or semi-persistent CSI-RS resource set for channel measurement, the value of d is equal to the period of the CSI-RS resource. The earliest of the N4 slot intervals starts at slot l = n + delta, where n is the uplink slot in which CSI is reported, and the slot offset delta ∈ {-n CSI_ref ,0,1,2} is set by the upper layer parameter delta, where delta = n CSI_refcan be set according to UE capability.

[0423] The UE predicts N4 PMIs and reports them to the base station. Instead of generating and reporting each of the N4 PMIs to the base station, it compresses the N4 PMIs using DD basis vectors to generate one PMI, and reports the generated one PMI to the base station. For this, Q DD basis vectors are used, and 2L SD basis vectors and M FD basis vectors are used. That is, a total of 2L*M*Q vector combinations are possible for the 2L SD basis vectors, M FD basis vectors, and Q DD basis vectors, and a weighting value is applied by multiplying an independent coefficient for each vector combination. Each vector combination is expressed as coefficient * SD basis vector (x) FD basis vector (x) DD basis vector (wherein, the symbol (x) means Kronecker product), and the PMI compressed in the space, frequency, and time axes is expressed through the sum of these vector combinations. In Fig. 19, W1 denotes an SD basis matrix composed of SD basis vectors, and Wf denotes an FD basis matrix composed of FD basis vectors.

[0424] In the present disclosure, in case of reporting CSI for multiple time instances as above, when PMI for three time instances is compressed and reported to the base station as in the examples of FIGS. 17 and 18, the UE can calculate / report PMI through a codebook having a structure similar to the agreement in Table 9 below using Q length 3 DD basis vectors.

[0425]

[0426] In Table 9, W1 represents the spatial domain (SD) basis matrix, and W f stands for the frequency domain (FD) basis matrix, and W d stands for the Doppler domain (DD) basis matrix, refers to a matrix composed of coefficients applied between SD / FD / DD basis vectors.

[0427] When N4=1, the DD basis is legacy W1, , W f can be equivalent to using (i.e., no Doppler domain compression applied), and for N4>1, legacy W1, W f For all SF / FD basis using , the DD orthogonal DFT basis can be commonly chosen.

[0428] Referring to Table 8 and Table 9, in the Rel-18 Type-II codebook, N4 means the DD basis vector length, and Q means the number of DD basis vectors used in the codebook, both of which are set / instructed by the base station to the UE by RRC signaling. In addition, since N4 means the number of time instances of the PMI to be compressed, in the case of FIG. 17 and FIG. 18, N4 = 3. That is, the UE selects Q vectors from the DFT matrix of size N4 × N4 (i.e., N4 by N4) and W d We can construct a matrix, which is an FD basis matrix W f It can be expressed through the Kronecker product.

[0429] As described above, the UE predicts the channel for multiple time instances and calculates the PMI for the predicted channel. However, the channel prediction accuracy may be low for some time instances. For example, when the UE estimates the channel for eight time instances (i.e., when N4=8), the channel prediction accuracy may be relatively low for the last two time instances, which correspond to the most future time points. In this case, it is preferable to exclude the PMIs for those two time instances and compress only the remaining PMIs, rather than compressing and transmitting the PMIs for those two time instances together. This is because the PMIs for those two time instances may have low prediction accuracy and may be of no use, and compressing them with other PMIs may increase the quantization error (i.e., information loss due to compression) of the remaining six PMIs, which have high prediction accuracy. In other words, by excluding those two PMIs from compression, the quantization error (i.e., information loss due to compression) for the remaining six PMIs can be reduced.

[0430] Therefore, in the present disclosure, we propose a method in which a UE calculates a codebook / PMI by excluding one or more time instances from among the time instances set by the base station and reports the same to the base station. In the example above, the UE can calculate the codebook / PMI by puncturing / assuming the last two elements of each length 8 DD basis vector to 0 to exclude two PMIs with low prediction accuracy.

[0431] In the following description of the present disclosure, a time instance refers to a predetermined unit of time resources, and may be set / defined as, for example, one slot, one or more slots, or one or more symbols. In addition, a time instance may be set continuously or discontinuously.

[0432] According to one embodiment of the present disclosure, the UE may puncture some elements of each DD basis vector (i.e., elements corresponding to excluded time instances) to assume them as '0' and calculate the codebook / PMI. Here, puncturing may mean an operation of replacing / assuming the value of the corresponding element as 0.

[0433] Additionally, as another way to implement the same functionality, the puncturing element can be replaced by removing the value corresponding to the puncturing element from the basis vector. That is, the UE can calculate the codebook / PMI based on the DD basis vector derived by removing some elements from each DD basis vector. For example, a length 4 basis vector [1 2 3 4] T Puncturing the 2nd and 4th elements in [1 2 3 4] T is

[0013] T is replaced by [1 2 3 4] or when puncturing the 3rd or more elements T is

[0012] T can be replaced with . As a result, the N4 value itself can be changed from 4 to 2.

[0434] Here, for some elements, the UE can select a specific n1, n2, n3, ..., nk-th element (i.e., select one or more non-contiguous / contiguous elements) and apply it commonly to all DD basis vectors (i.e., puncture / assume the corresponding element value to 0 or generate a DD basis vector with the corresponding element value removed). Alternatively, the UE can select n and puncture the n-th or more elements of all DD basis vectors or remove the n-th or more elements (i.e., select one or more consecutive elements starting from the n-th element).

[0435] The range and / or minimum and / or maximum values ​​of elements selectable by the UE may be preset by the base station for the UE. For example, n > N4 / 2 (i.e., minimum value N4 / 2) or n > 4 (i.e., minimum value 4), etc. In addition, for example, different (i.e., individually) ranges and / or minimum and / or maximum values ​​may be set for each rank.

[0436] Information about punctured elements (or information about removed elements) can be expressed using a bitmap.

[0437] For example, the kth bit of a bitmap consisting of N4 bits can indicate whether the kth element is punctured (or removed). Alternatively, the very first element can be excluded from puncturing (or removal) (i.e., the channel is always reported), and a bitmap consisting of N4-1 bits can be used to indicate whether the second to N4th elements are punctured (or removed). Alternatively, for example, if the UE reports a value of n (e.g., if elements beyond the nth element are punctured (or removed), the UE can report the n value itself instead of the bitmap.

[0438] The UE can select the element to be punctured (or removed) and report its value (e.g., n (i.e., reported by the element where puncturing / removal is started) or {n1, n2, n3, ..., nk} (i.e., reported by the element being punctured / removed)) to the base station, and the base station can know that the time instance corresponding to the puncturing (or removal) is excluded from the PMI report based on the report from the UE.

[0439] A CSI report may include two parts (i.e., part 1 and part 2). Part 1 may have a fixed payload size and may be used to identify the number of information bits in part 2. Additionally, part 1 may be transmitted in its entirety before part 2. Additionally, parts 1 and 2 may be encoded separately. For example, part 1 may include the RI (if reported), the CQI, and the total number of non-zero amplitude coefficients reported across layers. In this case, part 2 may include the PMI. As another example, part 1 may include the RI (if reported), the CQI or the first CQI, and the total number of non-zero amplitude coefficients reported across layers. In this case, part 2 may include the second CQI and the PMI.

[0440] Since the puncturing (or removal) information described above (i.e., information on which element is punctured or removed) does not affect the codebook payload, it may be encoded in CSI part 1 and reported to the base station. Alternatively, if the puncturing element (or removed element) is affected by some codebook parameter of CSI part 1, the puncturing (or removal) information (i.e., information on which element is punctured or removed) may be encoded in CSI part 2 and reported to the base station.

[0441] The UE can consider multiple time instances when calculating not only PMI but also CQI.

[0442] Here, the UE can calculate the highest CQI for multiple time instances. For example, when calculating a CQI for multiple time instances, the UE can calculate the highest CQI that satisfies a target block error rate (BLER) for all of the time instances, or the UE can calculate the highest CQI that satisfies the target BLER for some of the time instances (e.g., i) the first time instance and the middle time instances, or ii) the first time instance and the last time instance). In this case, the UE can calculate the CQI without considering (i.e., not using in the CQI calculation) the time instances corresponding to the punctured (or removed) elements.

[0443] Additionally, the UE may compute multiple CQIs for multiple time instances. For example, the UE may compute a CQI for each of, say, N4 time instances, or the UE may compute a CQI for each of some of the N4 time instances (e.g., i) the first time instance and the middle time instances, or ii) the first time instance and the last time instance). In this case, the UE may compute the CQI without considering the time instances corresponding to the punctured (or removed) elements (i.e., not using them in the CQI calculation), or the UE may not compute / report the CQI for the time instances corresponding to the punctured (or removed) elements (i.e., only compute the CQI for the remaining time instances (or some of the remaining time instances).

[0444] As described above, when a UE excludes one or more time instances from among the time instances set by the base station, the UE may do so based on the prediction accuracy for the channel. In other words, the base station may set a threshold value for judging the prediction accuracy for the UE to determine whether to puncture (or remove) specific one or more elements based on the prediction accuracy. That is, the UE may puncture (or remove) one or more elements only if the prediction accuracy for the channel is lower than the threshold value.

[0445] In addition, instead of reporting the CQI (and / or PMI) for the punctured (or removed) time instance, the UE may report the accuracy / confidence level of the CQI (and / or PMI) estimation / prediction value of the corresponding time instance, or the difference value from the threshold (hereinafter referred to as prediction accuracy information), etc. to the base station. Accordingly, the base station can receive information on channel change / prediction performance in the time domain (i.e., on the time axis). Here, as described above, when the UE encodes the information on the punctured (or removed) time instance through CSI part 1 and reports it to the base station, the UE may encode the information on the prediction accuracy for the punctured (or removed) time instance through CSI part 2 and report it to the base station.

[0446] In addition, regardless of the time instances punctured (or removed) by the UE, the base station can configure for the UE which time instances the prediction accuracy is to be reported, or the UE can select which time instances the prediction accuracy is to be reported for and report the prediction accuracy of the corresponding time instances to the base station. Here, when the UE selects a time instance for which the prediction accuracy is to be reported and reports it to the base station, the time instance information (i.e., information about the time instance for which the prediction accuracy is reported) can be reported using the method for reporting information on punctured (or removed) time instances proposed above. That is, the UE can select time instances {n1, n2, n3, ..., nk} for which the prediction accuracy is to be reported (e.g., using a bitmap) and report them to the base station as CSI part 1 or CSI part 2. Alternatively, the UE may select a value n, which is the start time of the time instance for which prediction accuracy is to be reported (in this case, prediction accuracy is reported for the nth to N4th time instances) and report it to the base station as CSI part 1 or CSI part 2.

[0447] As described above, when puncturing is applied to one or more elements of the basis vector (or one or more elements are removed to reduce the basis vector length), CSI compression for the corresponding time instance is omitted, so the same compression effect can be achieved with fewer basis vectors. Therefore, when puncturing is applied to one or more elements (or one or more elements are removed to reduce the basis vector length), the PMI overhead can be reduced by reducing the number of basis vectors Q. For example, when N4 and Q are set from the base station, if the number of elements that are not punctured (or not removed) through puncturing (or through element removal) (i.e., # of non-puncturing elements = N4 - # of puncturing elements) is greater than N4 / 4 and less than or equal to N4 / 2, the Q value can be reduced to Q=Q / 2. Alternatively, if the number of non-punctured (or non-removed) elements through puncturing (or element removal) is greater than or equal to N4 / 8 and less than or equal to N4 / 4, then the value of Q can be reduced to Q=Q / 4. That is, if N4 / 2n+1 < the number of non-punctured (or non-removed) elements (i.e., # of non-puncturing elements <= N4 / 2n), then the value of Q can be reduced to Q=Q / 2n.

[0448] In the proposed method of the present disclosure described above, a time instance may mean a specific slot, but the present disclosure is not limited thereto. That is, in the present disclosure, a time instance may mean a time duration composed of multiple slots (or composed of multiple symbols).

[0449] In addition, the proposed method of the present disclosure described above can be ultimately applied through a combination / combination of one or more proposed methods.

[0450] In addition, the proposed method of the present disclosure described above has been mainly explained using a codebook to which DD compression using a DD basis is applied as an example, but this is for convenience of explanation, and the proposed method of the present disclosure can be equally applied to a codebook to which TD compression using a TD (time domain) basis is applied.

[0451] In addition, the proposed method of the present disclosure described above is also applicable to a case where an AI / ML UE predicts and reports future CSI (e.g., CSI for a channel after a CSI reporting time point or CSI for a channel after a CSI reference resource) to a base station. For example, the UE may be configured to function for the AI / ML operation illustrated in FIG. 12, and may acquire / derive future CSI through channel / CSI data collection, model training using the data, and inference of CSI. In other words, if a plurality of time instances set by the base station are after the CSI reporting time point or the CSI reference resource (or if some of the plurality of time instances set by the base station are after the CSI reporting time point or the CSI reference resource), the UE may exclude one or more time instances from the configured time instances, calculate a codebook / PMI, and report the codebook / PMI to the base station.

[0452] In addition, in the description of the proposed method of the present disclosure described above, the parameters exemplified / described, or whether or not the proposed method is applied, etc. may be set / instructed by the base station to the UE, or reported by the UE to the base station, or may be set / defined as a fixed value.

[0453] In addition, although the proposed method of the present disclosure described above has been described based on the Rel-16 eType II codebook for convenience of explanation, the same proposed method can be extended and applied to the Rel-17 FeType II codebook, and can also be extended and applied to codebooks defined in future releases.

[0454] FIG. 20 is a diagram illustrating a signaling procedure between a network and a UE for a method of transmitting and receiving channel state information according to one embodiment of the present disclosure.

[0455] FIG. 20 illustrates signaling between a network (e.g., TRP 1, TRP 2) and a terminal (i.e., UE) in a situation of multiple TRPs (i.e., M-TRP, or multiple cells, hereinafter, all TRPs can be replaced with cells) to which the methods proposed in the present disclosure can be applied.

[0456] Here, UE / Network is merely an example and can be replaced with various devices, as described in FIG. 23 below. FIG. 20 is merely for convenience of explanation and does not limit the scope of the present disclosure. Furthermore, some steps shown in FIG. 20 may be omitted depending on circumstances and / or settings.

[0457] In the following description, a Network may be a single base station including multiple TRPs, and may be a single cell including multiple TRPs. For example, an ideal / non-ideal backhaul may be established between TRP 1 and TRP 2 constituting the Network. In addition, the following description is based on multiple TRPs, but this can be equally extended and applied to transmission through multiple panels. In addition, in the present disclosure, an operation of a UE receiving a signal from TRP1 / TRP2 can also be interpreted / described (or can be an operation) as an operation of the UE receiving a signal from the Network (via / using TRP1 / 2), and an operation of the UE transmitting a signal to TRP1 / TRP2 can also be interpreted / described (or can be an operation) as an operation of the UE transmitting a signal to the Network (via / using TRP1 / TRP2), and vice versa.

[0458] In addition, as described above, "TRP" can be applied by replacing it with expressions such as panel, antenna array, cell (e.g., macro cell / small cell / pico cell, etc.), transmission point (TP), base station (gNB, etc.). As described above, TRP can be distinguished according to information (e.g., index, identifier (ID)) about CORESET group (or CORESET pool). For example, when one UE is configured to perform transmission and reception with multiple TRPs (or cells), this may mean that multiple CORESET groups (or CORESET pools) are configured for one UE. Such configuration of CORESET group (or CORESET pool) can be performed through higher layer signaling (e.g., RRC signaling, etc.). In addition, a base station may be a general term for an object that performs data transmission and reception with a UE. For example, the base station may be a concept including one or more Transmission Points (TPs), one or more Transmission and Reception Points (TRPs), etc. In addition, the TPs and / or TRPs may include a panel of the base station, a transmission and reception unit, etc.

[0459] Referring to Figure 20, for convenience of explanation, signaling between one network (base station) and a UE is considered, but it is obvious that the signaling method can be extended and applied to signaling between multiple TRPs and multiple UEs.

[0460] Referring to FIG. 20, the network transmits configuration information related to channel state information (CSI) to the UE (S2001). That is, the UE receives configuration information related to channel state information (CSI) from the network.

[0461] The configuration information related to the above CSI may include at least one of CSI-IM (interference management) resource configuration information, CSI measurement configuration information, CSI resource configuration information, and CSI report configuration information.

[0462] Additionally, the configuration information related to the CSI (in particular, the configuration information related to the CSI report) may include information about the proposed method described above.

[0463] For example, the configuration information may include information about the DD (Doppler domain) (and / or TD (time domain)) basis vector length (i.e., the number of time instances for CSI calculation / derivation) (e.g., N4). In other words, the configuration information may include information related to the time instances for CSI calculation / derivation. For example, the codebook configuration information may be included in the CSI report configuration information, and the codebook configuration information may include information about the DD basis vector length (e.g., vectorLengthDD).

[0464] For example, the configuration information may include information regarding a threshold that the UE will use to determine channel prediction accuracy. Additionally, the configuration information may further include information regarding the time instance at which the UE must report channel prediction accuracy.

[0465] The network transmits CSI-RS to the UE on one or more (i.e., K, where K is a natural number) CSI-RS resources (S2002). That is, the UE receives CSI-RS from the network on one or more (i.e., K, where K is a natural number) CSI-RS resources.

[0466] Here, the UE can receive CSI-RS through one or more antenna ports on one or more CSI-RS resources based on the above configuration information.

[0467] The network receives channel state information (CSI) (feedback / report) from the UE (S2003). That is, the UE transmits channel state information (CSI) (feedback / report) to the network.

[0468] Here, channel state information (CSI) (feedback / report) may be transmitted via an uplink physical layer channel (e.g., PUCCH or PUSCH). In addition, the CSI may include at least one of PMI, CQI, RI, and LI.

[0469] The CSI reported by the UE to the network can be derived / generated based on the proposed method described above.

[0470] Here, the CSI may include PMIs corresponding to indices of a codebook for indicating precoding matrix(s). For example, the codebook may correspond to a codebook based on (linear combining) of SD basis vector(s), FD basis vector(s), and DD basis vector(s).

[0471] Additionally, the PMI may indicate precoding matrices for each of a plurality of time instances (or a plurality of (consecutive) slot intervals).

[0472] Here, the precoding matrices indicated by the PMI can be determined from a plurality of vectors (i.e., DD basis vectors). In addition, the precoding matrices indicated by the PMI can be determined from L vectors (i.e., SD basis vectors) + M υ can be determined from the vectors (i.e., FD basis vectors, layers l=1,...,υ) + Q vectors (i.e., DD basis vectors). In other words, the precoding matrix can be determined (via linear combination) from the SD basis vector, the FD basis vector, and the DD basis vector.

[0473] For example, the PMI may indicate precoding matrices for each of the remaining time instances, excluding one or more time instances, among a plurality of time instances set by the base station.

[0474] As described above, the precoding matrices indicated by the PMI can be determined from a plurality of vectors. Here, for example, i) each of the plurality of vectors (e.g., DD basis vectors) may have one or more elements corresponding to one or more time instances among the plurality of elements corresponding to the plurality of time instances (i.e., multiple time instances set by the base station) considered as 0; or ii) each of the plurality of vectors (e.g., DD basis vectors) may be generated by removing one or more elements among the plurality of elements (i.e., multiple elements corresponding to multiple time instances set by the base station) (e.g., reducing the length of the DD basis vector). For example, it is assumed that the base station sets the DD (Doppler domain) (and / or TD (time domain)) basis vector length (i.e., the number of time instances for CSI calculation / derivation) to 4 (e.g., N4). In this case, when the UE determines that the first and third elements (i.e., the first and third time instances) are excluded for PMI calculation / derivation, in the case i), the UE calculates [0 x 0 x] T (where x means the corresponding element value) a precoding matrix can be generated based on the vector, or in the case of ii), the UE can generate [xx] T (Here, x means the corresponding element value, excluding the first and third elements) A precoding matrix can be generated based on the vector.

[0475] In addition, the CSI may include information about one or more of the elements. In other words, it may include information about one or more elements excluded from the generation of the precoding matrix among a plurality of elements set by the base station. For example, the information about the one or more elements may correspond to a bitmap indicating the one or more elements (i.e., each bit of the bitmap corresponds one-to-one to each element) or may correspond to a minimum value of the one or more elements (i.e., all elements greater than or equal to the minimum value in ascending order of the indices of the plurality of elements are excluded).

[0476] Additionally, one or more channel quality indicators (CQIs) for the plurality of time instances can be calculated for remaining time instances excluding the one or more time instances among the plurality of time instances.

[0477] Additionally, one or more time instances may be excluded from the plurality of time instances based on whether the channel prediction accuracy is lower than a predetermined threshold or a threshold set by the base station. In other words, the UE may exclude time instances where the channel prediction accuracy is lower than the threshold set by the base station.

[0478] Additionally, the CSI may include information about the prediction accuracy for the channel for the one or more time instances (e.g., information about the value or level of accuracy) or information about the difference value from the threshold.

[0479] Additionally, the CSI may include information about a difference value from the threshold for one or more time instances set by the base station or selected by the UE. In other words, apart from time instances excluded from the generation of precoding matrices, the CSI may include information about a difference value from the threshold for one or more time instances set by the base station or selected by the UE.

[0480] Additionally, based on one or more elements of the plurality of vectors determining the precoding matrices indicated by the PMI being considered as 0 or one or more elements being removed, the number of the plurality of vectors may be determined to be smaller than when determining the precoding matrices for each of the plurality of time instances.

[0481] FIG. 21 is a diagram illustrating the operation of a UE for a method for transmitting and receiving channel state information according to one embodiment of the present disclosure.

[0482] FIG. 21 illustrates the operation of a UE based on the previously proposed method. The example in FIG. 21 is provided for convenience of explanation and does not limit the scope of the present disclosure. Some of the step(s) illustrated in FIG. 21 may be omitted depending on the situation and / or setting. Furthermore, the UE in FIG. 21 is merely an example and may be implemented as a device illustrated in FIG. 23 below. For example, the processor (102 / 202) in FIG. 23 may control the transceiver (106 / 206) to transmit and receive channels / signals / data / information, etc., and may also control the storage of transmitted or received channels / signals / data / information, etc., in the memory (104 / 204).

[0483] Referring to FIG. 21, the UE receives configuration information related to channel state information (CSI) from the base station (S2101).

[0484] The configuration information related to the above CSI may include at least one of CSI-IM (interference management) resource configuration information, CSI measurement configuration information, CSI resource configuration information, and CSI report configuration information.

[0485] Additionally, the configuration information related to the CSI (in particular, the configuration information related to the CSI report) may include information about the proposed method described above.

[0486] For example, the configuration information may include information about the DD (Doppler domain) (and / or TD (time domain)) basis vector length (i.e., the number of time instances for CSI calculation / derivation) (e.g., N4). In other words, the configuration information may include information related to the time instances for CSI calculation / derivation. For example, the codebook configuration information may be included in the CSI report configuration information, and the codebook configuration information may include information about the DD basis vector length (e.g., vectorLengthDD).

[0487] For example, the configuration information may include information regarding a threshold that the UE will use to determine channel prediction accuracy. Additionally, the configuration information may further include information regarding the time instance at which the UE must report channel prediction accuracy.

[0488] The UE receives a CSI-RS from the base station on one or more (i.e., K, where K is a natural number) CSI-RS resources (S2102).

[0489] Here, the UE can receive CSI-RS through one or more antenna ports on one or more CSI-RS resources based on the above configuration information.

[0490] The UE transmits channel state information (CSI) (feedback / report) to the base station (S2103).

[0491] Here, channel state information (CSI) (feedback / report) may be transmitted via an uplink physical layer channel (e.g., PUCCH or PUSCH). In addition, the CSI may include at least one of PMI, CQI, RI, and LI.

[0492] The CSI reported by the UE to the base station can be derived / generated based on the proposed method described above.

[0493] Here, the CSI may include PMIs corresponding to indices of a codebook for indicating precoding matrix(s). For example, the codebook may correspond to a codebook based on (linear combining) of SD basis vector(s), FD basis vector(s), and DD basis vector(s).

[0494] Additionally, the PMI may indicate precoding matrices for each of a plurality of time instances (or a plurality of (consecutive) slot intervals).

[0495] Here, the precoding matrices indicated by the PMI can be determined from a plurality of vectors (i.e., DD basis vectors). In addition, the precoding matrices indicated by the PMI can be determined from L vectors (i.e., SD basis vectors) + M υcan be determined from the vectors (i.e., FD basis vectors, layers l=1,...,υ) + Q vectors (i.e., DD basis vectors). In other words, the precoding matrix can be determined (via linear combination) from the SD basis vector, the FD basis vector, and the DD basis vector.

[0496] For example, the PMI may indicate precoding matrices for each of the remaining time instances, excluding one or more time instances, among a plurality of time instances set by the base station.

[0497] As described above, the precoding matrices indicated by the PMI can be determined from a plurality of vectors. Here, for example, i) each of the plurality of vectors (e.g., DD basis vectors) may have one or more elements corresponding to one or more time instances among the plurality of elements corresponding to the plurality of time instances (i.e., multiple time instances set by the base station) considered as 0; or ii) each of the plurality of vectors (e.g., DD basis vectors) may be generated by removing one or more elements among the plurality of elements (i.e., multiple elements corresponding to multiple time instances set by the base station) (e.g., reducing the length of the DD basis vector). For example, it is assumed that the base station sets the DD (Doppler domain) (and / or TD (time domain)) basis vector length (i.e., the number of time instances for CSI calculation / derivation) to 4 (e.g., N4). In this case, when the UE determines that the first and third elements (i.e., the first and third time instances) are excluded for PMI calculation / derivation, in the case i), the UE calculates [0 x 0 x] T(where x means the corresponding element value) a precoding matrix can be generated based on the vector, or in the case of ii), the UE can generate [xx] T (Here, x means the corresponding element value, excluding the first and third elements) A precoding matrix can be generated based on the vector.

[0498] In addition, the CSI may include information about one or more of the elements. In other words, it may include information about one or more elements excluded from the generation of the precoding matrix among a plurality of elements set by the base station. For example, the information about the one or more elements may correspond to a bitmap indicating the one or more elements (i.e., each bit of the bitmap corresponds one-to-one to each element) or may correspond to a minimum value of the one or more elements (i.e., all elements greater than or equal to the minimum value in ascending order of the indices of the plurality of elements are excluded).

[0499] Additionally, one or more channel quality indicators (CQIs) for the plurality of time instances can be calculated for remaining time instances excluding the one or more time instances among the plurality of time instances.

[0500] Additionally, one or more time instances may be excluded from the plurality of time instances based on whether the channel prediction accuracy is lower than a predetermined threshold or a threshold set by the base station. In other words, the UE may exclude time instances where the channel prediction accuracy is lower than the threshold set by the base station.

[0501] Additionally, the CSI may include information about the prediction accuracy for the channel for the one or more time instances (e.g., information about the value or level of accuracy) or information about the difference value from the threshold.

[0502] Additionally, the CSI may include information about a difference value from the threshold for one or more time instances set by the base station or selected by the UE. In other words, apart from time instances excluded from the generation of precoding matrices, the CSI may include information about a difference value from the threshold for one or more time instances set by the base station or selected by the UE.

[0503] Additionally, based on one or more elements of the plurality of vectors determining the precoding matrices indicated by the PMI being considered as 0 or one or more elements being removed, the number of the plurality of vectors may be determined to be smaller than when determining the precoding matrices for each of the plurality of time instances.

[0504] FIG. 22 is a diagram illustrating the operation of a base station for a method for transmitting and receiving channel state information according to one embodiment of the present disclosure.

[0505] FIG. 22 illustrates the operation of a base station based on the previously proposed method. The example in FIG. 22 is provided for convenience of explanation and does not limit the scope of the present disclosure. Some of the step(s) illustrated in FIG. 22 may be omitted depending on circumstances and / or settings. Furthermore, the base station in FIG. 22 is merely an example and may be implemented as a device illustrated in FIG. 23 below. For example, the processor (102 / 202) in FIG. 23 may control the transceiver (106 / 206) to transmit and receive channels / signals / data / information, etc., and may also control the storage of transmitted or received channels / signals / data / information, etc., in the memory (104 / 204).

[0506] Referring to FIG. 22, the base station transmits configuration information related to channel state information (CSI) to the UE (S2201).

[0507] The configuration information related to the above CSI may include at least one of CSI-IM (interference management) resource configuration information, CSI measurement configuration information, CSI resource configuration information, and CSI report configuration information.

[0508] Additionally, the configuration information related to the CSI (in particular, the configuration information related to the CSI report) may include information about the proposed method described above.

[0509] For example, the configuration information may include information about the DD (Doppler domain) (and / or TD (time domain)) basis vector length (i.e., the number of time instances for CSI calculation / derivation) (e.g., N4). In other words, the configuration information may include information related to the time instances for CSI calculation / derivation. For example, the codebook configuration information may be included in the CSI report configuration information, and the codebook configuration information may include information about the DD basis vector length (e.g., vectorLengthDD).

[0510] For example, the configuration information may include information regarding a threshold that the UE will use to determine channel prediction accuracy. Additionally, the configuration information may further include information regarding the time instance at which the UE must report channel prediction accuracy.

[0511] The base station transmits CSI-RS to the UE on one or more (i.e., K, where K is a natural number) CSI-RS resources (S2202).

[0512] Here, the UE can receive CSI-RS through one or more antenna ports on one or more CSI-RS resources based on the above configuration information.

[0513] The base station receives channel state information (CSI) (feedback / report) from the UE (S2203).

[0514] Here, channel state information (CSI) (feedback / report) may be transmitted via an uplink physical layer channel (e.g., PUCCH or PUSCH). In addition, the CSI may include at least one of PMI, CQI, RI, and LI.

[0515] The CSI received from the UE can be derived / generated based on the proposed method described above.

[0516] Here, the CSI may include PMIs corresponding to indices of a codebook for indicating precoding matrix(s). For example, the codebook may correspond to a codebook based on (linear combining) of SD basis vector(s), FD basis vector(s), and DD basis vector(s).

[0517] Additionally, the PMI may indicate precoding matrices for each of a plurality of time instances (or a plurality of (consecutive) slot intervals).

[0518] Here, the precoding matrices indicated by the PMI can be determined from a plurality of vectors (i.e., DD basis vectors). In addition, the precoding matrices indicated by the PMI can be determined from L vectors (i.e., SD basis vectors) + M υ can be determined from the vectors (i.e., FD basis vectors, layers l=1,...,υ) + Q vectors (i.e., DD basis vectors). In other words, the precoding matrix can be determined (via linear combination) from the SD basis vector, the FD basis vector, and the DD basis vector.

[0519] For example, the PMI may indicate precoding matrices for each of the remaining time instances, excluding one or more time instances, among a plurality of time instances set by the base station.

[0520] As described above, the precoding matrices indicated by the PMI can be determined from a plurality of vectors. Here, for example, i) each of the plurality of vectors (e.g., DD basis vectors) may have one or more elements corresponding to one or more time instances among the plurality of elements corresponding to the plurality of time instances (i.e., multiple time instances set by the base station) considered as 0; or ii) each of the plurality of vectors (e.g., DD basis vectors) may be generated by removing one or more elements among the plurality of elements (i.e., multiple elements corresponding to multiple time instances set by the base station) (e.g., reducing the length of the DD basis vector). For example, it is assumed that the base station sets the DD (Doppler domain) (and / or TD (time domain)) basis vector length (i.e., the number of time instances for CSI calculation / derivation) to 4 (e.g., N4). In this case, when the UE determines that the first and third elements (i.e., the first and third time instances) are excluded for PMI calculation / derivation, in the case i), the UE calculates [0 x 0 x] T (where x means the corresponding element value) a precoding matrix can be generated based on the vector, or in the case of ii), the UE can generate [xx] T (Here, x means the corresponding element value, excluding the first and third elements) A precoding matrix can be generated based on the vector.

[0521] In addition, the CSI may include information about one or more of the elements. In other words, it may include information about one or more elements excluded from the generation of the precoding matrix among a plurality of elements set by the base station. For example, the information about the one or more elements may correspond to a bitmap indicating the one or more elements (i.e., each bit of the bitmap corresponds one-to-one to each element) or may correspond to a minimum value of the one or more elements (i.e., all elements greater than or equal to the minimum value in ascending order of the indices of the plurality of elements are excluded).

[0522] Additionally, one or more channel quality indicators (CQIs) for the plurality of time instances may be calculated for time instances remaining among the plurality of time instances excluding the one or more time instances.

[0523] Additionally, one or more time instances may be excluded from the plurality of time instances based on whether the channel prediction accuracy is lower than a predetermined threshold or a threshold set by the base station. In other words, the UE may exclude time instances where the channel prediction accuracy is lower than the threshold set by the base station.

[0524] Additionally, the CSI may include information about the prediction accuracy for the channel for the one or more time instances (e.g., information about the value or level of accuracy) or information about the difference value from the threshold.

[0525] Additionally, the CSI may include information about a difference value from the threshold for one or more time instances set by the base station or selected by the UE. In other words, apart from time instances excluded from the generation of precoding matrices, the CSI may include information about a difference value from the threshold for one or more time instances set by the base station or selected by the UE.

[0526] Additionally, based on one or more elements of the plurality of vectors determining the precoding matrices indicated by the PMI being considered as 0 or one or more elements being removed, the number of the plurality of vectors may be determined to be smaller than when determining the precoding matrices for each of the plurality of time instances.

[0527] General devices to which the present disclosure may be applied

[0528] FIG. 23 is a block diagram illustrating a wireless communication device according to one embodiment of the present disclosure.

[0529] Referring to FIG. 23, the first wireless device (100) and the second wireless device (200) can transmit and receive wireless signals through various wireless access technologies (e.g., LTE, NR).

[0530] A first wireless device (100) includes one or more processors (102) and one or more memories (104), and may further include one or more transceivers (106) and / or one or more antennas (108). The processor (102) controls the memories (104) and / or the transceivers (106), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in the present disclosure. For example, the processor (102) may process information in the memory (104) to generate first information / signal, and then transmit a wireless signal including the first information / signal via the transceiver (106). In addition, the processor (102) may receive a wireless signal including second information / signal via the transceiver (106), and then store information obtained from signal processing of the second information / signal in the memory (104). The memory (104) may be connected to the processor (102) and may store various information related to the operation of the processor (102). For example, the memory (104) may perform some or all of the processes controlled by the processor (102), or may store software code including commands for performing the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in the present disclosure. Here, the processor (102) and the memory (104) may be part of a communication modem / circuit / chip designed to implement a wireless communication technology (e.g., LTE, NR). The transceiver (106) may be connected to the processor (102) and may transmit and / or receive wireless signals via one or more antennas (108). The transceiver (106) may include a transmitter and / or a receiver. The transceiver (106) may be used interchangeably with an RF (Radio Frequency) unit. In the present disclosure, a wireless device may also mean a communication modem / circuit / chip.

[0531] A second wireless device (200) includes one or more processors (202), one or more memories (204), and may further include one or more transceivers (206) and / or one or more antennas (208). The processor (202) controls the memories (204) and / or the transceivers (206), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in the present disclosure. For example, the processor (202) may process information in the memory (204) to generate third information / signals, and then transmit a wireless signal including the third information / signals via the transceivers (206). Furthermore, the processor (202) may receive a wireless signal including fourth information / signals via the transceivers (206), and then store information obtained from signal processing of the fourth information / signals in the memory (204). The memory (204) may be connected to the processor (202) and may store various information related to the operation of the processor (202). For example, the memory (204) may perform some or all of the processes controlled by the processor (202), or may store software code including commands for performing the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in the present disclosure. Here, the processor (202) and the memory (204) may be part of a communication modem / circuit / chip designed to implement a wireless communication technology (e.g., LTE, NR). The transceiver (206) may be connected to the processor (202) and may transmit and / or receive wireless signals via one or more antennas (208). The transceiver (206) may include a transmitter and / or a receiver. The transceiver (206) may be used interchangeably with an RF unit. In the present disclosure, a wireless device may also mean a communication modem / circuit / chip.

[0532] Hereinafter, hardware elements of the wireless device (100, 200) will be described in more detail. Although not limited thereto, one or more protocol layers may be implemented by one or more processors (102, 202). For example, one or more processors (102, 202) may implement one or more layers (e.g., functional layers such as PHY, MAC, RLC, PDCP, RRC, SDAP). One or more processors (102, 202) may generate one or more Protocol Data Units (PDUs) and / or one or more Service Data Units (SDUs) according to the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in the present disclosure. One or more processors (102, 202) may generate messages, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in the present disclosure. One or more processors (102, 202) can generate signals (e.g., baseband signals) including PDUs, SDUs, messages, control information, data or information according to the functions, procedures, proposals and / or methods disclosed in the present disclosure, and provide the signals to one or more transceivers (106, 206). One or more processors (102, 202) can receive signals (e.g., baseband signals) from one or more transceivers (106, 206) and obtain 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.

[0533] One or more processors (102, 202) may be referred to as a controller, a microcontroller, a microprocessor, or a microcomputer. One or more processors (102, 202) may be implemented by hardware, firmware, software, or a combination thereof. For example, one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), one or more Digital Signal Processing Devices (DSPDs), one or more Programmable Logic Devices (PLDs), or one or more Field Programmable Gate Arrays (FPGAs) may be included in one or more processors (102, 202). The descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this disclosure may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, etc. The descriptions, functions, procedures, proposals, methods and / or operation flowcharts disclosed in this disclosure may be implemented using firmware or software configured to perform one or more processors (102, 202) or stored in one or more memories (104, 204) and driven by one or more processors (102, 202). The descriptions, functions, procedures, proposals, methods and / or operation flowcharts disclosed in this disclosure may be implemented using firmware or software in the form of codes, instructions and / or sets of instructions.

[0534] One or more memories (104, 204) may be coupled to one or more processors (102, 202) and may store various forms of data, signals, messages, information, programs, codes, instructions, and / or commands. The one or more memories (104, 204) may be configured as ROM, RAM, EPROM, flash memory, hard drives, registers, cache memory, computer-readable storage media, and / or combinations thereof. The one or more memories (104, 204) may be located internally and / or externally to the one or more processors (102, 202). Additionally, the one or more memories (104, 204) may be coupled to the one or more processors (102, 202) via various technologies, such as wired or wireless connections.

[0535] One or more transceivers (106, 206) can transmit user data, control information, wireless signals / channels, etc., as mentioned in the methods and / or flowcharts of the present disclosure, to one or more other devices. One or more transceivers (106, 206) can receive user data, control information, wireless signals / channels, etc., as mentioned in the descriptions, functions, procedures, proposals, methods and / or flowcharts of the present disclosure, from one or more other devices. For example, one or more transceivers (106, 206) can be connected to one or more processors (102, 202) and can transmit and receive wireless signals. For example, one or more processors (102, 202) can control one or more transceivers (106, 206) to transmit user data, control information, or wireless signals to one or more other devices. Additionally, one or more processors (102, 202) may control one or more transceivers (106, 206) to receive user data, control information, or wireless signals from one or more other devices. Additionally, one or more transceivers (106, 206) may be coupled to one or more antennas (108, 208), and one or more transceivers (106, 206) may be configured to transmit and receive user data, control information, wireless signals / channels, or the like, as referred to in the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in the present disclosure, via one or more antennas (108, 208). In the present disclosure, one or more antennas may be multiple physical antennas or multiple logical antennas (e.g., antenna ports). One or more transceivers (106, 206) can convert received user data, control information, wireless signals / channels, etc. from RF band signals to baseband signals in order to process the received user data, control information, wireless signals / channels, etc. using one or more processors (102, 202).One or more transceivers (106, 206) may convert user data, control information, wireless signals / channels, etc. processed by one or more processors (102, 202) from baseband signals to RF band signals. For this purpose, one or more transceivers (106, 206) may include an (analog) oscillator and / or filter.

[0536] The embodiments described above are combinations of components and features of the present disclosure in a predetermined form. Each component or feature should be considered optional unless explicitly stated otherwise. Each component or feature may be implemented without being combined with other components or features. Furthermore, it is also possible to form embodiments of the present disclosure by combining some components and / or features. The order of operations described in the embodiments of the present disclosure may be changed. Some components or features of one embodiment may be included in another embodiment or may be replaced with corresponding components or features of another embodiment. It is self-evident that claims that do not have an explicit citation relationship in the patent claims may be combined to form embodiments or incorporated as new claims through post-application amendments.

[0537] It will be apparent to those skilled in the art that the present disclosure may be embodied in other specific forms without departing from the essential characteristics thereof. Therefore, the above detailed description should not be construed as limiting in any respect, but rather as illustrative. The scope of the present disclosure should be determined by a reasonable interpretation of the appended claims, and all modifications within the scope of equivalents of the present disclosure are intended to be included within the scope of the present disclosure.

[0538] The scope of the present disclosure includes software or machine-executable instructions (e.g., an operating system, an application, firmware, a program, etc.) that cause operations according to the methods of various embodiments to be executed on a device or a computer, and a non-transitory computer-readable medium having such software or instructions stored thereon and executable on the device or computer. Instructions that can be used to program a processing system to perform the features described in the present disclosure can be stored on / in a storage medium or a computer-readable storage medium, and a computer program product including such a storage medium can be used to implement the features described in the present disclosure. The storage medium can include, but is not limited to, high-speed random access memory, such as DRAM, SRAM, DDR RAM, or other random access solid state memory devices, and can include 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 remotely located from the processor(s). The memory or, alternatively, the non-volatile memory device(s) within the memory comprise a non-transitory computer-readable storage medium. The features described in this disclosure may be incorporated into software and / or firmware stored on any of the machine-readable media, which may control the hardware of the processing system and allow the processing system to interact with other mechanisms that utilize results according to embodiments of the present disclosure. Such software or firmware may include, but is not limited to, application code, device drivers, operating systems, and execution environments / containers.

[0539] Here, the wireless communication technology implemented in the wireless device (100, 200) of the present disclosure may include not only LTE, NR, and 6G, but also Narrowband Internet of Things for low-power communication. At this time, for example, NB-IoT technology may be an example of LPWAN (Low Power Wide Area Network) technology, and may be implemented with standards such as LTE Cat NB1 and / or LTE Cat NB2, and is not limited to the above-described names. Additionally or alternatively, the wireless communication technology implemented in the wireless device (100, 200) of the present disclosure may perform communication based on LTE-M technology. At this time, for example, LTE-M technology may be an example of LPWAN technology, and may be called by various names such as eMTC (enhanced Machine Type Communication). For example, LTE-M technology can be implemented by at least one of various standards such as 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, and is not limited to the above-described names. Additionally or alternatively, the wireless communication technology implemented in the wireless device (100, 200) of the present disclosure can include at least one of ZigBee, Bluetooth, and Low Power Wide Area Network (LPWAN) considering low-power communication, and is not limited to the above-described names. For example, ZigBee technology can create personal area networks (PAN) related to small / low-power digital communication based on various standards such as IEEE 802.15.4, and can be called by various names.

[0540] The method proposed in this disclosure is explained with a focus on examples applied to 3GPP LTE / LTE-A and 5G systems, but can be applied to various wireless communication systems in addition to 3GPP LTE / LTE-A and 5G systems.

Claims

1. A method performed by a user equipment (UE) in a wireless communication system, the method comprising: A step of receiving configuration information related to channel state information (CSI) from a base station, wherein the configuration information includes information related to a plurality of time instances; A step of receiving a CSI-RS on K CSI-reference signal (CSI-RS) resources from the base station (K is an integer greater than 0); and A step of transmitting the CSI to the base station, wherein the CSI includes a precoding matrix indicator (PMI) corresponding to codebook indices, A method wherein the PMI indicates precoding matrices for each of the remaining time instances excluding one or more time instances among the plurality of time instances.

2. In paragraph 1, The precoding matrices indicated by the above PMI are determined from multiple vectors, i) a method wherein each of the plurality of vectors is generated by considering one or more elements corresponding to one or more time instances as 0 among the plurality of elements corresponding to the plurality of time instances, or ii) removing one or more elements from among the plurality of elements.

3. In paragraph 2, A method wherein the CSI includes information about one or more of the excluded elements.

4. In paragraph 2, A method wherein the information about the one or more elements corresponds to a bitmap indicating the one or more elements or corresponds to a minimum value of the one or more elements.

5. In paragraph 1, A method wherein one or more channel quality indicators (CQIs) for the plurality of time instances are calculated for time instances remaining among the plurality of time instances excluding the one or more time instances.

6. In paragraph 1, A method wherein one or more time instances are excluded from the plurality of time instances based on a prediction accuracy for the channel being lower than a threshold determined in advance or set by the base station.

7. In paragraph 6, A method wherein the CSI comprises information about the prediction accuracy for the channel for the one or more time instances or information about the difference value from the threshold.

8. In paragraph 6, A method wherein the CSI includes information about a difference value from the threshold for one or more time instances set by the base station or selected by the UE.

9. In paragraph 2, A method wherein the number of the plurality of vectors is determined to be smaller than when determining the precoding matrices for each of the plurality of time instances based on one or more of the elements being considered as 0 or removed.

10. In a user equipment (UE) operating in a wireless communication system, the UE: One or more transceivers for transmitting and receiving wireless signals; and comprising one or more processors controlling one or more of the above transceivers, One or more of the above processors: Receive configuration information related to channel state information (CSI) from a base station, wherein the configuration information includes information related to multiple time instances, Receive a CSI-RS on K CSI-reference signal (CSI-RS) resources from the base station (K is an integer greater than 0), and The CSI is transmitted to the base station, and the CSI is set to include a precoding matrix indicator (PMI) corresponding to codebook indices, The above PMI indicates precoding matrices for each of the remaining time instances, excluding one or more time instances among the plurality of time instances.

11. One or more non-transitory computer-readable media storing one or more instructions, The one or more commands are executed by one or more processors so that a user equipment (UE): Receive configuration information related to channel state information (CSI) from a base station, wherein the configuration information includes information related to multiple time instances, Receive a CSI-RS on K CSI-reference signal (CSI-RS) resources from the base station (K is an integer greater than 0), and Controlling the above CSI to be transmitted to the base station, wherein the CSI includes a precoding matrix indicator (PMI) corresponding to codebook indices, A computer-readable medium in which the PMI indicates precoding matrices for each of the remaining time instances excluding one or more time instances among the plurality of time instances.

12. In a processing device configured to control a user equipment (UE) in a wireless communication system, the processing device: one or more processors; and One or more computer memories operatively connected to said one or more processors and storing instructions that perform operations based on being executed by said one or more processors, The above actions are: A step of receiving configuration information related to channel state information (CSI) from a base station, wherein the configuration information includes information related to a plurality of time instances; A step of receiving a CSI-RS on K CSI-reference signal (CSI-RS) resources from the base station (K is an integer greater than 0); and A step of transmitting the CSI to the base station, wherein the CSI includes a precoding matrix indicator (PMI) corresponding to codebook indices, A processing device in which the PMI indicates precoding matrices for each of the remaining time instances excluding one or more time instances among the plurality of time instances.

13. A method performed by a base station in a wireless communication system, the method comprising: A step of transmitting configuration information related to channel state information (CSI) to a user equipment (UE), wherein the configuration information includes information related to a plurality of time instances; A step of transmitting a CSI-RS on K CSI-reference signal (CSI-RS) resources to the UE (K is an integer greater than 0); and A step of receiving the CSI from the UE, wherein the CSI includes a precoding matrix indicator (PMI) corresponding to codebook indices, A method wherein the PMI indicates precoding matrices for each of the remaining time instances excluding one or more time instances among the plurality of time instances.

14. In a base station operating in a wireless communication system, the base station: One or more transceivers for transmitting and receiving wireless signals; and comprising one or more processors controlling one or more of the above transceivers, One or more of the above processors: Transmitting configuration information related to channel state information (CSI) to a user equipment (UE), wherein the configuration information includes information related to multiple time instances, Transmitting CSI-RS on K CSI-reference signal (CSI-RS) resources to the UE (K is an integer greater than 0), and Receive the CSI from the UE, wherein the CSI is set to include a precoding matrix indicator (PMI) corresponding to codebook indices, A base station, wherein the PMI indicates precoding matrices for each of the remaining time instances, excluding one or more time instances among the plurality of time instances.

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