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

Abstract

A method and apparatus for transmitting / receiving channel state information (CSI) in a wireless communication system are disclosed. According to one embodiment of the present disclosure, the method may comprise the steps of: receiving configuration information related to CSI from a base station; receiving, from the base station, CSI-RS on the one or more CSI resources; and transmitting the CSI to the base station based on the configuration information.

Description

Technical Field

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

[0002] A mobile communication system has been developed to provide voice services while ensuring user mobility. However, the mobile communication system has been extended to data services as well as voice services, and currently, the explosive growth of services has led to resource shortages, and users have demanded faster services, thus requiring more advanced mobile communication systems.

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

[0004] Technical issues

[0005] The technical purpose of the present disclosure is to provide a method and device for sending and receiving CSI.

[0006] In addition, an additional technical objective of the present disclosure is to provide a method and apparatus for sending and receiving CSI based on artificial intelligence / machine learning (AI / ML) operations.

[0007] The technical objectives achieved by the present disclosure are not limited to the above-mentioned technical objectives, and other technical objectives not described herein will be clearly understood by those skilled in the relevant art from the following description.

[0008] Technical Solution

[0009] 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; receiving a CSI reference signal (CSI-RS) on one or more CSI resources from the base station; and sending CSI to the base station based on the configuration information. The CSI may include information for a CSI reporting mode assumed by the UE to calculate the CSI.

[0010] According to an additional aspect of the present disclosure, a method performed by a base station in a wireless communication system may include: sending configuration information related to channel state information (CSI) to a user equipment (UE); sending a CSI reference signal (CSI-RS) on one or more CSI resources to the UE; and receiving CSI from the UE. The CSI may include information for a CSI reporting mode assumed by the UE to calculate the CSI.

[0011] Technical Effects

[0012] According to an embodiment of the present disclosure, transmission and reception of CSI feedback reflecting dynamic channel conditions is possible.

[0013] In addition, according to the embodiments of the present disclosure, since dynamic channel conditions are reflected in CSI feedback, downlink transmission performance can be improved.

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

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

[0016] Figure 1 The diagram shows a structure of a wireless communication system to which the present disclosure can be applied.

[0017] Figure 2 The figure illustrates a frame structure in a wireless communication system to which the present disclosure can be applied.

[0018] Figure 3 The diagram illustrates a resource grid in a wireless communication system to which the present disclosure may be applied.

[0019] Figure 4 The figure illustrates physical resource blocks in a wireless communication system to which the present disclosure can be applied.

[0020] Figure 5 The diagram illustrates a time slot structure in a wireless communication system to which the present disclosure can be applied.

[0021] Figure 6 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 physical channels are illustrated.

[0022] Figure 7 Illustration of the classification of artificial intelligence.

[0023] Figure 8 Diagram of a feedforward neural network.

[0024] Fig. 9 Diagram of a recurrent neural network.

[0025] Fig.10 Diagram of a convolutional neural network.

[0026] Fig.11 Shows the autoencoder.

[0027] Fig.12 Diagram of the functional framework for AI operations.

[0028] Fig.13 It is a diagram illustrating segmentation AI inference.

[0029] Fig.14 The diagram illustrates the application of the functional framework in a wireless communication system.

[0030] Fig.15 The diagram illustrates the application of the functional framework in a wireless communication system.

[0031] Fig.16 The diagram illustrates the application of the functional framework in a wireless communication system.

[0032] Fig.17 is a diagram illustrating a signaling process between a network and a UE for a method for transmitting and receiving channel state information according to an embodiment of the present disclosure.

[0033] Fig.18 is a diagram illustrating an operation of a UE for a method of transmitting and receiving channel state information according to an embodiment of the present disclosure.

[0034] Fig.19 is a diagram illustrating an operation of a base station for a method of transmitting and receiving channel state information according to an embodiment of the present disclosure.

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

[0036] Hereinafter, embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. The detailed description to be disclosed by the accompanying drawings is intended to describe exemplary embodiments of the present disclosure, rather than to represent the only embodiment in which the present disclosure can be implemented. The following detailed description includes specific details to provide a complete understanding of the present disclosure. However, those skilled in the relevant art will appreciate that the present disclosure can be implemented without these specific details.

[0037] In some cases, well-known structures and devices may be omitted, or may be shown in the form of a block diagram based on the core functions of each structure and device in order to prevent ambiguity in the concepts of the present disclosure.

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

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

[0040] The terms used in this disclosure are intended to describe specific embodiments rather than to limit the claims. As used in the description of the embodiments and the appended claims, the singular form is intended to include the plural form unless the context clearly indicates otherwise. The term "and / or" used in this disclosure may refer to one of the related enumerated items, or mean that it refers to and includes any and all possible combinations of two or more of them. In addition, unless otherwise stated, the " / " between words in the present invention has the same meaning as "and / or".

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

[0042] In the present disclosure, sending or receiving a channel includes the meaning of sending or receiving information or signals through the corresponding channel. For example, sending a control channel means sending control information or control signals through the control channel. Similarly, sending a data channel means sending data information or data signals through the data channel.

[0043] Hereinafter, downlink (DL) means communication from a base station to a terminal, and uplink (UL) means communication from a terminal to a base station. In the downlink, the transmitter may be a part of a base station, and the receiver may be a part of a terminal. In the uplink, the transmitter may be a part of a terminal, and the receiver may be a part of a base station. The base station may be expressed as a first communication device, and the terminal may be expressed as a second communication device. The base station (BS) may be replaced by terms such as a fixed station, a node B, an eNB (evolved node B), a gNB (next generation node B), a BTS (base station transceiver system), an access point (AP), a network (5G network), an AI (artificial intelligence) system / module, an RSU (roadside unit), a robot, a drone (UAV: unmanned aerial vehicle), an AR (augmented reality) device, a VR (virtual reality) device, and the like. In addition, the terminal may be fixed or mobile, and may be replaced by 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 (roadside unit), robot, AI (artificial intelligence) module, drone (UAV: unmanned aerial vehicle), AR (augmented reality) device, VR (virtual reality) device, etc.

[0044] The following description may be used for various radio access systems such as CDMA, FDMA, TDMA, OFDMA, SC-FDMA, etc. CDMA may be implemented by, for example, UTRA (Universal Terrestrial Radio Access) or CDMA2000. TDMA may be implemented by radio technologies such as GSM (Global System for Mobile Communications) / GPRS (General Packet Radio Service) / EDGE (Enhanced Data Rates for GSM Evolution). OFDMA may be implemented by radio technologies such as IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE802-20, E-UTRA (Evolved UTRA), etc. UTRA is part of UMTS (Universal Mobile Telecommunications System). 3GPP (3rd Generation Partnership Project) LTE (Long Term Evolution) is part of E-UMTS (Evolved UMTS) using E-UTRA, and LTE-A (Advanced) / LTE-A pro is an advanced version of 3GPP LTE. 3GPP NR (New Radio or New Radio Access Technology) is an advanced version of 3GPP LTE / LTE-A / LTE-A pro.

[0045] In order to make the description clearer, 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 to this. LTE means the technology after 3GPP TS (Technical Specification) 36.xxx version 8. Specifically, the LTE technology in or after 3GPP TS 36.xxx version 10 is called LTE-A, and the LTE technology in or after 3GPP TS 36.xxx version 13 is called LTE-A pro. 3GPP NR means the technology in or after TS 38.xxx version 15. LTE / NR can be referred to as a 3GPP system. "xxx" means the detailed number of the standard document. LTE / NR can generally be referred to as a 3GPP system. For the background technology, terms, abbreviations, etc. used to describe the present disclosure, reference can be made to the matters described in the standard documents disclosed before the present disclosure. For example, reference can be made to the following documents.

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

[0047] For 3GPP NR, you can refer to 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 (NR and NG-RAN (Next Generation Radio Access Network) overall description), and TS 38.331 (Radio Resource Control Protocol Specification).

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

[0049] -BM: Beam Management

[0050] -CQI: Channel Quality Indicator

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

[0052] -CSI: Channel State Information

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

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

[0055] -DMRS: Demodulation Reference Signal

[0056] -FDM: Frequency Division Multiplexing

[0057] - FFT: Fast Fourier Transform

[0058] -IFDMA: Interleaved Frequency Division Multiple Access

[0059] -IFFT: Inverse Fast Fourier Transform

[0060] -L1-RSRP: Layer 1 reference signal received power

[0061] -L1-RSRQ: Layer 1 reference signal reception quality

[0062] -MAC: Media Access Control

[0063] -NZP: Non-Zero Power

[0064] - OFDM: Orthogonal Frequency Division Multiplexing

[0065] –PDCCH: Physical Downlink Control Channel

[0066] -PDSCH: Physical Downlink Shared Channel

[0067] -PMI: Precompiled Matrix Indicator

[0068] -RE: Resource Element

[0069] -RI: Rank Indicator

[0070] -RRC: Radio Resource Control

[0071] -RSSI: Received Signal Strength Indicator

[0072] - Rx: Receive

[0073] - QCL: Quasi Co-sited

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

[0075] -SSB (or SS / PBCH block): Synchronization signal block (including PSS (primary synchronization signal), SSS (secondary synchronization signal) and PBCH (physical broadcast channel))

[0076] -TDM: Time Division Multiplexing

[0077] -TRP: Transmit and Receive Point

[0078] -TRS: Tracking Reference Signal

[0079] -Tx: Send

[0080] -UE: User Equipment

[0081] -ZP: Zero Power

[0082] Overall system

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

[0084] The new RAT system including NR uses an OFDM transmission method or a transmission method similar thereto. The new RAT system may follow OFDM parameters different from those of LTE. Alternatively, the new RAT system follows the parameters of the existing LTE / LTE-A as is, but may support a wider system bandwidth (e.g., 100 MHz). Alternatively, one cell may support multiple parameter sets. In other words, terminals operating according to different parameter sets may coexist in one cell.

[0085] A parameter set corresponds to one subcarrier spacing in the frequency domain. As the reference subcarrier spacing is scaled by an integer N, different parameter sets can be defined.

[0086] Figure 1 The diagram illustrates a structure of a wireless communication system to which the present disclosure can be applied.

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

[0088] Figure 2 The diagram illustrates a frame structure in a wireless communication system to which the present disclosure may be applied.

[0089] The NR system may support multiple parameter sets. Here, the parameter set may be defined by subcarrier spacing and cyclic prefix (CP) overhead. Here, multiple subcarrier spacings may be derived by scaling the basic (reference) subcarrier spacing by an integer N (or, μ). In addition, although it is assumed that very low subcarrier spacings are not used in very high carrier frequencies, the parameter set used may be selected independently of the frequency band. In addition, various frame structures according to multiple parameter sets may be supported in the NR system.

[0090] In the following, OFDM parameter sets and frame structures that can be considered in the NR system are described. Multiple OFDM parameter sets supported in the NR system can be defined as shown in Table 1 below.

[0091] [Table 1]

[0092]

[0093] NR supports multiple parameter sets (or subcarrier spacing (SCS)) for supporting various 5G services. For example, when the SCS is 15kHz, it supports wide areas of traditional cellular bands; and when the SCS is 30kHz / 60kHz, it supports dense cities, lower latency and wider carrier bandwidth; and when the SCS is 60kHz or higher, it supports bandwidths exceeding 24.25GHz to overcome phase noise.

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

[0095] [Table 2]

[0096]

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

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

[0099] [Table 3]

[0100]

[0101] [Table 4]

[0102]

[0103] Figure 2 This is an example of μ=2 (SCS is 60kHz), see Table 3, 1 subframe can include 4 time slots. Figure 21 subframe = {1, 2, 4} shown in is an example, and the number of slots that can be included in 1 subframe is defined as in Table 3 or Table 4. In addition, a mini-slot may include 2, 4, or 7 symbols or more or less symbols.

[0104] Regarding the physical resources in the NR system, antenna ports, resource grids, resource elements, resource blocks, carrier parts, etc. can be considered. Hereinafter, the physical resources that can be considered in the NR system will be described in detail.

[0105] First, regarding antenna ports, antenna ports are defined so that the channel carrying symbols in the antenna port can be inferred from the channel carrying other symbols in the same antenna port. When the large-scale properties of the channel on which the symbols in one antenna port are carried can be inferred from the channel carrying the symbols of another antenna port, the two antenna ports can be said to be in a QC / QCL (quasi-co-located or quasi-co-located) relationship. In this case, the large-scale properties include at least one of delay spread, Doppler spread, frequency shift, average received power, and receive timing.

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

[0107] refer to Figure 3 , which graphically depicts a resource grid configuration with N in the frequency domain RB μ N sc RB subcarriers, and one subframe is configured with 14·2 μ OFDM symbols, but not limited to this. In the NR system, the transmitted signal consists of 2 μ N symb （μ） OFDM symbols and N RB μ N sc RB Here, N RB μ ≤ N RB max,μ . N RB max,μ represents the maximum transmission bandwidth, which may differ between uplink and downlink and between parameter sets. In this case, one resource grid can be configured for each μ and antenna port p. Each element of the resource grid for μ and antenna port p is called a resource element and is uniquely identified by an index pair (k, l'). Here, k = 0, ..., N RB μ N sc RB-1 is the index in the frequency domain, and l'=0,...,2 μ N symb （μ） -1 refers to the symbol position in the subframe. When referencing resource elements 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 corresponds to the complex value a k,l' （p,μ） When there is no risk of confusion or when no specific antenna port or parameter set is specified, the indices p and μ may be dropped and the complex value may be a k,l' （p） or a k,l' In addition, a resource block (RB) is defined as N sc RB =12 consecutive subcarriers.

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

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

[0110] -absoluteFrequencyPointA represents the frequency position of point A, expressed in ARFCN (Absolute Radio Frequency Channel Number).

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

[0112] [Formula 1]

[0113]

[0114] In Equation 1, k is defined relative to point A, so that k = 0 corresponds to a subcarrier centered at point A. Physical resource blocks are numbered from 0 to N in a bandwidth part (BWP). BWP,i size,μ -1 numbering and i is the number of the BWP. Physical resource block n in BWP iPRB and public resource block n CRB The relationship between is given by the following formula 2.

[0115] [Formula 2]

[0116]

[0117] N BWP,i start,μ is the common resource block where the BWP starts relative to common resource block 0.

[0118] Figure 4 The figure illustrates a physical resource block in a wireless communication system to which the present disclosure can be applied. Figure 5 The diagram illustrates a time slot structure in a wireless communication system to which the present disclosure may be applied.

[0119] refer to Figure 4 and Figure 5 , a slot includes a plurality of symbols in the time domain. For example, for a normal CP, 1 slot includes 7 symbols, but for an extended CP, 1 slot includes 6 symbols.

[0120] A carrier includes 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 may correspond to one parameter set (e.g., SCS, CP length, etc.). A carrier may include up to N (e.g., 5) BWPs. Data communication may be performed through activated BWPs, and only one BWP may be activated for one terminal. In a resource grid, each element is called a resource element (RE) and may map one complex symbol.

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

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

[0123] Figure 6 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 physical channels are illustrated.

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

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

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

[0127] Meanwhile, when the terminal accesses the base station for the first time or has no radio resources for signal transmission, it may perform a random access (RACH) procedure on the base station (S603 to S606). For the random access procedure, the terminal may send a specific sequence as a preamble through a physical random access channel (PRACH) (S603 and S605), and may receive a response message to the preamble through a PDCCH and a corresponding PDSCH (S604 and S606). The contention-based RACH may additionally perform a contention resolution procedure.

[0128] The terminal that subsequently performs the above process may perform PDCCH / PDSCH reception (S607) and PUSCH (Physical Uplink Shared Channel) / PUCCH (Physical Uplink Control Channel) transmission (S608) as a general uplink / downlink signal transmission process. Specifically, the terminal receives downlink control information (DCI) through the PDCCH. Here, DCI includes control information such as resource allocation information for the terminal, and the format varies according to its purpose of use.

[0129] At the same time, the control information sent by the terminal to the base station through the uplink or received by the terminal from the base station includes downlink / uplink ACK / NACK (acknowledgement / non-acknowledgement) signal, CQI (channel instruction indicator), PMI (precoding matrix indicator), RI (rank indicator), etc. For the 3GPP LTE system, the terminal can send the above-mentioned CQI / PMI / RI and other control information through PUSCH and / or PUCCH.

[0130] Table 5 shows an example of the DCI format in the NR system.

[0131] [Table 5]

[0132]

[0133] Referring to Table 5, DCI formats 0_0, 0_1, and 0_2 may include resource information (e.g., UL / SUL (supplementary UL), frequency resource allocation, time resource allocation, frequency hopping, etc.), information related to a transport block (TB) (e.g., MCS (modulation coding and scheme), NDI (new data indicator), RV (redundancy version), etc.), information related to HARQ (hybrid-automatic repeat and request) (e.g., process number, DAI (downlink assignment index), PDSCH-HARQ feedback timing, etc.), information related to multiple antennas (e.g., DMRS sequence initialization information, antenna port, CSI request, etc.), power control information related to scheduling of PUSCH (e.g., PUSCH power control, etc.), and control information included in each DCI format may be predefined.

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

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

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

[0137] Next, DCI formats 1_0, 1_1 and 1_2 may include resource information (e.g., frequency resource allocation, time resource allocation, VRB (virtual resource block) -PRB (physical resource block) mapping, etc.), information related to transport blocks (TBs) (e.g., MCS, NDI, RV, etc.), information related to HARQ (e.g., process number, DAI, PDSCH-HARQ feedback timing, etc.), information related to multiple antennas (e.g., antenna ports, TCI (transmission configuration indicator), SRS (sounding reference signal) requests, etc.), information related to PUCCH scheduling regarding PDSCH (e.g., PUCCH power control, PUCCH resource indicator, etc.), and the control information included in each DCI format may be predefined.

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

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

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

[0141] Artificial Intelligence (AI) Operation

[0142] With the technological advancement of artificial intelligence / machine learning (AI / ML), nodes and UEs in wireless communication networks are becoming more intelligent / advanced. Specifically, due to the intelligence of the network / base station, it is expected to be possible to quickly optimize and derive / apply various network / base station decision parameter values ​​(e.g., transmit / receive power of each base station, transmit power of each UE, precoder / beam of base station / UE, time / frequency resource allocation for each UE, duplex method of each base station, etc.) according to various environmental parameters (e.g., distribution / location of base stations, distribution / location / material of buildings / furniture, etc., location / moving direction / speed of UE, climate information, etc.). Following this trend, many standardization organizations (e.g., 3GPP, O-RAN) are considering the introduction, and research on this is also actively underway.

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

[0144] Figure 7 Illustration of the classification of artificial intelligence.

[0145] refer to Figure 7 , Artificial Intelligence (AI) corresponds to all automation in which machines are able to replace work that should be done by humans.

[0146] Machine learning (ML) refers to techniques in which machines learn decision-making patterns from data on their own without explicitly programmed rules.

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

[0148] AI (or AI / ML) can be narrowly referred to as artificial intelligence based on deep learning, but the present disclosure is not limited thereto. That is, in the present disclosure, AI (or AI / ML) can be collectively referred to as automation technology applied to intelligent machines (e.g., UE, RAN, network nodes, etc.) that can perform tasks like humans.

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

[0150] 1. Offline / Online Learning

[0151] a) Offline Learning

[0152] Offline learning follows a sequential process of database collection, learning, and prediction. That is, collection and learning can be performed offline, and the completed program can be installed in the field and used for prediction work. For offline learning, the system does not learn incrementally, the learning is performed using all available collected data, and the learning is applied to the system without further learning. If learning about new data is necessary, learning can be restarted using all new data.

[0153] b) Online learning

[0154] This refers to a method that gradually improves performance by incrementally learning additional data using data generated in real time, and more recently, by taking advantage of the fact that data that can be used for learning is constantly being generated over the Internet. Learning is performed in real time for each (bundle) of specific data collected online, allowing the system to quickly adapt to changing data.

[0155] Building AI systems using only online learning, so that learning can be performed only with data generated in real time, or after performing offline learning using a predetermined data set, additional learning can be performed using additional real-time data (online + offline learning).

[0156] 2. Classification based on AI / ML framework concepts

[0157] a) Focused Learning

[0158] 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 one centralized node.

[0159] b) Federated Learning

[0160] b) Federated learning is a collective model built on data that exists across distributed data owners. Instead of collecting data into the model, the AI / ML model is imported into the data source, allowing local nodes / single 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 has advantages in increasing computing speed and information security. In other words, the process of uploading personal data to a central server is not necessary, preventing the leakage and abuse of personal information.

[0161] c) Distributed Learning

[0162] Distributed learning refers to a concept in which the machine learning process is scaled and distributed across a cluster of nodes. The trained model is partitioned and shared across multiple nodes running simultaneously to accelerate model training.

[0163] 3. Classification by learning method

[0164] a) Supervised Learning

[0165] Supervised learning is a machine learning task where the goal is to learn a mapping function from input to output given a labeled dataset. The input data is called training data and has known labels or outcomes. An example of supervised learning is as follows.

[0166] -Regression: linear regression, logistic regression

[0167] - Example-based algorithm: k-nearest neighbor (KNN)

[0168] -Decision Tree Algorithm: Classification and Regression Trees (CART)

[0169] -Support Vector Machine (SVM)

[0170] -Bayesian algorithm: Naive Bayes

[0171] - Fusion algorithm: Extreme Gradient Boosting, Bagging method: Random Forest

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

[0173] b) Unsupervised Learning

[0174] Unsupervised learning is a machine learning task that aims to learn features that describe hidden structure in unlabeled data. The input data is not labeled, and no known outcome exists. Some examples of unsupervised learning include K-means clustering, principal component analysis (PCA), nonlinear independent component analysis (ICA), and long short-term memory (LSTM).

[0175] c) Reinforcement Learning (RL)

[0176] In reinforcement learning (RL), an agent aims to optimize a long-term goal by interacting with the environment based on a trial-and-error process and is goal-directed learning based on interactions with the environment. An example of an RL algorithm is as follows.

[0177] -Q-Learning

[0178] - Multi-armed bandit learning

[0179] -Deep Q Network

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

[0181] -Temporal Difference Learning

[0182] -Actuator-Evaluator Reinforcement Learning

[0183] -Deep Deterministic Policy Gradient (DDPG)

[0184] -Monte-Carlo tree search

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

[0186] -Model-based reinforcement learning: refers to RL algorithms that use predictive models. Using a model of the various dynamic states of the environment and which states bring rewards, it obtains the probability of transitioning between states.

[0187] - Model-free reinforcement learning: refers to RL algorithms based on values ​​or policies that maximize future rewards. The multi-agent environment / state is less computationally complex and does not require an accurate representation of the environment.

[0188] In addition, RL algorithms can also be divided into value-based RL and policy-based RL, policy-based RL and non-policy RL, etc.

[0189] In the following, representative models of deep learning are illustrated with examples.

[0190] Figure 8 Diagram of a feedforward neural network.

[0191] Feedforward neural network (FFNN) consists of input layer, hidden layer and output layer.

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

[0193] Fig. 9 Diagram of a recurrent neural network.

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

[0195] exist Fig. 9 , A represents a neural network, xt represents an input value, and ht represents an output value. Here, ht may refer to a state value representing a current state based on time, and ht-1 may represent a previous state value.

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

[0197] Fig.10 Diagram of a convolutional neural network.

[0198] Convolutional Neural Networks (CNNs) are used for two purposes: reducing model complexity and extracting superior features by applying convolution operations commonly used in the field of image processing or image processing.

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

[0200] -Stride: refers to how far to move the kernel within the input.

[0201] - Feature map: refers to the result of applying the kernel to the input. Multiple feature maps can be extracted to ensure robustness to distortion, changes, etc.

[0202] -Padding: refers to the value added to resize the feature map.

[0203] -Pooling: refers to the operation of reducing the size of feature maps by downsampling them (e.g., max pooling, average pooling).

[0204] Fig.11 Graph of an autoencoder.

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

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

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

[0208] Hereinafter, for a more specific explanation of AI (or AI / ML), the terms can be defined as follows.

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

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

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

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

[0213] Fig.12 Diagram of the functional framework for AI operations.

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

[0215] Examples of input data may include measurements from the UE or different network entities, feedback from actuators, outputs from AI models.

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

[0217] After performing the data preparation process, the model training function (10) provides training data (11) to the model training function (20), and provides 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).

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

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

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

[0221] The model inference function (30) is a function that provides AI model inference output (16) (e.g., predictions or decisions). When applicable, the model inference function (30) can provide model performance feedback (14) to the model training function (20). If necessary, based on the inference data (12) delivered by the data collection function (10), the model inference function (30) is also responsible for data preparation (e.g., data preprocessing and cleaning, formatting and transformation).

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

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

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

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

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

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

[0228] - Validation data: This refers to a dataset used to validate the model that has already been learned. In other words, it usually refers to a dataset used to prevent overfitting of the training dataset.

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

[0230] -Test data: refers to the data set used for final evaluation. This data is not related to learning.

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

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

[0233] Cat 0a) No Collaboration Framework: AI / ML algorithms are purely implementation based and do not require any air interface changes.

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

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

[0236] Cat 2) can perform joint ML tasks between UE and gNB. This level requires AI / ML model commands and exchanges between network nodes.

[0237] Previously in Fig.12 The functions illustrated in the drawings may be implemented in a RAN node (eg, a base station, a TRP, a base station central unit (CU) etc.), a network node, an Operation Administration and Maintenance (OAM) of a network operator, or a UE.

[0238] Alternatively, in Fig.12 The functions illustrated in the figure may be implemented by cooperation between two or more entities in the RAN, network nodes, network operator's OAM or UE. For example, one entity may perform Fig.12 Some of the functions of the other entity can be performed and other entities can perform the remaining functions. Fig.12 Some functions illustrated in the figure are performed by a single entity (e.g., UE, RAN node, network node, etc.), so the transmission / provision of data / information between each function can be omitted. For example, if the model training function (20) and the model inference function (30) are performed by the same entity, the deployment / update of the model (13) and the delivery / provision of the model performance feedback (14) can be omitted.

[0239] Alternatively, in Fig.12 Any of the functions illustrated in the figure may be performed by collaboration between two or more entities among the RAN, network nodes, network operator's OAM or UE. This can be referred to as split AI operation.

[0240] Fig.13 It is a diagram illustrating segmentation AI inference.

[0241] Fig.13 The diagram illustrates a situation where, in a split AI operation, the model inference function is performed in collaboration with an end device such as a UE and a network AI / ML endpoint.

[0242] In addition to the model inference function, the model training function, executor, and data collection functions are divided into multiple parts depending on the current task and environment, and can be executed collaboratively by multiple entities.

[0243] For example, the computationally intensive and energy-intensive parts can be executed at the network endpoint, while the personal information-sensitive and latency-sensitive parts can be executed at the end device. In this case, the end device is able to execute the task / model from the input data to a specific part / layer, and then send the intermediate data to the network endpoint. The network endpoint executes the remaining parts / layers and provides inference output to one or more devices that perform the action / task.

[0244] Fig.14 The diagram illustrates the application of the functional framework in a wireless communication system.

[0245] Fig.14 The diagram illustrates a situation where the AI ​​model training function is performed by a network node (eg, a core network node, an OAM of a network operator, etc.) and the AI ​​model inference function is performed by a RAN node (eg, a base station, a TRP, a CU of a base station, etc.).

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

[0247] Step 2: The network nodes use the received training data to train the AI ​​model.

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

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

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

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

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

[0253] Step 7: RAN Node 1, RAN Node 2 and UE (or "RAN Node 1 and UE", or "RAN Node 1 and RAN Node 2") perform actions based on the output data. For example, in the case of a load balancing operation, the UE may move from RAN Node 1 to RAN Node 2.

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

[0255] Fig.15 The diagram illustrates the application of the functional framework in a wireless communication system.

[0256] Fig.15The diagram illustrates a situation where both the AI ​​model training function and the AI ​​model inference function are performed by a RAN node (e.g., a base station, a TRP, a CU of a base station, etc.).

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

[0258] Step 2: RAN node 1 uses the received training data to train the AI ​​model.

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

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

[0261] Step 5: RAN Node 1, RAN Node 2 and UE (or "RAN Node 1 and UE", or "RAN Node 1 and RAN Node 2") perform actions based on the output data. For example, in the case of a load balancing operation, the UE may move from RAN Node 1 to RAN Node 2.

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

[0263] Fig.16 The diagram illustrates the application of the functional framework in a wireless communication system.

[0264] Fig.16 The diagram illustrates a situation in which the AI ​​model training function is performed by a RAN node (e.g., a base station, a TRP, a CU of a base station, etc.) and the AI ​​model inference function is performed by a UE.

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

[0266] Step 2: The RAN node uses the received training data to train the AI ​​model.

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

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

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

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

[0271] Step 7: The UE and RAN nodes perform actions based on the output data.

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

[0273] CSI (Channel State Information) related operations

[0274] In NR (New Radio) systems, CSI-RS (Channel State Information-Reference Signal) is used for time and / or frequency tracking, CSI calculation, L1 (Layer 1)-RSRP (Reference Signal Received Power) calculation and mobility. Here, CSI calculation is related to CSI acquisition, while L1-RSRP calculation is related to Beam Management (BM).

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

[0276] -In order to perform one of the uses of CSI-RS, a terminal (eg, user equipment, UE) receives CSI-related configuration information from a base station (eg, generalized Node B, gNB) through RRC (Radio Resource Control) signaling.

[0277] The CSI-related configuration information may include at least one of information related to CSI-IM (interference management) resources, information related to CSI measurement configuration, information related to CSI resource configuration, information related to CSI-RS resources, or information related to CSI report configuration.

[0278] i) Information related to CSI-IM resources 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 a resource set includes at least one CSI-IM resource. Each CSI-IM resource is identified by a CSI-IM resource ID.

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

[0280] Each NZP CSI-RS resource set may be configured with parameters indicating the usage of the CSI-RS (eg, a "repeat" parameter related to BM, a "trs-Info" parameter related to tracking).

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

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

[0283] The CSI measurement may include (1) a process in which a terminal receives a CSI-RS and (2) a process in which a CSI is calculated through the received CSI-RS, and a detailed description thereof is described later.

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

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

[0286] In this case, when the number of CSI-ReportConfig is configured as "None (or No Report)", the terminal can omit the report. However, although the number is configured as "None (or No Report)", the terminal can also perform reporting to the base station. When the number is configured as "None", a non-periodic TRS is triggered or configuration repetition is configured. In this case, the terminal's report can be omitted only when repetition is configured as "On".

[0287] 1) CSI measurement

[0288] The NR system supports more flexible and dynamic CSI measurement and reporting. Here, CSI measurement may include the process of receiving CSI-RS and obtaining CSI by calculating the received CSI-RS.

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

[0290] 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. Additionally, each port emulates the interference layer with (desirable channel sum) pre-coded NZP CSI-RS in NZP CSI-RS based IMR. Because it is about intra-cell interference measurement for multi-user cases, it mainly targets MU interference.

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

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

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

[0294] Describes resource settings and resource setting configuration in more detail.

[0295] 2) Resource settings

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

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

[0298] The time domain behavior of the CSI-RS resources in the CSI resource settings included in the CSI-ResourceConfig IE may be indicated by the higher layer parameter resourceType and may be configured as 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 by the parameter set of the associated DL BWP, as given by bwp-id.

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

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

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

[0302] -CSI-IM resources for interference measurements

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

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

[0305] In other words, the CMR (channel measurement resource) may be the NZP CSI-RS used for CSI acquisition, and the IMR (interference measurement resource) may be the NZP CSI-RS used for CSI-IM and IM.

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

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

[0308] The UE may assume that the CSI-RS resources for channel measurement and the CSI-IM / NZP CSI-RS resources for interference measurement configured for one CSI report are "QCL-Type D" for each resource.

[0309] 3) Resource settings configuration

[0310] As mentioned above, a resource setting may mean a list of resource sets.

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

[0312] One report setup can be linked to up to 3 resource setups.

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

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

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

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

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

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

[0319] 4) CSI calculation

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

[0321] In addition, when performing interference measurement in NZP CSI-RS, the UE does not expect that one or more NZP CSI-RS resources are configured in the resource set associated in the resource setting for channel measurement.

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

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

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

[0325] - Consider the EPRE (Energy Per Resource Element) ratio of all interfering transmission layers of the NZP CSI-RS port for interference measurement.

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

[0327] 5) CSI Report

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

[0329] 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), a SS / PBCH block resource indicator (SSBRI), a layer indicator (LI), a rank indicator (RI), or an L1-RSRP.

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

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

[0332] i) Periodic CSI reporting is performed in short PUCCH and long PUCCH. The periodicity and slot offset of periodic CSI reporting can be configured by RRC and refers to CSI-ReportConfig IE.

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

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

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

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

[0337] DCI format 0_1 ​​may include a CSI request field and activate / deactivate a specific configured SP-CSI triggering state. SP CSI reporting has the same or similar activation / deactivation mechanism as data transmission in SPS PUSCH.

[0338] iii) Aperiodic CSI reporting is performed in PUSCH and triggered by DCI. In this case, information related to aperiodic CSI reporting triggering can be delivered / indicated / configured via MAC-CE.

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

[0340] In NR, the method of dividing and reporting CSI in multiple reporting instances of PUCCH-based CSI reporting in LTE (e.g., sending in the order of RI, WB PMI / CQI, SB PMI / CQI) is not applied. Instead, in NR, there is a restriction that specific CSI reports are not configured in short / long PUCCH, and CSI omission rules are defined. In addition, regarding AP CSI reporting timing, the PUSCH symbol / slot position is dynamically indicated by DCI. In addition, the candidate slot offset is configured by RRC. For CSI reporting, one slot offset (Y) is configured for each reporting setting. For UL-SCH, the slot offset K2 is configured separately.

[0341] Two CSI latency categories (low latency category, high latency category) are defined regarding CSI computational complexity. Low latency CSI is WB CSI, which includes up to 4 ports of type I codebook or up to 4 ports of non-PMI feedback CSI. High latency CSI refers to CSI other than low latency CSI. For ordinary terminals, (Z, Z') is defined in units of OFDM symbols. Here, Z represents the minimum CSI processing time from the receipt of a non-periodic CSI triggering DCI until the CSI report is performed. In addition, Z' refers to the minimum CSI processing time from the receipt of the CSI-RS for the channel / interference until the CSI report is performed.

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

[0343] Method for transmitting / receiving CSI

[0344] In the above CSI related operations, reportQuantity specifies the report content reported by the UE. reportQuantity can be set to the following values:

[0345] - "None": In this case, the UE does not provide CSI reports to the base station. For example, the base station may repeatedly transmit the CSI-RS, and the UE may try to receive the CSI-RS with each receive beam. The UE may then identify the best receive beam and use the receive beam for subsequent data reception.

[0346] - "CRI, RI, and CQI (cri-RI-CQI)": RI and CQI can be used in an open-loop (OL) MIMO transmission scheme. The base station can iteratively cycle through a set of predefined precoding matrices. In addition, the "cri-R1-CQI" value can be used when channel reciprocity exists and the base station can determine the appropriate precoding matrix from uplink SRS measurements.

[0347] - "CRI, RI, PMI, and CQI (cri-RI-PMI-CQI)": RI, CQI, and PMI can be used for closed-loop (CL) MIMO transmission schemes. A set of four codebook categories can be specified for PMI reporting: type-1 single-panel codebook and type-1 multi-panel codebook for single-user MIMO up to 8 ranks, type-2 single-panel codebook and type-2 port selection codebook for multi-user MIMO limited to up to 2 ranks. Type-1 codebooks provide relatively coarse information, while type-2 codebooks provide more detailed information at the expense of increased signal overhead.

[0348] - "CRI, RI, LI, PMI and CQI (cri-RI-LI-PMI-CQI)": This also applies to the CL MIMO transmission method and also provides LI (layer indicator) information to the base station. The LI information can be used to map a specific logical antenna port to the best transmission layer.

[0349] - "CRI, RI, i1 and CQI (cri-RI-i1-CQI)": This can be applied to a semi-open-loop (semi-OL) transmission scheme. In this case, the UE reports partial precoding information (only "i1") instead of reporting complete precoding information (a combination of "i1" and "i2"). This option can be used with a type 1 single-panel codebook, where the UE reports a single wideband PMI value (i.e., i1), which is assumed to change relatively slowly. The base station can combine the reported wideband PMI value (i.e., i1) with open-loop precoding. Typically, the reported CQI value depends on the reported PMI value. That is, the CQI value is valid when the base station transmits using the proposed precoding matrix. In this case, because the UE reports only partial PMI, the UE can assume that the precoding matrix for the remaining PMI is randomly selected. In addition, the UE can assume that precoding is applied to a precoded resource block group (PRG) having a size equal to the PDSCH bundling size (pdsch-BundleSizeForCSI) of the CSI.

[0350] – “CRI, RI, and i1 (cri-RI-i1)”: In this case, the base station instructs the UE to report RI and partial PMI, but does not instruct the UE to report CQI. This configuration can be used with the above “CRI, RI, PMI, and CQI (cri-RI-PMI-CQI)” configuration to support a hybrid solution. When configured as “CRI, RI, and i1 (cri-RI-i1)”, the UE can report a first CSI report to the base station using a type-1 single-panel codebook, which includes a PMI that includes a single wideband indication (i.e., i1) for all CSI reporting bands. This enables the base station to generate a coarse beam toward the UE. And, for example, the UE can provide a second CSI report to the base station using “CRI, RI, PMI, and CQI (cri-RI-PMI-CQI)” with a type-2 port selection codebook. The partial PMI of the first CSI report can be used to beamform the CSI-RS, and this CSI-RS can be used to generate a full PMI for the second CSI report.

[0351] – “CRI and RSRP (cri-RSRP)”: This can be used for beam management procedures. In this case, the UE can report up to 4 CRIs and up to 4 layer-1 RSRP measurements.

[0352] – “SSB Index and RSRP (ssb-Index-RSRP)”: This can be used for beam management procedures. In this case, the UE can report up to 4 SSBRIs (SS / PBCH Block Resource Indicators) and up to 4 Layer 1 RSRP measurements.

[0353] Hereinafter, in the description of the present disclosure, OL CSI report / feedback may mean a CSI report / feedback that does not include PMI (and also includes RI and CQI). In addition, CL CSI report / feedback may mean a CSI report / feedback that includes PMI (or further includes LI) (and further includes RI and CQI (and LI)). In addition, semi-OL CSI report / feedback may mean a report / CSI feedback that includes only a partial PMI (e.g., wideband partial PMI, i1) (or includes RI and CQI).

[0354] As described above, according to the existing LTE / NR standards, when the UE reports CSI, for the PMI among RI / PMI / CQI, a method of reporting only part of the entire PMI index (e.g., wideband part PMI, i1) or not reporting the PMI (i.e., semi-OL CSI feedback) is supported.

[0355] For example, in LTE, PMI is not reported, and the UE can randomly select a precoder of a fixed subset of PMI from the frequency / time resources (e.g., by cycling the precoder of the PMI subset) to calculate and report RI / CQI. In NR, only i1 (i.e., wideband PMI) is reported among the two indexes i1 / i2 constituting the PMI. In such a case, the UE can randomly select a precoder corresponding to the subset of i2 configured as RRC by the base station (e.g., by typeI-SinglePanel-codebookSubsetRestriction-i2) from the frequency / time resources (e.g., by cycling the precoder of the PMI subset) to calculate and report RI / CQI. Here, it is possible to calculate and report RI / CQI by b 15 A bitmap consisting of a bit sequence of b1, b2, b3, b4, b5, b6 (e.g., typeI-SinglePanel-codebookSubsetRestriction-i2) configures a codebook subset (or precompiler subset), where b0 is the least significant bit (LSB) and b 15 is the most significant bit (MSB). i is associated with the pre-coder corresponding to the codebook index i2=i. i is 0, the randomly selected precompiler for CQI calculation may not correspond to b i Any precompilers associated with it.

[0356] When the UE is configured with a CSI reporting configuration (CSI-ReportConfig) with the higher layer parameter reportQuantity set to "cri-RI-i1", the UE may expect the higher layer parameter "codebookType" set to "typeI-SinglePanel" and the higher layer parameter "PMI format indicator (pmi-FormatIndicator)" set to "widebandPMI" for the corresponding CSI reporting configuration. In addition, the UE may report a PMI consisting of a single wideband indication (i.e., i1) for the entire CSI reporting band.

[0357] When a UE configures a CSI reporting configuration (CSI-ReportConfig) with the higher layer parameter reportQuantity set to "cri-RI-i1-CQI", the UE may expect the higher layer parameter "codebookType" set to "typeI-SinglePanel" and the higher layer parameter "PMI format indicator (pmi-FormatIndicator)" set to "widebandPMI" for the corresponding CSI reporting configuration. Additionally, the UE may report a PMI consisting of a single wideband indication (i.e., i1) for the entire CSI reporting band. Here, the CQI may be conditionally calculated based on the reported i1, assuming that the PDSCH transmission uses N P ≥ 1 precoder (i.e., a precoder corresponding to the same i1 but different i2). In addition, the UE can assume that a precoder is from N P Here, the PRG (precoded resource block group) size used for CQI calculation can be configured by the higher layer parameter "PDSCH bundle size for CSI (pdsch-BundleSizeForCSI)".

[0358] This feedback scheme aims to report robust CSI by applying precoder random selection (eg, precoder loop) in environments where channel estimation is inaccurate or the channel changes rapidly.

[0359] Embodiment 1: The UE may report only some of the multiple indexes (eg, i1) constituting the PMI, and may configure the PMI set (or precompiler set or cyclic set) of the remaining indexes by itself.

[0360] In the future, AI / ML capabilities can be applied to UE / base station to optimize the pre-compiler (or cyclic pre-compiler) applied to such semi-OL CSI reporting / feedback, and the DL scheduling of the base station can be optimized through additional CSI quantity reporting.

[0361] The UE (supporting AI / ML functions) may report only some of the multiple indices constituting the PMI (e.g., i1), and may configure a precoder set (or a cyclic set) for the remaining indices by itself. The UE may calculate the RI / CQI by randomly selecting (or cyclically) a precoder based on the (precoded) resource unit determined by itself for the precoder set (or cyclic set) (i.e., assuming downlink transmission using a randomly selected precoder). For example, for an estimated channel, the UE may report i1 (i.e., report CSI including i1), determine i2 by itself (e.g., 1, 2, 3, 4), and may also determine the precoded resource unit by itself (e.g., 1 RB).

[0362] Accordingly, in addition to the existing RI / i1 / CQI (i.e., CSI) determined by the existing semi-OL CSI reporting method (or determined by the existing closed-loop CSI reporting / feedback method), the RI' / i1' / CQI' (i.e., CSI') calculated by the proposed method can be calculated / derived, and the UE can report the two CSIs together. That is, the UE can report the two CSIs calculated / derived by different CSI reporting / feedback modes / methods together to the base station. Here, the existing closed-loop CSI reporting / feedback method may mean a method in which the UE calculates the complete PMI from the codebook and calculates the RI and CQI accordingly.

[0363] When two CSIs calculated / derived using different CSI reporting / feedback modes / methods are reported together to the base station, the UE can additionally calculate the difference of CSI' based on the existing CSI (or vice versa) and report it to reduce feedback overhead. For example, if it is always assumed that RI'=RI, i1=i1' in CSI' (e.g., RI, i1 are unlikely to be significantly different), the UE does not report RI', i1' to the base station, and the UE can only report the difference (e.g., delta) between CQI' and CQI to the base station, and therefore, the UE can report RI / i1 / CQI / (delta)CQI' to the base station. The payload size of this difference can also be determined directly by the UE, and the UE can report the determined payload size to the base station. For example, if the difference between CQI' and CQI is large, it is preferable to increase the payload size and report the difference in more detail.

[0364] Alternatively, the UE may compare the CSI and CSI', and report a portion ( / the entirety) of the CSI as a replacement for a portion ( / the entirety) of the CSI'. For example, if CQI and CQI' are compared and CQI' is greater than CQI, CQI may be replaced by CQI', and RI / i1 / CQI' may be reported to the base station. In this way, information about whether a portion of the CSI is replaced may be additionally reported to the base station (i.e., explicitly reported with the CSI or implicitly reported separately from the CSI). For example, the UE may additionally report a 1-bit value together with the RI / i1 / CQI, and report to the base station the manner in which the corresponding CSI is calculated (i.e., in which CSI reporting / feedback mode / method the CSI is calculated) depending on whether the 1-bit value is 0 / 1. For example, the UE is able to decide by itself which CSI to report between the CSI calculated in a traditional manner and the CSI calculated in a new manner by the AI / ML-based UE, and report the decision result to the base station together with the CSI.

[0365] Meanwhile, in a semi-OL CSI scheme in which PMI is not reported at all, the UE can report only RI / CQI to the base station, without even reporting i1 or i1' to the base station. Here, the existing UE can calculate RI / CQI using a fixed (pre-determined or pre-configured) PMI. On the other hand, in the case where the UE supports AI / ML functions, it can even determine the corresponding PMI by itself to calculate RI' / CQI', and report the calculated RI' / CQI' in combination with the proposed method of the present disclosure (ie, together or separately).

[0366] The base station that has received the CSI' is able to estimate and identify the PMI set (or pre-compiler set or cycle set) and pre-compiled resource unit (or cycle resource unit) determined by the UE itself (as well as various other CSI-related values ​​determined by the UE) through AI / ML technology. By feeding back the estimated value to the UE, the UE and / or the base station are able to compare the value estimated by the base station with the value determined by the UE. If the difference is large (for example, if it exceeds a predetermined / preconfigured threshold), the UE and / or the base station is able to update or retrain the AI / ML model to improve accuracy. Therefore, the base station is able to limit the range of pre-compiled resource units (or cycle resource units) and PMI indexes (i.e., the index range of the PMI set (or pre-compiler set or cycle set)) to the UE.

[0367] Alternatively, the UE may report to the base station the PMI set (or pre-compiler set or cyclic set) and pre-compiled resource unit (or cyclic resource unit) determined by the UE itself. In this case, the UE and / or the base station may compare the value estimated by the base station with the value determined by the UE. If the difference is large (e.g., exceeding a predetermined / pre-configured threshold), the UE and / or the base station may update or retrain the AI / ML model to improve accuracy.

[0368] The above-mentioned embodiment 1 may correspond to a joint AI / ML model, in which both the base station and the UE must be able to predict / determine the PMI set (or pre-compiler set or cyclic set) and the pre-compiled resource unit (or cyclic resource unit), and the following input / output parameters may be considered for the joint AI / ML model.

[0369] However, the parameters illustrated below are merely examples, and the proposed method described above and the input / output parameters described below can be applied to the joint AI / ML model, the AL / ML model of the base station (gNB-side AI / ML) model, and the AL / ML model of the UE (UE-side AI / ML) model.

[0370] A. Parameters used in base stations

[0371] i) Input parameters for AI / ML models

[0372] - Time-varying channel characteristics (e.g., channel Doppler shift / spread, UE speed)

[0373] - Frequency selective channel characteristics (e.g., delay spread, average delay)

[0374] - DL channel estimated from SRS (i.e., DL reciprocal channel from UL estimated channel)

[0375] ii) Output parameters for AI / ML models

[0376] - PMI set (or precompiler set or loop set)

[0377] - Precompiled resource units (or loop resource units)

[0378] - Restrictions on PMI sets (or precompiler sets or loop sets) or precompiler resource units (or loop resource units)

[0379] B. Parameters used in UE

[0380] i) Input parameters for AI / ML models

[0381] - Time-varying channel characteristics (e.g., channel Doppler shift / spread, UE speed)

[0382] - Frequency selective channel characteristics (e.g., delay spread, average delay)

[0383] - Channel estimated from CSI-RS (i.e., from Channel Measurement Resources (CMR))

[0384] – Interference estimated from ZP / NZP CSI-RS (i.e., from Interference Measurement Resources (IMR))

[0385] - Restrictions on PMI sets (or precompiler sets or loop sets) or precompiler resource units (or loop resource units)

[0386] ii) Output parameters for AI / ML models

[0387] - PMI set (or precompiler set or loop set)

[0388] - Precompiled resource units (or loop resource units)

[0389] - Payload size used to report the difference between CSI (CSI according to the existing CL CSI reporting method) and CSI' (CSI according to the proposed method)

[0390] - Determine which CSI to report between CSI (CSI according to the existing CL CSI reporting method) and CSI' (CSI according to the proposed method)

[0391] As an example of the above-mentioned "pre-coded resource unit (or cyclic resource unit)", it can be determined as a physical resource block (PRB) bundling size (PRB bundling size) or a multiple of the PRB bundling size. In addition, in order to effectively apply the method proposed in the present disclosure, a more dynamic PRB bundling size configuration / indication (e.g., via MAC-CE (control element) and / or DCI) can be introduced.

[0392] In addition, the above-mentioned "pre-coded resource unit (or cyclic resource unit)" is not limited to the frequency resource unit, and can be configured as a time resource unit. The time resource unit can be configured / determined at the symbol (or symbol group) level, and a different pre-coded unit can be randomly selected (e.g., by cyclic selection) for each time resource unit to calculate the CSI.

[0393] Embodiment 2: The UE can determine by itself the payload size or the value of a code point indicating a difference (or a difference between code points) for reporting a difference between wideband (WB) CSI and subband (SB) CSI.

[0394] CSI can be classified into SB CSI and WB CSI according to frequency granularity. Here, SB CSI has higher accuracy and higher feedback overhead than WB CSI because CSI optimized for each subband is reported separately.

[0395] Each SB CSI is reported as a difference based on the WB CSI. Here, the UE can directly determine the payload size (defined as 2 bits in the current standard) for reporting the difference between the SB CSI and the WB CSI. In addition, the UE can report the determined payload size to the base station. For example, if the difference between the WB CSI and the SB CSI is large, it is more desirable to increase the payload size and report the difference in more detail.

[0396] In addition, the UE can determine the value of each code point representing the difference between the SB CSI and the WB CSI by itself. In addition, the UE can report the determined value of each code point (or the difference between the code point values) to the base station. For example, in the case of reporting a difference with 2 bits, the values ​​of 00, 01, 10, and 11 can be determined as -1, 0, 1, >1 (i.e., greater than 1) or -2, 0, 2, >2 (i.e., greater than 2), respectively.

[0397] Embodiment 3: The UE is able to determine the frequency CSI reporting / feedback method according to the density of the reference signal (RS) in the corresponding frequency region.

[0398] The RS designed in the current standard has a constant RS density in the frequency domain, but the RS designed for UEs supporting AI / ML functions may have low RS density or no RS in some frequencies / SBs (subbands) / RBs (resource blocks), or may have high RS density in some frequencies / SBs / RBs. In this way, the RS density may not be uniform in the frequency domain, which may result in lower RS ​​signaling overhead.

[0399] For example, in a frequency region where a low-density RS is configured, the UE may have lower channel estimation accuracy. Thus, in the case of low channel estimation accuracy, a semi-OL CSI feedback method that sends some indexes of a PMI and randomly selects (e.g., by cyclic selection) the remaining indexes or does not send the PMI at all may be more efficient than a closed-loop CSI feedback method that selects / reports a complete PMI from a codebook. Therefore, for a frequency region (e.g., SB) where a low-density RS is configured, the UE is able to send semi-OL CSI (i.e., use a semi-OL CSI reporting method / mode) (i.e., because the low-density RS frequency region has a low density, even if the UE sends a closed-loop CSI, the channel estimation accuracy is reduced). On the other hand, for a frequency region (e.g., SB) where a high-density RS is configured, the UE is able to send a closed-loop CSI (i.e., use a closed-loop CSI reporting method / mode).

[0400] In the case of an AI / ML UE, a high channel estimation accuracy can generally be maintained even for a frequency region with a low RS density, but the channel estimation accuracy may be reduced depending on the AI / ML model, parameter settings, training, etc., and also depends on the instantaneous channel environment. The AI / ML UE is able to predict the accuracy of the channel it has measured, and is able to feed back the semi-OL CSI for the frequency region (e.g., SB) with low accuracy (e.g., below / below a predetermined / preconfigured threshold), and is able to feed back the closed-loop CSI for the frequency region with high accuracy (e.g., above / exceeding a predetermined / preconfigured threshold). In addition, the UE is able to report information about the frequency region for which the semi-OL CSI has been fed back and the frequency region for which the closed-loop CSI has been fed back to the base station.

[0401] And / or, the above operation can also be performed on the time domain. For example, if the time when the CSI report is performed or the time when the CSI reference resource is performed is significantly different from the channel measurement time (for example, greater than N symbols / time slots), the UE can consider that the measured channel is outdated and feed back half-OL CSI, otherwise, the UE can consider that the measured channel is fresh and feed back closed CSI.

[0402] Here, CSI reference resources may mean the frequency and time unit (ie, resource) that the UE assumes to be allocated / transmitted when calculating / deriving CSI, and may be defined as follows. For example, the CSI reference resources of a serving cell may be defined as a set of downlink physical resource blocks (PRBs) corresponding to a band related to CSI derived in the frequency domain, while the CSI reference resources for CSI reporting in uplink slot n' in the time domain may be defined by a single downlink slot.

[0403] Alternatively, the UE may determine the accuracy of the measured channel and feed back semi-OL CSI in the time domain (e.g., the time when CSI reporting is performed, or the time of the CSI reference resource, or the time domain in which the channel represented by the CSI is located) with lower accuracy, and feed back closed-loop CSI in the time domain with higher accuracy.

[0404] For the proposed operation, the following input / output parameters of the AI / ML model for the UE may be considered.

[0405] However, the parameters illustrated below are merely examples, and the above-proposed method and the input / output parameters described below can be applied to the joint AI / ML model, the AL / ML model of the base station (gNB-side AI / ML) model, and the AL / ML model of the UE (UE-side AI / ML) model.

[0406] A. Parameters used in UE

[0407] i) Input parameters for AI / ML models

[0408] - Time-varying channel characteristics (e.g., channel Doppler shift / spread, UE speed)

[0409] - Frequency selective channel characteristics (e.g., delay spread, average delay)

[0410] – Channel estimated from CSI-RS (i.e., from CMR)

[0411] – Interference estimated from ZP / NZP CSI-RS (i.e., from IMR)

[0412] - Restrictions on PMI sets (or precompiler sets or loop sets) or precompiler resource units (or loop resource units)

[0413] - CSI-RS pattern (e.g., RE location, periodicity, transmission slot / symbol)

[0414] ii) Output parameters for AI / ML models

[0415] - Channel accuracy for each frequency region (and / or time region) measured from CMR

[0416] - Determine which CSI to report in each frequency domain (and / or time domain), CL CSI or semi-OL CSI

[0417] Embodiment 4: The UE may report to the base station, including in the CSI feedback information, which mode / method it assumes among the semi-OL CSI reporting mode / method, the CL (closed-loop) CSI reporting mode / method, or "a CSI reporting mode / method that is a mixture of semi-OL and CL CSI depending on the frequency and / or time domain".

[0418] CSI can be divided into CSI Part 1 (or I) and CSI Part 2 (or II), and Table 6 illustrates mapping of CSI fields of CSI Part 1 to one CSI report.

[0419]

Table 6

[0420]

[0421] The information about the above CSI reporting mode / method may be reported by including it in the CSI Part 1 whose payload size does not change dynamically. Alternatively, the base station may configure the mode to the UE.

[0422] When the semi-OL CSI mode or the mixed mode of semi-OL and CL is selected, there may be unused uplink control information (UCI) payload remaining in the PUCCH / PUSCH, which is the UL resource allocated by the base station for CSI reporting. For example, because the PMI is encoded in the CSI part 2 and the CL CSI reporting mode / scheme sends the entire PMI, the entire payload allocated for the CSI part 2 can be used to send the CSI. On the other hand, in the semi-OL CSI reporting mode / scheme, because some indexes of the PMI are reported or the PMI is not reported at all, the payload allocated for the CSI part 2 may remain. This remaining payload portion can be used for zero padding (i.e., mapped to 0) or for additionally reporting other CSI information (i.e., CSI information other than the PMI).

[0423] In addition, when a mixed CSI reporting mode / method of half OL and CL is selected (as described above), the number of CSIs to which half OL CSI is applied and the number of CSIs to which CL CSI is applied can be changed dynamically. For example, when reporting CSI for 10 SBs, half OL CSI can be applied to SBs 1 and 2, and CL CSI can be applied to the remaining SBs. Alternatively, half OL CSI can be applied to SBs 1, 2, 3, and 4, and CL CSI can be applied to the remaining SBs. In this way, as the number of half OL CSIs increases, the overhead of PMI reporting decreases, and thus the UCI payload size of CSI Part 2 can be reduced. In such a case, the UE can report information such as the number / ratio of half OL CSI in the total CSI (or the number / ratio of CL CSI, or the ratio between half OL CSI and CL CSI) in CSI Part 1. By doing so, the base station can calculate the payload size of PMI in CSI Part 2.

[0424] For the above proposed operations, the following input / output parameters of the AI / ML model for the UE may be considered.

[0425] However, the parameters listed below are merely examples, and the above-proposed method and the input / output parameters described below can be applied to the joint AI / ML model, the AL / ML model of the base station (gNB-side AI / ML) model, and the AL / ML model of the UE (UE-side AI / ML) model.

[0426] A. Parameters used in UE

[0427] i) Input parameters for AI / ML models

[0428] - Time-varying channel characteristics (e.g., channel Doppler shift / spread, UE speed)

[0429] - Frequency selective channel characteristics (e.g., delay spread, average delay)

[0430] - Channel estimated from CSI-RS (i.e., from CMR)

[0431] - Interference estimated from ZP / NZP CSI-RS (i.e., from IMR)

[0432] - Accuracy of each channel in each frequency region (and / or time region) measured from CMR

[0433] ii) Output parameters for AI / ML models

[0434] - Select the mode / method to be used among a semi-OL CSI mode / method, a CL (closed loop) CSI mode / method, or a CSI mode / method that is a hybrid of semi-OL and CL CSI depending on the frequency / time region.

[0435] Meanwhile, in the description of the methods proposed according to the above embodiments 1 to 4, semi-OL CSI feedback is mainly described for convenience of explanation, but the methods proposed according to the above embodiments 1 to 4 can be extended and applied to a general CSI feedback method.

[0436] In addition, the method proposed according to the above-mentioned embodiment 1 corresponds to a method of reporting only some of the multiple indexes constituting the PMI (for example, i1) and configuring a PMI set (or a precompiler set or a loop set) for the remaining indexes. However, if the UE configures the PMI set (or a precompiler set or a loop set) using only one PMI, the CSI can be calculated and reported based only on the corresponding PMI instead of applying an arbitrary selection of the UE. In such a case, the method proposed according to the above-mentioned embodiment 1 can also be applied. Alternatively, the UE may not report any PMI at all (i.e., not even report i1), and in such a case, the method proposed according to the above-mentioned embodiment 1 can also be applied.

[0437] In addition, the methods proposed according to the above-mentioned Embodiments 1 to 4 may be independently applied by only one proposed method, or may be finally applied by a combination / combination of two or more proposed methods.

[0438] In addition, the base station can indicate to the UE whether to apply the method proposed in the above embodiments 1 to 4 through signaling such as RRC / MAC-CE / DCI.

[0439] Fig.17 is a diagram illustrating a signaling process between a network and a UE for a method for transmitting and receiving channel state information according to an embodiment of the present disclosure.

[0440] Fig.17 The diagram shows signaling between a network (e.g., TRP 1, TRP 2) and a UE to which the method proposed in the present disclosure (e.g., a combination of one or more of the methods proposed in Embodiments 1 to 4, or Embodiments 1 to 4) can be applied. Here, UE / network is only an example and can be replaced by various devices. Fig.17 It is only for the convenience of explanation and does not limit the scope of the present disclosure. In addition, depending on the situation and / or setting, it can be omitted. Fig.17 Some of the steps are shown in the figure.

[0441] Fig.17 The signaling method described in can be extended and applied to signaling between multiple TRPs and multiple UEs. In the following description, the network can be a base station including multiple TRPs, and can be a cell including multiple TRPs. For example, an ideal / non-ideal backhaul can be configured between TRP 1 and TRP 2 that constitute the network. In addition, the following description is based on multiple TRPs, but it can be equally extended and applied to transmission through multiple panels. In addition, in the present disclosure, the operation of UE receiving a signal from TRP1 / TRP2 can also be interpreted / described as the operation (or can be its operation) of UE receiving a signal from the network (via / using TRP1 / 2), and the operation of UE sending a signal to TRP1 / TRP2 can also be interpreted / described as the operation (or can be its operation) of UE sending a signal to the network (via / using TRP1 / TRP2), and vice versa.

[0442] A base station may be a general term for an object that performs data transmission and reception with a UE. For example, a base station may be a concept including one or more TPs (transmission points), one or more TRPs (transmission and reception points), and the like. In addition, a TP and / or a TRP may include a panel, a transmission and reception unit, and the like of a base station. In addition, "TRP" may be applied by replacing it with expressions such as a panel, an antenna array, a cell (e.g., a macro cell / small cell / micro cell, etc.), a transmission point, a base station (gNB, etc.). As described above, a TRP can be distinguished based on information (e.g., an index, an ID) about a relevant CORESET group (or CORESET pool). As described above, a TRP can be distinguished based on information (e.g., an index, an ID) about a relevant CORESET group (or CORESET pool). For example, if one UE is configured to perform transmission and reception with multiple TRPs (or cells), this may mean configuring multiple CORESET groups (or CORESET pools) for one UE. Configuration of such a CORESET group (or CORESET pool) can be performed via higher layer signaling (e.g., RRC signaling, etc.).

[0443] The UE receives configuration information from the network ( S1701 ).

[0444] The configuration information may include information related to network configuration (eg, TRP configuration) / information related to M-TRP-based transmission and reception (eg, resource allocation, etc.). Here, the configuration information may be sent via higher layer signaling (eg, RRC signaling, MAC-CE, etc.).

[0445] The configuration information may include the CSI-related configuration information described in the above-mentioned methods (eg, embodiments 1 to 4, or a combination of one or more of the methods proposed in embodiments 1 to 4).

[0446] The CSI-related configuration information may include at least one of information related to CSI-IM (interference management) resources, information related to CSI measurement configuration, information related to CSI resource configuration, or information related to CSI report configuration.

[0447] Here, the configuration information related to CSI may include configuration information related to CSI reporting (i.e., CSI reporting configuration) (e.g., RRC IE "CSI-ReportConfig") (hereinafter referred to as first configuration information) and configuration information related to CSI resources (i.e., CSI resource configuration) (e.g., RRC IE "CSI-ResourceConfig") (hereinafter referred to as second configuration information).

[0448] Here, multiple CSI-RS resources for CSI calculation / measurement may be configured for the UE through configuration information related to CSI resources (i.e., CSI resource settings). In addition, one or more channel measurement resources (CMRs) (e.g., NZP CSI-RS) and / or one or more interference measurement resources (IMRs) (e.g., CSI-IM and / or NZP CSI-RS) may be configured through configuration information related to CSI resources (i.e., CSI resource settings). CMRs and IMRs may be collectively referred to as CSI resources.

[0449] Additionally, configuration information related to CSI resources (ie, CSI resource settings) may be linked / associated with configuration information related to specific CSI reports (ie, CSI report settings).

[0450] The UE receives CSI-RS from one or more CSI resources configured by configuration information (eg, CSI resource configuration) from the network ( S1702 ).

[0451] Here, according to the above-mentioned embodiment 3, the CSI-RS may have different densities (ie, the number of CSI-RS resources may be different) in a frequency region (eg, subband, resource block, resource block group) through the frequency domain.

[0452] The UE may receive downlink control information (DCI) from the network ( S1703 ).

[0453] Here, the DCI may be a DCI that triggers aperiodic CSI reporting to the UE, or may be a DCI that activates semi-persistent CSI reporting.

[0454] In addition, DCI may be transmitted via a downlink control channel (eg, PDCCH, etc.), and in addition to triggering / activating CSI reporting, may also schedule a downlink data channel (eg, PDSCH) / uplink data channel (eg, PUSCH).

[0455] The UE transmits (reports) CSI to the network ( S1704 ).

[0456] Here, the CSI may include at least one of CQI, PMI, RI, CRI, and LI.

[0457] As described above, the UE can report CSI to the network based on the configuration information related to CSI reporting (and triggering / activation according to DCI). For example, the CSI reporting type, such as periodic CSI reporting, semi-persistent CSI reporting, and aperiodic CSI reporting, can be configured through the configuration information related to CSI reporting, and in the case of periodic CSI reporting, the period and time slot offset, etc. can also be configured.

[0458] According to the above-described embodiment 4, the UE can assume a specific CSI reporting mode to calculate the CSI, and in this case, the CSI can include information on the CSI reporting mode assumed by the UE to calculate the CSI.

[0459] For example, the PMI may include multiple indices for identifying a precoding matrix (e.g., indices i1 and i2 for identifying a precoding matrix in a predefined codebook), and the CSI reporting mode may be determined as one of the following: i) a first CSI reporting mode (i.e., a CL CSI reporting mode), in which the CSI includes multiple indices; ii) a second CSI reporting mode (i.e., a semi-OL CSI reporting mode), in which the CSI includes only some of the multiple indices (e.g., a single wideband indication for the entire CSI reporting band, i1); iii) a third CSI reporting mode, in which the CSI is a mixture of the first CSI reporting mode and the second CSI reporting mode in the frequency domain and / or time domain (i.e., a CSI reporting mode that mixes CL CSI and semi-OL CSI).

[0460] In addition, when the CSI reporting mode is determined to be the second CSI reporting mode or the third CSI reporting mode, a portion of the payload allocated for CSI that is not used according to the CSI reporting mode may be zero-filled or used to report additional information other than the PMI.

[0461] In addition, when the CSI reporting mode is determined to be the third CSI reporting mode, the CSI may include information about a ratio of information according to the first CSI reporting mode and / or information according to the second CSI reporting mode.

[0462] Meanwhile, the operation of determining the CSI reporting mode can be performed by the UE (or an external device installed or connected to the UE), and for this purpose, the above Fig.12 At least one function of the AI / ML operation (particularly, model training, model inference) can be performed by the UE (or an external device installed in or connected to the UE).

[0463] In addition, according to the above-mentioned embodiment 3, when the CSI-RS density of each frequency region (e.g., subband, resource block, resource block group) in the frequency domain is different, the CSI reporting mode can be determined based on the CSI-RS density for each frequency region. In addition, the accuracy of channel measurement for each frequency region is predicted by the UE in the frequency domain, and the CSI reporting mode can be determined based on the accuracy of channel measurement for each frequency region.

[0464] In addition, the CSI reporting mode may be determined based on i) a difference between a reporting time of the CSI and a channel measurement time in the time domain or ii) a difference between a CSI reference resource and a channel measurement time.

[0465] Meanwhile, the operation of determining the CSI reporting mode according to the frequency region and / or the time region may be performed by the UE (or an external device installed on the UE or connected to the UE), and for this purpose, the above Fig.12 At least one function of the AI / ML operation (particularly model training, model inference) can be performed by the UE (or an external device installed on the UE or connected to the UE).

[0466] In addition, according to embodiment 1, based on the CSI reporting mode being determined as the second CSI reporting mode, the CSI may include some indexes constituting PMI, RI and CQI, and the CQI may be calculated by applying a precoder randomly selected from a precoder set determined by the UE for each resource unit determined by the UE (i.e., it may be assumed that the CQI is calculated using downlink transmission of the randomly selected precoder).

[0467] In addition, based on the CSI including both the first CSI according to the first CSI reporting mode and the second CSI according to the second CSI reporting mode, the second CSI can be reported only as a difference value from the first CSI.

[0468] Meanwhile, the operation of determining the PMI set (or pre-compiler set, cyclic set) and / or the operation of determining the pre-compiled resource unit (or cyclic resource unit) may be performed by the UE (or an external device installed on the UE or connected to the UE), and for this purpose, the above Fig.12 At least one function of the AI / ML operation (particularly, model training, model reasoning) can be performed by the UE (or an external device installed on or connected to the UE). In addition, the UE (or an external device installed on or connected to the UE) can also perform the above Fig.16 The process (particularly, model inference) determines a PMI set (or a precompiler set, a loop set) and / or a precompiler resource unit (or a loop resource unit).

[0469] In addition, according to Embodiment 2, CSI may include WB CSI and SB CSI, and a payload size for reporting a difference between SB CSI and WB CSI is determined by the UE, and CSI may include information on the payload size.

[0470] In addition, the CSI may include WB CSI and SB CSI, and each code point value for indicating a difference between the SB CSI and the WB CSI is determined by the UE, and the CSI may include information on each code point value.

[0471] Fig.18 is a diagram illustrating an operation of a UE for a method of transmitting and receiving channel state information according to an embodiment of the present disclosure.

[0472] refer to Fig.18 , Fig.18 It is to illustrate the operation of the UE based on the proposed method (for example, embodiments 1 to 4, a combination of one or more of the methods proposed in embodiments 1 to 4). Fig.18 The examples are for convenience of explanation and do not limit the scope of the present disclosure. Fig.18 Some of the steps illustrated in the figure may be omitted depending on the situation and / or setting. Fig.18 The UE in is only an example and can be implemented as follows Fig. 20 For example, Fig. 20 The processor (102 / 202) can control the transceiver (106 / 206) to send and receive channels / signals / data / information, etc., and can also control the storage of the sent or received channels / signals / data / information, etc. in the memory (104 / 204).

[0473] in addition, Fig.18 The operation can be done by Fig. 20 One or more processors (102, 202) of the processor, and Fig.18 The operation can be used to drive Fig. 20 The instructions / programs (eg, instructions, executable code) of at least one processor (eg, 102, 202) are stored in a memory (eg, Fig. 20 in one or more memories (104, 204)).

[0474] The UE receives configuration information related to channel state information (CSI) from a base station ( S1801 ).

[0475] The configuration information related to CSI may include the configuration information related to CSI described in the above-mentioned methods (eg, embodiments 1 to 4, or a combination of one or more of the methods proposed in embodiments 1 to 4).

[0476] The CSI-related configuration information may include at least one of information related to CSI-IM (interference management) resources, information related to CSI measurement configuration, information related to CSI resource configuration, or information related to CSI report configuration.

[0477] Here, the CSI-related configuration information may include configuration information related to CSI reporting (i.e., CSI reporting configuration) (e.g., RRC IE "CSI-ReportConfig") (hereinafter referred to as first configuration information) and configuration information related to CSI resources (i.e., CSI resource configuration) (e.g., RRC IE "CSI-ResourceConfig") (hereinafter referred to as second configuration information).

[0478] Here, multiple CSI-RS resources for CSI calculation / measurement can be configured for the UE through configuration information related to CSI resources (i.e., CSI resource settings). In addition, one or more channel measurement resources (CMRs) (e.g., NZP CSI-RS) and / or one or more interference measurement resources (IMRs) (e.g., CSI-IM and / or NZP CSI-RS) can be configured through configuration information related to CSI resources (i.e., CSI resource settings). CMRs and IMRs may be collectively referred to as CSI resources.

[0479] Additionally, configuration information related to CSI resources (ie, CSI resource settings) may be linked / associated with configuration information related to specific CSI reports (ie, CSI report settings).

[0480] The UE receives a CSI-RS from one or more CSI resources configured by configuration information (eg, CSI resource setting) ( S1802 ).

[0481] Here, according to the above-mentioned embodiment 3, the CSI-RS may have different densities in a frequency region (eg, subband, resource block, resource block group) by frequency domain (ie, the number of CSI-RS resources may be different).

[0482] Although Fig.18 Although not shown in the figure, the UE may receive downlink control information (DCI) from the base station. Here, the DCI may be a DCI that triggers aperiodic CSI reporting to the UE, or may be a DCI that activates semi-persistent CSI reporting. In addition, the DCI may be sent via a downlink control channel (e.g., PDCCH, etc.), and in addition to triggering / activating CSI reporting, may also schedule a downlink data channel (e.g., PDSCH) / uplink data channel (e.g., PUSCH).

[0483] The UE sends (reports) CSI to the base station ( S1803 ).

[0484] Here, the CSI may include at least one of CQI, PMI, RI, CRI, and LI.

[0485] As described above, the UE can report CSI to the network based on the configuration information related to CSI reporting (and according to the triggering / activation of DCI). For example, the CSI reporting type, such as periodic CSI reporting, semi-persistent CSI reporting, and aperiodic CSI reporting, can be configured through the configuration information related to CSI reporting, and in the case of periodic CSI reporting, the periodicity and time slot offset can also be configured.

[0486] According to the above-described embodiment 4, the UE can assume a specific CSI reporting mode to calculate the CSI, and in this case, the CSI can include information on the CSI reporting mode assumed by the UE to calculate the CSI.

[0487] For example, the PMI may include multiple indices for identifying a precoding matrix (e.g., indices i1 and i2 for identifying a precoding matrix in a predefined codebook), and the CSI reporting mode may be determined as one of the following: i) a first CSI reporting mode (i.e., a CL CSI reporting mode), in which the CSI includes multiple indices; ii) a second CSI reporting mode (i.e., a semi-OL CSI reporting mode), in which the CSI includes only some of the multiple indices (e.g., a single wideband indication for the entire CSI reporting band, i1); iii) a third CSI reporting mode, in which the CSI is a mixture of the first CSI reporting mode and the second CSI reporting mode in the frequency domain and / or time domain (i.e., a CSI reporting mode that mixes CL CSI and semi-OL CSI).

[0488] In addition, when the CSI reporting mode is determined to be the second CSI reporting mode or the third CSI reporting mode, a portion of the payload allocated for CSI that is not used according to the CSI reporting mode may be zero-filled or used to report additional information other than the PMI.

[0489] In addition, when the CSI reporting mode is determined to be the third CSI reporting mode, the CSI may include information about a ratio of information according to the first CSI reporting mode and / or information according to the second CSI reporting mode.

[0490] Meanwhile, the operation of determining the CSI reporting mode can be performed by the UE (or an external device installed or connected to the UE), and for this purpose, the above Fig.12 At least one function of the AI / ML operation (particularly model training, model inference) can be performed by the UE (or an external device installed on or connected to the UE).

[0491] In addition, according to the above-mentioned embodiment 3, when the CSI-RS density of each frequency region (e.g., subband, resource block, resource block group) in the frequency domain is different, the CSI reporting mode can be determined based on the density of the CSI-RS used for each frequency region. In addition, the accuracy of the channel measurement for each frequency region is predicted by the UE in the frequency domain, and the CSI reporting mode can be determined based on the accuracy of the channel measurement for each frequency region.

[0492] In addition, the CSI reporting mode may be determined based on i) a difference between a reporting time of the CSI and a channel measurement time in the time domain or ii) a difference between a CSI reference resource and a channel measurement time.

[0493] Meanwhile, the operation of determining the CSI reporting mode according to the frequency region and / or the time region may be performed by the UE (or an external device installed on the UE or connected to the UE), and for this purpose, the above Fig.12 At least one function of the AI / ML operation (particularly model training, model inference) can be performed by the UE (or an external device installed on the UE or connected to the UE).

[0494] In addition, according to embodiment 1, based on the CSI reporting mode being determined as the second CSI reporting mode, the CSI may include some indexes constituting PMI, RI and CQI, and the CQI may be calculated by applying a precoder randomly selected from a precoder set determined by the UE for each resource unit determined by the UE (i.e., it may be assumed that the CQI is calculated using downlink transmission of the randomly selected precoder).

[0495] In addition, based on the CSI including both the first CSI according to the first CSI reporting mode and the second CSI according to the second CSI reporting mode, the second CSI can be reported only as a difference value from the first CSI.

[0496] Meanwhile, the operation of determining the PMI set (or pre-compiler set, cyclic set) and / or the operation of determining the pre-compiled resource unit (or cyclic resource unit) may be performed by the UE (or an external device installed on the UE or connected to the UE), and for this purpose, the above Fig.12 At least one function of the AI / ML operation (particularly, model training, model reasoning) of the UE can be performed by the UE (or an external device installed on the UE or connected to the UE). In addition, the UE (or an external device installed on the UE or connected to the UE) can also perform the above Fig.16 The process (particularly, 5. Model Reasoning) determines the PMI set (or precompiler set, loop set) and / or precompiler resource unit (or loop resource unit).

[0497] In addition, according to Embodiment 2, CSI may include WB CSI and SB CSI, and a payload size for reporting a difference between SB CSI and WB CSI is determined by the UE, and CSI may include information on the payload size.

[0498] In addition, the CSI may include WB CSI and SB CSI, and each code point value for indicating a difference between the SB CSI and the WB CSI is determined by the UE, and the CSI may include information on each code point value.

[0499] Fig.19 is a diagram illustrating the operation of a base station according to a method for transmitting and receiving channel state information according to an embodiment of the present disclosure.

[0500] refer to Fig.19 , Fig.19 The operation of the base station based on the proposed method (eg, a combination of one or more of the methods proposed in Embodiments 1 to 4 or Embodiments 1 to 4) is illustrated. Fig.19 The examples are for illustrative purposes only and do not limit the scope of the present disclosure. Fig.19 Some of the steps illustrated in the figure may be omitted depending on the situation and / or setting. Fig.19 The base station in is only an example and can be implemented as follows Fig. 20 For example, Fig. 20 The processor (102 / 202) can control the transceiver (106 / 206) to transmit and receive channels / signals / data / information, etc., and can also control to store the transmitted or received channels / signals / data / information, etc. in the memory (104 / 204).

[0501] in addition, Fig.19 The operation can be done by Fig. 20 One or more processors (102, 202) process, and Fig.19 The operation can be done by Fig. 20 One or more processors (102, 202) process. 19 can be used to drive Fig. 20 The instructions / programs (eg, instructions, executable code) of at least one processor (eg, 102, 202) are stored in a memory (eg, Fig. 20 in one or more memories (104, 204)).

[0502] The base station sends configuration information related to channel state information (CSI) to the UE (S1901).

[0503] The configuration information related to CSI may include the configuration information related to CSI described in the above-mentioned methods (eg, embodiments 1 to 4, or a combination of one or more of the methods proposed in embodiments 1 to 4).

[0504] The CSI-related configuration information may include at least one of information related to CSI-IM (interference management) resources, information related to CSI measurement configuration, information related to CSI resource configuration, or information related to CSI report configuration.

[0505] Here, the CSI-related configuration information may include configuration information related to CSI reporting (i.e., CSI reporting configuration) (e.g., RRC IE "CSI-ReportConfig") (hereinafter referred to as first configuration information) and configuration information related to CSI resources (i.e., CSI resource configuration) (e.g., RRC IE "CSI-ResourceConfig") (hereinafter referred to as second configuration information).

[0506] Here, multiple CSI-RS resources for CSI calculation / measurement can be configured for the UE through the configuration information related to the CSI resource (i.e., CSI resource setting). In addition, one or more channel measurement resources (CMRs) (e.g., NZP CSI-RS) and / or one or more interference measurement resources (IMRs) (e.g., CSI-IM and / or NZP CSI-RS) can be configured through the configuration information related to the CSI resource (i.e., CSI resource setting). CMRs and IMRs may be collectively referred to as CSI resources.

[0507] Additionally, configuration information related to CSI resources (ie, CSI resource settings) may be linked / associated with configuration information related to specific CSI reports (ie, CSI report settings).

[0508] The base station transmits CSI-RS from one or more CSI resources configured by configuration information (eg, CSI resource setting) to the UE ( S1902 ).

[0509] Here, according to the above-mentioned embodiment 3, the CSI-RS may have different densities in a frequency region (eg, subband, resource block, resource block group) by frequency domain (ie, the number of CSI-RS resources may be different).

[0510] Although Fig.18 Although not shown in the figure, the base station is able to send downlink control information (DCI) to the UE. Here, the DCI can be a DCI that triggers aperiodic CSI reporting to the UE, or it can be a DCI that activates semi-persistent CSI reporting. In addition, the DCI can be sent via a downlink control channel (e.g., PDCCH, etc.), and in addition to triggering / activating CSI reporting, it can also schedule a downlink data channel (e.g., PDSCH) / uplink data channel (e.g., PUSCH).

[0511] The base station receives CSI from the UE ( S1903 ).

[0512] Here, the CSI may include at least one of CQI, PMI, RI, CRI, and LI.

[0513] As described above, CSI can be reported from the UE based on the configuration information related to CSI reporting (and when triggered / activated by DCI). For example, the CSI reporting type, such as periodic CSI reporting, semi-persistent CSI reporting, and aperiodic CSI reporting, can be configured through the configuration information related to CSI reporting, and in the case of periodic CSI reporting, the periodicity and time slot offset, etc. can also be configured.

[0514] According to the above-mentioned embodiment 4, the UE may assume a specific CSI reporting mode to calculate the CSI, and in this case, the CSI may include information on the CSI reporting mode assumed by the UE to calculate the CSI.

[0515] For example, the PMI may include multiple indices for identifying a precoding matrix (e.g., indices i1 and i2 for identifying a precoding matrix in a predefined codebook), and the CSI reporting mode may be determined as one of the following: i) a first CSI reporting mode (i.e., a CL CSI reporting mode), in which the CSI includes multiple indices; ii) a second CSI reporting mode (i.e., a semi-OL CSI reporting mode), in which the CSI includes only some of the multiple indices (e.g., a single wideband indication for the entire CSI reporting band, i1); iii) a third CSI reporting mode, in which the CSI is a mixture of the first CSI reporting mode and the second CSI reporting mode in the frequency domain and / or time domain (i.e., a CSI reporting mode that mixes CL CSI and semi-OL CSI).

[0516] In addition, when the CSI reporting mode is determined to be the second CSI reporting mode or the third CSI reporting mode, a portion of the payload allocated for CSI that is not used according to the CSI reporting mode may be zero-filled or used to report additional information other than the PMI.

[0517] In addition, when the CSI reporting mode is determined to be the third CSI reporting mode, the CSI may include information about a ratio of information according to the first CSI reporting mode and / or information according to the second CSI reporting mode.

[0518] Meanwhile, the operation of determining the CSI reporting mode can be performed by the UE (or an external device installed or connected to the UE), and for this purpose, the above Fig.12 At least one function of the AI / ML operation (particularly model training, model inference) can be performed by the UE (or an external device installed on or connected to the UE).

[0519] In addition, according to the above-mentioned embodiment 3, when the CSI-RS density of each frequency region (e.g., subband, resource block, resource block group) in the frequency domain is different, the CSI reporting mode can be determined based on the density of the CSI-RS used for each frequency region. In addition, the accuracy of the channel measurement for each frequency region is predicted by the UE in the frequency domain, and the CSI reporting mode can be determined based on the accuracy of the channel measurement for each frequency region.

[0520] In addition, the CSI reporting mode may be determined based on i) a difference between a reporting time of the CSI and a channel measurement time in the time domain or ii) a difference between a CSI reference resource and a channel measurement time.

[0521] Meanwhile, the operation of determining the CSI reporting mode according to the frequency region and / or the time region may be performed by the UE (or an external device installed on the UE or connected to the UE), and for this purpose, the above Fig.12At least one function of the AI / ML operation (particularly model training, model inference) can be performed by the UE (or an external device installed on the UE or connected to the UE).

[0522] In addition, according to embodiment 1, based on the CSI reporting mode being determined as the second CSI reporting mode, the CSI may include some indexes constituting PMI, RI and CQI, and the CQI may be calculated by applying a precoder randomly selected from a precoder set determined by the UE for each resource unit determined by the UE (i.e., it may be assumed that the CQI is calculated using downlink transmission of the randomly selected precoder).

[0523] In addition, based on the CSI including both the first CSI according to the first CSI reporting mode and the second CSI according to the second CSI reporting mode, the second CSI can be reported only as a difference value from the first CSI.

[0524] Meanwhile, the operation of determining the PMI set (or pre-compiler set, cyclic set) and / or the operation of determining the pre-compiled resource unit (or cyclic resource unit) may be performed by the UE (or an external device installed on the UE or connected to the UE), and for this purpose, the above Fig.12 At least one function of the AI / ML operation (particularly, model training, model reasoning) of the UE can be performed by the UE (or an external device installed on the UE or connected to the UE). In addition, the UE (or an external device installed on the UE or connected to the UE) can also perform the above Fig.16 The process (particularly, 5. Model Reasoning) determines the PMI set (or precompiler set, loop set) and / or precompiler resource unit (or loop resource unit).

[0525] In addition, according to Embodiment 2, CSI may include WB CSI and SB CSI, and a payload size for reporting a difference between SB CSI and WB CSI is determined by the UE, and CSI may include information on the payload size.

[0526] In addition, the CSI may include WB CSI and SB CSI, and each code point value for indicating a difference between the SB CSI and the WB CSI is determined by the UE, and the CSI may include information on each code point value.

[0527] General equipment to which the present disclosure can be applied

[0528] Fig. 20 is a diagram illustrating a block diagram of a wireless communication device according to an embodiment of the present disclosure.

[0529] refer to Fig. 20, the first wireless device 100 and the second wireless device 200 can send and receive wireless signals through a variety of radio access technologies (e.g., LTE, NR).

[0530] The first wireless device 100 may include 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 may control the memory 104 and / or the transceiver 106 and may be configured to implement the description, function, process, proposal, method and / or operation flowchart disclosed in the present disclosure. For example, the processor 102 may send a wireless signal including the first information / signal through the transceiver 106 after generating the first information / signal by processing the information in the memory 104. In addition, the processor 102 may receive a wireless signal including the second information / signal through the transceiver 106, and then store the information obtained by signal processing of the second information / signal in the memory 104. The memory 104 may be connected to the processor 102 and may store various information related to the operation of the processor 102. For example, the memory 104 may store software code including commands for executing all or part of the process controlled by the processor 102 or for executing the description, function, process, proposal, method and / or operation flowchart 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 wireless communication technology (e.g., LTE, NR). The transceiver 106 may be connected to the processor 102 and may send and / or receive wireless signals through one or more antennas 108. The transceiver 106 may include a transmitter and / or a receiver. The transceiver 106 may be used in conjunction with an RF (radio frequency) unit. In the present disclosure, a wireless device may refer to a communication modem / circuit / chip.

[0531] The second wireless device 200 may include one or more processors 202 and one or more memories 204, and may further include one or more transceivers 206 and / or one or more antennas 208. The processor 202 may control the memory 204 and / or the transceiver 206 and may be configured to implement the description, function, process, proposal, method and / or operation flowchart disclosed in the present disclosure. For example, the processor 202 may generate third information / signal by processing the information in the memory 204, and then transmit the wireless signal including the third information / signal through the transceiver 206. In addition, the processor 202 may receive a wireless signal including fourth information / signal through the transceiver 206, and then store the information obtained by signal processing of the fourth information / signal in the memory 204. The memory 204 may be connected to the processor 202 and may store various information related to the operation of the processor 202. For example, the memory 204 may store software code including commands for executing all or part of the process controlled by the processor 202 or for executing the description, function, process, proposal, method and / or operation flowchart 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 wireless communication technology (e.g., LTE, NR). The transceiver 206 may be connected to the processor 202 and may send and / or receive wireless signals through one or more antennas 208. The transceiver 206 may include a transmitter and / or a receiver. The transceiver 206 may be used in conjunction with an RF unit. In the present disclosure, a wireless device may refer to a communication modem / circuit / chip.

[0532] In the following, the hardware elements of the wireless device 100, 200 will be described in more detail. It is not limited thereto, and 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 PDUs (protocol data units) and / or one or more SDUs (service data units) according to the descriptions, functions, processes, proposals, methods, and / or operational flow charts included in the present disclosure. One or more processors 102, 202 may generate messages, control information, data, or information according to the descriptions, functions, processes, proposals, methods, and / or operational flow charts disclosed in the present disclosure. One or more processors 102, 202 may generate signals (e.g., baseband signals) including PDUs, SDUs, messages, control information, data, or information according to the functions, processes, proposals, and / or methods disclosed in the present disclosure to provide them to one or more transceivers 106, 206. The one or more processors 102, 202 may receive signals (e.g., baseband signals) from the one or more transceivers 106, 206 and obtain PDUs, SDUs, messages, control information, data, or information according to the descriptions, functions, processes, proposals, methods, and / or operational flow charts disclosed in the present disclosure.

[0533] One or more processors 102, 202 may be referred to as controllers, microcontrollers, microprocessors, or microcomputers. One or more processors 102, 202 may be implemented by hardware, firmware, software, or a combination thereof. In an example, one or more ASICs (Application Specific Integrated Circuits), one or more DSPs (Digital Signal Processors), one or more DSPDs (Digital Signal Processing Devices), one or more PLDs (Programmable Logic Devices), or one or more FPGAs (Field Programmable Gate Arrays) may be included in one or more processors 102, 202. The description, functions, processes, proposals, methods, and / or operational flowcharts disclosed in the present disclosure may be implemented using firmware or software and the firmware or software may be implemented to include modules, processes, functions, etc. Firmware or software configured to execute the description, functions, processes, proposals, methods, and / or operational flowcharts disclosed in the present disclosure may be included in one or more processors 102, 202 or may be stored in one or more memories 104, 204 and driven by one or more processors 102, 202. The description, functions, processes, proposals, methods, and / or operational flowcharts disclosed in the present disclosure may be implemented by firmware or software in the form of codes, commands, and / or command sets.

[0534] One or more memories 104, 204 may be connected to one or more processors 102, 202 and may store data, signals, messages, information, programs, codes, instructions, and / or commands in various forms. One or more memories 104, 204 may be configured with ROM, RAM, EPROM, flash memory, hard drive, registers, cache memory, computer-readable storage media, and / or combinations thereof. One or more memories 104, 204 may be located inside and / or outside one or more processors 102, 202. In addition, one or more memories 104, 204 may be connected to one or more processors 102, 202 via a variety of technologies such as wired or wireless connections.

[0535] One or more transceivers 106, 206 may send user data, control information, wireless signals / channels, etc. mentioned in the methods and / or operation flow charts, etc. of the present disclosure to one or more other devices. One or more transceivers 106, 206 may receive user data, control information, wireless signals / channels, etc. mentioned in the description, function, process, proposal, method and / or operation flow chart, etc. disclosed in the present disclosure from one or more other devices. For example, one or more transceivers 106, 206 may be connected to one or more processors 102, 202 and may send and receive wireless signals. For example, one or more processors 102, 202 may control one or more transceivers 106, 206 to send user data, control information or wireless signals to one or more other devices. In addition, 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. In addition, one or more transceivers 106, 206 may be connected to one or more antennas 108, 208, and one or more transceivers 106, 206 may be configured to send and receive user data, control information, wireless signals / channels, etc. mentioned in the description, functions, processes, proposals, methods and / or operation flow charts disclosed in the present disclosure through one or more antennas 108, 208. In the present invention, one or more antennas may be multiple physical antennas or multiple logical antennas (e.g., antenna ports). One or more transceivers 106, 206 may convert received wireless signals / channels, etc. from RF band signals to baseband signals by using one or more processors 102, 202 to process received user data, control information, wireless signals / channels, etc. One or more transceivers 106, 206 may convert user data, control information, wireless signals / channels, etc. processed by using one or more processors 102, 202 from baseband signals to RF band signals. Therefore, one or more transceivers 106, 206 may include (analog) oscillators and / or filters.

[0536] The above-mentioned embodiments are elements and features of the present disclosure combined in a predetermined form. Unless otherwise explicitly mentioned, each element or feature should be considered optional. Each element or feature can be implemented in a form that is not combined with other elements or features. In addition, the embodiments of the present disclosure may include combined partial elements and / or features. The order of the operations described in the embodiments of the present disclosure may be changed. Some elements or features of an embodiment may be included in other embodiments, or may be replaced by corresponding elements or features of other embodiments. It is clear that an embodiment may include a combined claim without an explicit dependency in the claim, or may be included as a new claim by modification after application.

[0537] It is clear to those skilled in the art that the present disclosure can be implemented in other specific forms within the scope of the essential features of the present disclosure. Therefore, the above detailed description should not be interpreted restrictively in every aspect, but should be considered as illustrative. The scope of the present invention should be determined by the reasonable interpretation of the attached claims, and all changes within the equivalent scope of the present disclosure are included in the scope of the present invention.

[0538] The scope of the present disclosure includes software or machine executable commands (e.g., operating systems, applications, firmware, programs, etc.) that perform operations in a device or computer according to the methods of various embodiments, and non-transitory computer-readable media that store such software or commands, etc. and can be executed in a device or computer. Commands that can be used to program a processing system that performs the features described in the present disclosure can be stored in a storage medium or a computer-readable storage medium, and the features described in the present disclosure can be implemented by using a computer program product that includes such a storage medium. The storage medium may include a high-speed random access memory, such as DRAM, SRAM, DDR RAM, or other random access solid-state storage devices, but is not limited thereto, and it may 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 located away from the processor. The memory or alternatively, the non-volatile memory device in the memory includes a non-transitory computer-readable storage medium. The features described in this disclosure may be stored in any machine-readable medium to control the hardware of a processing system, and may be integrated into software and / or firmware that allows the processing system to interact with other mechanisms using the results from 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 devices 100 and 200 of the present disclosure may include narrowband Internet of Things for low-power communication, as well as LTE, NR and 6G. Here, for example, NB-IoT technology may be an example of LPWAN (Low Power Wide Area Network) technology, which may be implemented in standards such as LTE Cat NB1 and / or LTE Cat NB2, and is not limited to the above names. In addition or alternatively, the wireless communication technology implemented in the wireless devices 100 and 200 of the present disclosure may perform communication based on LTE-M technology. Here, in the example, LTE-M technology may be an example of LPWAN technology and may be referred to as various names such as eMTC (enhanced machine type communication). For example, LTE-M technology may be implemented in at least any one of the various standards including 1) LTE CAT 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-BL (non-bandwidth limited), 5) LTE-MTC, 6) LTE machine type communication, and / or 7) LTE M, etc., and is not limited to the above names. Additionally or alternatively, the wireless communication technology implemented in the wireless devices 100, 200 of the present disclosure may include at least any one of ZigBee, Bluetooth, and a low power wide area network (LPWAN) considering low power communication, and it is not limited to the above names. In an example, the ZigBee technology may generate a PAN (Personal Area Network) related to small / low power digital communication based on various standards such as IEEE 802.15.4, etc., and may be referred to by various names.

[0540] Industrial availability

[0541] The method proposed in the present invention is mainly described by taking the application in 3GPP LTE / LTE-A and 5G systems as an example, but can also be applied to various wireless communication systems other than 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: receiving configuration information related to channel state information (CSI) from a base station; receiving a CSI reference signal (CSI-RS) on one or more CSI resources from the base station; as well as sending the CSI to the base station based on the configuration information, The CSI includes information of a CSI reporting mode assumed by the UE to calculate the CSI.

2. The method according to claim 1, wherein: A precoding matrix indicator (PMI) includes a plurality of indexes for identifying a precoding matrix, and The CSI reporting mode is determined as one of the following: i) a first CSI reporting mode, wherein the CSI includes the multiple indexes; ii) a second CSI reporting mode, wherein the CSI includes only some of the multiple indexes; and iii) a third CSI reporting mode, wherein the CSI is a mixture of the first CSI reporting mode and the second CSI reporting mode according to the frequency domain and / or time domain.

3. The method according to claim 2, wherein: Based on the CSI reporting mode being determined as one of the second CSI reporting mode or the third CSI reporting mode, a payload portion unused due to the CSI reporting mode in the payload allocated for the CSI is zero-filled or used to report additional information other than the PMI.

4. The method according to claim 2, wherein: Based on the CSI reporting mode being determined as the third CSI reporting mode, the CSI includes information of a ratio of the information used in the first CSI reporting mode and / or the information used in the second CSI reporting mode.

5. The method according to claim 2, wherein: The CSI-RS has different density for each frequency region in the frequency domain, and The CSI reporting mode is determined based on the density of the CSI-RS for each frequency region.

6. The method according to claim 2, wherein: Predicting the accuracy of the channel measurement for each frequency region in the frequency domain, and The CSI reporting mode is determined based on the accuracy of the channel measurement for each frequency region.

7. The method according to claim 2, wherein: The CSI reporting mode is determined based on i) a difference between a reporting time of the CSI and a channel measurement time or ii) a difference between a CSI reference resource and a channel measurement time in a time domain.

8. The method according to claim 2, wherein: Based on the CSI reporting mode being determined as the second CSI reporting mode, the CSI includes some indexes constituting the PMI, a rank indicator (RI), and a channel quality indicator (CQI), and The CQI is calculated by applying a precoder randomly selected from a set of precoders determined by the UE to each resource unit determined by the UE.

9. The method according to claim 2, wherein: Based on the CSI including both the first CSI related to the first CSI reporting mode and the second CSI related to the second CSI reporting mode, the second CSI is reported only as a difference from the first CSI.

10. The method according to claim 2, wherein: The CSI includes wideband (WB) CSI and subband (SB) CSI, and The payload size for reporting the difference between the SB CSI and the WB CSI is determined by the UE, and the CSI includes information for the payload size.

11. The method according to claim 2, wherein: The CSI includes wideband (WB) CSI and subband (SB) CSI, and Each code point value indicating a difference between the SB CSI and the WB CSI is determined by the UE, and the CSI includes information for each code point value.

12. A user equipment (UE) operating in a wireless communication system, the UE comprising: at least one transceiver, the at least one transceiver configured to send and receive wireless signals; as well as at least one processor configured to control the at least one transceiver, Wherein, the at least one processor is configured to: receiving configuration information related to channel state information (CSI) from a base station; receiving a CSI reference signal (CSI-RS) on one or more CSI resources from the base station; and sending the CSI to the base station based on the configuration information, The CSI includes information of a CSI reporting mode assumed by the UE to calculate the CSI.

13. At least one non-transitory computer-readable medium storing at least one instruction, wherein: The at least one instruction executable by the at least one processor controls a user equipment (UE) to: receiving configuration information related to channel state information (CSI) from a base station; receiving a CSI reference signal (CSI-RS) on one or more CSI resources from the base station; and sending the CSI to the base station based on the configuration information, The CSI includes information of a CSI reporting mode assumed by the UE to calculate the CSI.

14. A processing device, the processing device being configured to control a user equipment (UE) in a wireless communication system, the processing device comprising: at least one processor; as well as At least one computer memory operably connected to the at least one processor and storing instructions that upon execution by the at least one processor perform operations comprising: receiving configuration information related to channel state information (CSI) from a base station; receiving a CSI reference signal (CSI-RS) on one or more CSI resources from the base station; as well as sending the CSI to the base station based on the configuration information, The CSI includes information of a CSI reporting mode assumed by the UE to calculate the CSI.

15. A method performed by a base station in a wireless communication system, the method comprising: Sending configuration information related to channel state information (CSI) to a user equipment (UE); sending a CSI reference signal (CSI-RS) on one or more CSI resources to the UE; and receiving the CSI from the UE, The CSI includes information of a CSI reporting mode assumed by the UE to calculate the CSI.

16. A base station operating in a wireless communication system, the base station comprising: at least one transceiver, the at least one transceiver configured to send and receive wireless signals; as well as at least one processor configured to control the at least one transceiver, Wherein, the at least one processor is configured to: Sending configuration information related to channel state information (CSI) to a user equipment (UE); sending a CSI reference signal (CSI-RS) on one or more CSI resources to the UE; and receiving the CSI from the UE, The CSI includes information of a CSI reporting mode assumed by the UE to calculate the CSI.

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