Method by which apparatus performs communication in wireless communication system and apparatus therefor

Abstract

Disclosed are a method for performing communication in a wireless communication system, and an apparatus therefor according to various embodiments. The apparatus may receive monitoring configuration information related to channel state information (CSI) prediction, perform monitoring for performance evaluation of the CSI prediction performed on the basis of a CSI report configuration associated with the monitoring configuration information, and transmit prediction accuracy information to a base station on the basis of a result of the monitoring.

Description

A method for a device to perform communication in a wireless communication system and a device for the same

[0001] This relates to a method for a terminal to perform CSI monitoring in a wireless communication system and a device for doing so.

[0002] A wireless communication system is a multiple access system that supports communication with multiple users by sharing available system resources (e.g., bandwidth, transmission power, etc.). Examples of multiple access systems include CDMA (code division multiple access), FDMA (frequency division multiple access), TDMA (time division multiple access), OFDMA (orthogonal frequency division multiple access), SC-FDMA (single carrier frequency division multiple access), and MC-FDMA (multi carrier frequency division multiple access) systems.

[0003] Sidelink (SL) refers to a communication method in which User Equipment (UE) establishes a direct link to directly exchange voice or data between terminals without passing through a Base Station (BS). SL is being considered as a solution to address the burden on base stations caused by rapidly increasing data traffic.

[0004] V2X (vehicle-to-everything) refers to a communication technology that exchanges information with other vehicles, pedestrians, and infrastructure-equipped objects through wired or wireless communication. V2X can be classified into four types: V2V (vehicle-to-vehicle), V2I (vehicle-to-infrastructure), V2N (vehicle-to-network), and V2P (vehicle-to-pedestrian). V2X communication can be provided through PC5 interfaces and / or Uu interfaces.

[0005] Meanwhile, as more communication devices require larger communication capacities, the need for improved mobile broadband communication compared to existing Radio Access Technology (RAT) is emerging. Accordingly, communication systems considering services or terminals sensitive to reliability and latency are being discussed; next-generation radio access technology that incorporates improved mobile broadband communication, Massive Machine Type Communication (MTC), and Ultra-Reliable and Low Latency Communication (URLC) can be referred to as new radio access technology (new RAT) or new radio (NR). Vehicle-to-everything (V2X) communication can also be supported in NR.

[0006] Figure 1 is a diagram illustrating a comparison between V2X communication based on RAT prior to NR and V2X communication based on NR.

[0007] Regarding V2X communication, prior to NR, RATs mainly discussed methods for providing safety services based on V2X messages such as BSM (Basic Safety Message), CAM (Cooperative Awareness Message), and DENM (Decentralized Environmental Notification Message). V2X messages can include location information, dynamic information, attribute information, etc. For example, a terminal can transmit a CAM of the periodic message type and / or a DENM of the event-triggered message type to another terminal.

[0008] For example, the CAM may include basic vehicle information such as dynamic state information of the vehicle, such as direction and speed, static data of the vehicle, such as dimensions, external lighting conditions, and route history. For example, a terminal may broadcast the CAM, and the latency of the CAM may be less than 100ms. For example, in the event of an unexpected situation such as a vehicle breakdown or accident, the terminal may generate a DENM and transmit it to other terminals. For example, all vehicles within the transmission range of the terminal may receive the CAM and / or DENM. In this case, the DENM may have a higher priority than the CAM.

[0009] Since then, regarding V2X communication, various V2X scenarios have been presented in NR. For example, various V2X scenarios may include vehicle platooning, advanced driving, extended sensors, remote driving, etc.

[0010] For example, based on vehicle platooning, vehicles can dynamically form groups and move together. For example, to perform platoon operations based on vehicle platooning, vehicles belonging to said group can receive periodic data from the lead vehicle. For example, vehicles belonging to said group can use said periodic data to reduce or increase the distance between vehicles.

[0011] For example, based on enhanced driving, vehicles can be semi-automated or fully automated. For example, each vehicle can adjust trajectories or maneuvers based on data acquired from local sensors of nearby vehicles and / or nearby logical entities. Additionally, for example, each vehicle can mutually share driving intentions with nearby vehicles.

[0012] For example, based on extended sensors, raw data or processed data or live video data acquired through local sensors can be exchanged between vehicles, logical entities, pedestrian terminals and / or V2X application servers. Thus, for example, a vehicle can perceive an environment that is enhanced compared to the environment it can detect using its own sensors.

[0013] For example, based on remote driving, a remote driver or V2X application can operate or control a remote vehicle for a person unable to drive or for a remote vehicle located in a dangerous environment. For example, in cases where the route is predictable, such as in public transportation, cloud computing-based driving can be used for the operation or control of the remote vehicle. Additionally, access to a cloud-based back-end service platform, for example, can be considered for remote driving.

[0014] Meanwhile, methods to specify service requirements for various V2X scenarios, such as vehicle platooning, enhanced driving, extended sensors, and remote driving, are being discussed in NR-based V2X communication.

[0015] The technical problem that the present invention aims to solve is to provide a method for performing CSI monitoring more accurately and efficiently.

[0016] The technical problems are not limited to those mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art to which the present invention belongs from the description below.

[0017] A method by a UE (User Equipment) according to one aspect comprises: receiving monitoring setting information related to CSI prediction (channel state information prediction); performing monitoring for evaluating the performance of said CSI prediction based on a CSI reporting setting associated with said monitoring setting information; and transmitting prediction accuracy information to a base station based on the result of said monitoring. The CSI reporting setting associated with said monitoring setting information may be either a first CSI reporting setting for AI / ML (artificial intelligence / machine learning) based CSI prediction or a second CSI reporting setting for non-AI / ML based CSI prediction.

[0018] Alternatively, the monitoring setting information may include a CSI reporting setting ID (identifier) ​​that identifies the associated CSI reporting setting.

[0019] Alternatively, the above monitoring setting information may include information about the monitoring resource on which monitoring is performed.

[0020] Alternatively, based on the fact that the CSI reporting setting associated with the above monitoring setting information is the second CSI reporting setting, the prediction accuracy information may include a squared generalized cosine similarity (SGCS) value or a normalized mean squared error (NMSE) value calculated based on the predicted value according to the non-AI / ML-based CSI prediction and the actual measured value of the monitoring resource.

[0021] Alternatively, the prediction accuracy information may further include information on the number of times the SGCS value below the first performance threshold is calculated or the number of times the NMSE value exceeding the second performance threshold is calculated, and the first performance threshold and the second performance threshold may be included in the monitoring setting information or pre-set.

[0022] Alternatively, it may further include a step of reporting to the base station information regarding the preferred prediction method among the AI / ML-based CSI prediction or the non-AI / ML-based CSI prediction based on the prediction accuracy information.

[0023] Alternatively, the above CSI reporting setting may include instruction information regarding whether it is the first CSI reporting setting or the second CSI reporting setting.

[0024] Alternatively, the method may further include a step of performing a report on an applicable functionality related to the above CSI prediction, and the UE may determine the CSI reporting setting associated with the monitoring setting information as the first CSI reporting setting or the second CSI reporting setting based on the reporting time of the applicable functionality.

[0025] Alternatively, based on the fact that the CSI reporting setting associated with the above monitoring setting information is activated or triggered after the reporting time of the applicable function, the UE may determine the CSI reporting setting associated with the above monitoring setting information as the first CSI reporting setting.

[0026] According to another aspect, at least one non-transient computer-readable recording medium comprises instructions for performing operations when executed by at least one processor, said operations include receiving monitoring setting information related to CSI prediction (channel state information prediction); performing monitoring for evaluating the performance of said CSI prediction based on a CSI reporting setting associated with said monitoring setting information; and transmitting prediction accuracy information to a base station based on the result of said monitoring, said CSI reporting setting associated with said monitoring setting information may be either a first CSI reporting setting for AI / ML (artificial intelligence / machine learning) based CSI prediction or a second CSI reporting setting for non-AI / ML based CSI prediction.

[0027] According to another aspect, a UE (User Equipment) includes an RF (Radio Frequency) transceiver; and a processor connected to the RF transceiver, wherein the processor controls the RF transceiver to receive monitoring setting information related to CSI prediction (channel state information prediction), performs monitoring for evaluating the performance of said CSI prediction based on a CSI reporting setting associated with said monitoring setting information, transmits prediction accuracy information to a base station based on the results of said monitoring, and the said CSI reporting setting associated with said monitoring setting information may be either a first CSI reporting setting for AI / ML (artificial intelligence / machine learning) based CSI prediction or a second CSI reporting setting for non-AI / ML based CSI prediction.

[0028] According to another aspect, a processing device controlling a UE (User Equipment) comprises at least one processor; and at least one memory connected to the at least one processor and storing instructions that perform operations when executed by the at least one processor, wherein the operations include causing the UE to: receive monitoring setting information related to CSI prediction (channel state information prediction); perform monitoring for evaluating the performance of said CSI prediction based on a CSI reporting setting associated with said monitoring setting information; and transmit prediction accuracy information to a base station based on the result of said monitoring, wherein the said CSI reporting setting associated with said monitoring setting information may be either a first CSI reporting setting for AI / ML (artificial intelligence / machine learning) based CSI prediction or a second CSI reporting setting for non-AI / ML based CSI prediction.

[0029] A method by a base station according to another aspect comprises the steps of: transmitting monitoring setting information related to CSI prediction (channel state information prediction) to a UE (User Equipment); and receiving from the UE prediction accuracy information regarding the monitoring result of a performance evaluation of the CSI prediction performed based on a CSI reporting setting associated with the monitoring setting information, wherein the CSI reporting setting associated with the monitoring setting information may be either a first CSI reporting setting for AI / ML (artificial intelligence / machine learning) based CSI prediction or a second CSI reporting setting for non-AI / ML based CSI prediction.

[0030] A base station according to another aspect includes a Radio Frequency (RF) transceiver; and a processor connected to the RF transceiver, wherein the processor controls the RF transceiver to transmit monitoring setting information related to channel state information prediction to a User Equipment (UE), and receives from the UE prediction accuracy information regarding the monitoring result of a performance evaluation of the CSI prediction performed based on a CSI reporting setting associated with the monitoring setting information, and the CSI reporting setting associated with the monitoring setting information may be either a first CSI reporting setting for AI / ML (artificial intelligence / machine learning) based CSI prediction or a second CSI reporting setting for non-AI / ML based CSI prediction.

[0031] According to one embodiment of the present invention, CSI monitoring can be performed more accurately and efficiently in a wireless communication system. For example, by supporting monitoring for non-AI / ML-based CSI prediction, a base station can be provided with an opportunity to quantitatively determine the performance of a non-AI / ML-based CSI prediction method.

[0032] The effects obtainable from various embodiments are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art from the description below.

[0033] The drawings attached to this specification are intended to provide an understanding of the present invention, to illustrate various embodiments of the invention, and to explain the principles of the invention together with the description in the specification.

[0034] Figure 1 is a diagram illustrating a comparison between V2X communication based on RAT prior to NR and V2X communication based on NR.

[0035] Figure 2 shows the structure of an LTE system.

[0036] Figure 3 shows the structure of the NR system.

[0037] Figure 4 shows the structure of a wireless frame of NR.

[0038] Figure 5 shows the slot structure of an NR frame.

[0039] FIG. 6 shows a communication structure that can be provided in a 6G system according to one embodiment of the present disclosure.

[0040] FIG. 7 shows an electromagnetic spectrum according to one embodiment of the present disclosure.

[0041] Figure 8 shows the radio protocol architecture for SL communication.

[0042] Figure 9 shows a terminal performing V2X or SL communication.

[0043] Figure 10 shows a resource unit for V2X or SL communication.

[0044] FIG. 11 shows an example of a BWP according to one embodiment of the present disclosure.

[0045] FIG. 12 illustrates a procedure in which a terminal performs V2X or SL communication according to a resource allocation mode, according to one embodiment of the present disclosure.

[0046] FIGS. 13 and FIGS. 14 illustrate an example of a sensing operation according to an embodiment of the present disclosure.

[0047] FIG. 15 illustrates a time / frequency resource for a sensing operation according to one embodiment of the present specification.

[0048] Figure 16 illustrates a general functional architecture for an AI / ML model.

[0049] Figure 17 is a diagram illustrating an inference method during AI / ML-based CSI compression.

[0050] Figure 18 is a diagram illustrating a method for performing AI / ML-based CSI prediction.

[0051] Figure 19 is a diagram illustrating a signaling procedure related to the reporting of capability information and applicability in relation to AI / ML-based CSI prediction.

[0052] Figures 20 and 21 are diagrams illustrating the signaling procedure between a base station and a terminal in relation to the reporting of predicted CSI.

[0053] Figure 22 is a diagram illustrating how a UE reports monitoring information related to CSI prediction to a base station.

[0054] FIG. 23 is a diagram illustrating how a base station receives monitoring information related to CSI prediction from a UE.

[0055] FIG. 24 illustrates a communication system to which the present invention is applied.

[0056] FIG. 25 illustrates a wireless device that can be applied to the present invention.

[0057] FIG. 26 illustrates another example of a wireless device to which the present invention applies. The wireless device may be implemented in various forms depending on the use-example / service.

[0058] FIG. 27 illustrates a vehicle or autonomous vehicle to which the present invention is applied.

[0059] A wireless communication system is a multiple access system that supports communication with multiple users by sharing available system resources (e.g., bandwidth, transmission power, etc.). Examples of multiple access systems include CDMA (code division multiple access), FDMA (frequency division multiple access), TDMA (time division multiple access), OFDMA (orthogonal frequency division multiple access), SC-FDMA (single carrier frequency division multiple access), and MC-FDMA (multi carrier frequency division multiple access) systems.

[0060] Sidelink refers to a communication method in which User Equipment (UE) establishes a direct link to directly exchange voice or data between terminals without passing through a Base Station (BS). Sidelink is being considered as a solution to address the burden on base stations caused by rapidly increasing data traffic.

[0061] V2X (vehicle-to-everything) refers to a communication technology that exchanges information with other vehicles, pedestrians, and infrastructure-equipped objects through wired or wireless communication. V2X can be classified into four types: V2V (vehicle-to-vehicle), V2I (vehicle-to-infrastructure), V2N (vehicle-to-network), and V2P (vehicle-to-pedestrian). V2X communication can be provided through PC5 interfaces and / or Uu interfaces.

[0062] Meanwhile, as more communication devices require larger communication capacities, the need for improved mobile broadband communication compared to existing Radio Access Technology (RAT) is emerging. Accordingly, communication systems considering services or terminals sensitive to reliability and latency are being discussed; next-generation radio access technology that incorporates improved mobile broadband communication, Massive MTC, and URLLC (Ultra-Reliable and Low Latency Communication) can be referred to as new radio access technology (new RAT) or new radio (NR). Vehicle-to-everything (V2X) communication can also be supported in NR.

[0063] The following technologies can be used in various wireless communication systems such as CDMA (code division multiple access), FDMA (frequency division multiple access), TDMA (time division multiple access), OFDMA (orthogonal frequency division multiple access), and SC-FDMA (single carrier frequency division multiple access). CDMA can be implemented using wireless technologies such as UTRA (universal terrestrial radio access) or CDMA2000. TDMA can be implemented using wireless technologies such as GSM (global system for mobile communications), GPRS (general packet radio service), and EDGE (enhanced data rates for GSM evolution). OFDMA can be implemented using wireless technologies such as IEEE (institute of electrical and electronics engineers) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802-20, and E-UTRA (evolved UTRA). IEEE 802.16m is an evolution of IEEE 802.16e and provides backward compatibility with systems based on IEEE 802.16e. 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) which uses E-UTRA (evolved-UMTS terrestrial radio access), employing OFDMA in the downlink and SC-FDMA in the uplink.LTE-A (advanced) is an evolution of 3GPP LTE.

[0064] 5G NR is a successor technology to LTE-A and is a new clean-slate type mobile communication system with characteristics such as high performance, low latency, and high availability. 5G NR can utilize all available spectrum resources, ranging from low frequency bands below 1 GHz to mid-frequency bands from 1 GHz to 10 GHz, and high frequency (millimeter wave) bands above 24 GHz.

[0065] For clarity of explanation, the description focuses on LTE-A or 5G NR, but the technical concept of the embodiment(s) is not limited thereto.

[0066] Figure 2 shows the structure of an applicable LTE system. This can be called an E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network), or an LTE (Long Term Evolution) / LTE-A system.

[0067] Referring to FIG. 2, the E-UTRAN includes a base station (20; Base Station, BS) that provides a control plane and a user plane to a terminal (10). The terminal (10) may be fixed or mobile and may be referred to by other terms such as MS (Mobile Station), UT (User Terminal), SS (Subscriber Station), MT (Mobile Terminal), or Wireless Device. The base station (20) refers to a fixed station that communicates with the terminal (10) and may be referred to by other terms such as eNB (evolved-NodeB), BTS (Base Transceiver System), or Access Point.

[0068] Base stations (20) can be connected to each other through an X2 interface. The base station (20) is connected to the EPC (Evolved Packet Core, 30) through the S1 interface, more specifically to the MME (Mobility Management Entity) through the S1-MME and to the S-GW (Serving Gateway) through the S1-U.

[0069] The EPC (30) consists of an MME, an S-GW, and a P-GW (Packet Data Network-Gateway). The MME holds information regarding the terminal's connection information or capabilities, and this information is primarily used for managing the terminal's mobility. The S-GW is a gateway with an E-UTRAN as its endpoint, and the P-GW is a gateway with a PDN as its endpoint.

[0070] The layers of the Radio Interface Protocol between a terminal and a network can be classified into L1 (Layer 1), L2 (Layer 2), and L3 (Layer 3) based on the lower three layers of the Open System Interconnection (OSI) model, which is widely known in communication systems. Among these, the Physical Layer, belonging to Layer 1, provides Information Transfer Services using a physical channel, while the Radio Resource Control (RRC) layer, located at Layer 3, performs the role of controlling radio resources between the terminal and the network. To this end, the RRC layer exchanges RRC messages between the terminal and the base station.

[0071] Figure 3 shows the structure of the NR system.

[0072] Referring to FIG. 3, the NG-RAN may include gNBs and / or eNBs that provide user plane and control plane protocol termination to terminals. FIG. 7 illustrates a case where only gNBs are included. The gNBs and eNBs are connected to each other via Xn interfaces. The gNBs and eNBs are connected to the 5G Core Network (5GC) via NG interfaces. More specifically, they are connected to the access and mobility management function (AMF) via NG-C interfaces and to the user plane function (UPF) via NG-U interfaces.

[0073] Figure 4 shows the structure of a wireless frame of NR.

[0074] Referring to FIG. 4, radio frames can be used for uplink and downlink transmission in NR. The radio frame has a length of 10 ms and can be defined as two 5 ms half-frames (HF). A half-frame may contain five 1 ms subframes (SF). A subframe may be divided into one or more slots, and the number of slots within a subframe may be determined by the subcarrier spacing (SCS). Each slot may contain 12 or 14 OFDM(A) symbols according to the cyclic prefix (CP).

[0075] When normal CP is used, each slot may contain 14 symbols. When extended CP is used, each slot may contain 12 symbols. Here, the symbols may include OFDM symbols (or CP-OFDM symbols) and SC-FDMA (Single Carrier - FDMA) symbols (or DFT-s-OFDM (Discrete Fourier Transform-spread-OFDM) symbols).

[0076] Table 1 below shows the number of symbols per slot ((N) according to the SCS setting (u) when normal CP is used. slot symb ), number of slots per frame((N frame,u slot ) and the number of slots per subframe((N subframe,u slot ) exemplifies.

[0077] SCS (15*2 u )N slot symb N frame,u slot N subframe,u slot 15KHz (u=0)1410130KHz (u=1)1420260KHz (u=2)14404120KHz (u=3)14808240KHz (u=4)1416016

[0078] Table 2 shows the number of symbols per slot, the number of slots per frame, and the number of slots per subframe according to the SCS when an extended CP is used.

[0079] SCS (15*2 u )N slot symb N frame,u slot N subframe,u slot 60KHz (u=2)12404

[0080] In an NR system, the OFDM(A) numerology (e.g., SCS, CP length, etc.) can be configured differently among multiple cells that are merged into a single terminal. Accordingly, the (absolute time) interval of a time resource (e.g., subframe, slot, or TTI) (collectively referred to as TU (Time Unit) for convenience) composed of the same number of symbols can be configured differently among the merged cells.

[0081] In NR, multiple numerologies or SCSs may be supported to support various 5G services. For example, if the SCS is 15 kHz, a wide area in traditional cellular bands may be supported, and if the SCS is 30 kHz / 60 kHz, dense-urban, lower latency, and wider carrier bandwidth may be supported. If the SCS is 60 kHz or higher, a bandwidth greater than 24.25 GHz may be supported to overcome phase noise.

[0082] The NR frequency band can be defined by two types of frequency ranges. The two types of frequency ranges may be FR1 and FR2. The numerical values ​​of the frequency ranges may change, for example, as shown in Table 3 below. Among the frequency ranges used in an NR system, FR1 may mean "sub 6GHz range" and FR2 may mean "above 6GHz range" and may be referred to as millimeter wave (mmW).

[0083] Frequency Range designationCorresponding frequency rangeSubcarrier Spacing (SCS)FR1450MHz - 6000MHz15, 30, 60kHzFR224250MHz - 52600MHz60, 120, 240kHz

[0084] As described above, the numerical value of the frequency range of the NR system may change. For example, FR1 may include a band of 410 MHz to 7125 MHz as shown in Table 4 below. That is, FR1 may include a frequency band of 6 GHz (or 5850, 5900, 5925 MHz, etc.) or higher. For example, the frequency band of 6 GHz (or 5850, 5900, 5925 MHz, etc.) or higher included within FR1 may include an unlicensed band. The unlicensed band may be used for various purposes, for example, for communication for vehicles (e.g., autonomous driving).

[0085] Frequency Range designationCorresponding frequency rangeSubcarrier Spacing (SCS)FR1410MHz - 7125MHz15, 30, 60kHzFR224250MHz - 52600MHz60, 120, 240kHz

[0086] Figure 5 shows the slot structure of an NR frame.

[0087] Referring to FIG. 5, a slot contains multiple symbols in the time domain. For example, in the case of a normal CP, one slot may contain 14 symbols, but in the case of an extended CP, one slot may contain 12 symbols. Alternatively, in the case of a normal CP, one slot may contain 7 symbols, but in the case of an extended CP, one slot may contain 6 symbols.

[0088] A carrier includes multiple subcarriers in the frequency domain. A Resource Block (RB) can be defined as multiple (e.g., 12) consecutive subcarriers in the frequency domain. A Bandwidth Part (BWP) can be defined as multiple consecutive (P)RBs ((Physical) Resource Blocks) in the frequency domain and can correspond to a single numerology (e.g., SCS, CP length, etc.). A carrier can include up to N (e.g., 5) BWPs. Data communication can be performed through the active BWPs. Each element can be referred to as a Resource Element (RE) in a resource grid and can be mapped to a single complex symbol.

[0089] Meanwhile, a wireless interface between terminals or a wireless interface between a terminal and a network may be composed of L1, L2, and L3 layers. In various embodiments of the present disclosure, L1 layer may refer to the physical layer. Additionally, for example, L2 layer may refer to at least one of the MAC layer, RLC layer, PDCP layer, and SDAP layer. Additionally, for example, L3 layer may refer to the RRC layer.

[0090] FIG. 6 illustrates a communication structure that can be provided in a 6G system according to one embodiment of the present disclosure. The embodiment of FIG. 6 can be combined with various embodiments of the present disclosure.

[0091] New network characteristics in 6G may be as follows.

[0092] - Satellite Integrated Network

[0093] - Connected Intelligence: Unlike previous generations of wireless communication systems, 6G is innovative and will update wireless evolution from "connected things" to "connected intelligence." AI can be applied at each stage of the communication process (or at each step of the signal processing described below).

[0094] - Seamless integration of wireless information and energy transfer

[0095] - Ubiquitous Super 3D Connectivity: Connectivity to the network and core network functions of drones and very low Earth orbit satellites will create Super 3D connectivity in 6G ubiquitous.

[0096] Some general requirements regarding the new network characteristics of 6G mentioned above may be as follows.

[0097] - Small cell networks

[0098] - Ultra-dense heterogeneous network

[0099] - High-capacity backhaul

[0100] - Radar technology integrated with mobile technology: High-precision localization (or location-based services) through communication is one of the functions of 6G wireless communication systems. Therefore, radar systems will be integrated with 6G networks.

[0101] - Softwarization and virtualization

[0102] The core implementation technologies of the 6G system are described below.

[0103] - Artificial Intelligence: Introducing AI into communications can streamline and enhance real-time data transmission. AI can determine how complex target tasks are performed using numerous analyses. In other words, AI can increase efficiency and reduce processing latency. Time-consuming tasks such as handover, network selection, and resource scheduling can be performed instantly using AI. AI can also play a significant role in M2M, machine-to-human, and human-to-machine communication. Furthermore, AI can enable rapid communication in Brain-Computer Interfaces (BCI). AI-based communication systems can be supported by metamaterials, intelligent structures, intelligent networks, intelligent devices, intelligent cognitive radios, self-sustaining wireless networks, and machine learning.

[0104] - THz Communication: Data transmission rates can be increased by expanding bandwidth. This can be achieved by using sub-THz communication with wide bandwidth and applying advanced large-scale MIMO technology. THz waves, also known as sub-millimeter radiation, generally refer to a frequency band between 0.1 THz and 10 THz with corresponding wavelengths ranging from 0.03 mm to 3 mm. The 100 GHz-300 GHz band range (Sub-THz band) is considered the primary portion of the THz band for cellular communication. Adding the Sub-THz band to the mmWave band increases 6G cellular communication capacity. Among the defined THz bands, the 300 GHz-3 THz band is located in the far-infrared (IR) frequency band. Although the 300 GHz-3 THz band is part of the optical band, it lies at the boundary of the optical band and immediately following the RF band. Therefore, this 300 GHz-3 THz band exhibits similarities to RF.

[0105] FIG. 7 illustrates an electromagnetic spectrum according to one embodiment of the present disclosure. The embodiment of FIG. 7 may be combined with various embodiments of the present disclosure. Key characteristics of THz communication include (i) a widely available bandwidth to support very high data transmission rates, and (ii) high path loss occurring at high frequencies (highly directional antennas are indispensable). The narrow beam width generated by highly directional antennas reduces interference. The small wavelength of THz signals allows a much larger number of antenna elements to be integrated into devices and BSs operating in this band. This enables the use of advanced adaptive array techniques that can overcome range limitations.

[0106] - Large-scale MIMO technology

[0107] - Hologram beamforming (HBF)

[0108] - Optical wireless technology

[0109] - Free Space Optical Transmission Backhaul Network (FSO backhaul network)

[0110] - Quantum communication

[0111] - Cell-free communication

[0112] - Integration of wireless information and power transmission

[0113] - Integration of wireless communication and sensing

[0114] - Integrated access and backhaul network

[0115] - Big data analysis

[0116] - Reconfigurable intelligent metasurface

[0117] - Metaverse

[0118] - blockchain

[0119] - Unmanned Aerial Vehicle (UAV): UAVs or drones will be a critical element in 6G wireless communication. In most cases, high-speed data wireless connectivity can be provided using UAV technology. Base station (BS) entities can be installed on UAVs to provide cellular connectivity. UAVs can possess specific features not found in fixed BS infrastructure, such as easy deployment, robust line-of-sight links, and controlled degrees of freedom for mobility. During emergencies, such as natural disasters, the deployment of ground communication infrastructure is not economically feasible, and sometimes services cannot be provided in volatile environments. UAVs can easily handle these situations. UAVs will become a new paradigm in the field of wireless communication. This technology facilitates the three fundamental requirements of wireless networks: eMBB, URLLC, and mMTC. UAVs can also support various purposes, such as enhancing network connectivity, fire detection, disaster emergency services, security and surveillance, pollution monitoring, parking monitoring, and accident monitoring. Therefore, UAV technology is recognized as one of the most critical technologies for 6G communication.

[0120] - Autonomous Driving (Self-Driving): V2X (Vehicle to Everything), a core element in building autonomous driving infrastructure, refers to technologies that enable vehicles to communicate and share with various elements on the road for autonomous driving, such as wireless communication between vehicles (Vehicle to Vehicle, V2V) and between vehicles and infrastructure (Vehicle to Infrastructure, V2I). Fast transmission speeds and low-latency technologies are essential to maximize autonomous driving performance and ensure high safety. Furthermore, future autonomous driving may go beyond merely delivering warning or guidance messages to the driver to actively intervene in vehicle operation and directly control the vehicle in dangerous situations. Since the amount of information to be transmitted and received may become massive for this purpose, it is expected that 6G will be able to maximize autonomous driving through faster transmission speeds and lower latency compared to 5G.

[0121] FIG. 8 illustrates a radio protocol architecture for SL communication. Specifically, FIG. 8 (a) shows the user plane protocol stack of NR, and FIG. 8 (b) shows the control plane protocol stack of NR.

[0122] The Sidelink Synchronization Signal (SLSS) and synchronization information are described below.

[0123] SLSS is an SL-specific sequence that may include PSSS (Primary Sidelink Synchronization Signal) and SSSS (Secondary Sidelink Synchronization Signal). The PSSS may be referred to as S-PSS (Sidelink Primary Synchronization Signal), and the SSSS may be referred to as S-SSS (Sidelink Secondary Synchronization Signal). For example, length-127 M-sequences may be used for S-PSS, and length-127 Gold sequences may be used for S-SSS. For example, a terminal may use S-PSS to detect a primary signal and obtain synchronization. For example, a terminal may use S-PSS and S-SSS to obtain detailed synchronization and detect a synchronization signal ID.

[0124] PSBCH (Physical Sidelink Broadcast Channel) may be a (broadcast) channel through which basic (system) information that a terminal must know first is transmitted before transmitting or receiving SL signals. For example, the basic information may include information related to SLSS, Duplex Mode (DM), TDD UL / DL (Time Division Duplex Uplink / Downlink) configuration, information related to resource pools, types of applications related to SLSS, subframe offsets, broadcast information, etc. For example, to evaluate PSBCH performance, in NR V2X, the payload size of PSBCH may be 56 bits, including a 24-bit CRC.

[0125] S-PSS, S-SSS, and PSBCH may be included in a block format that supports periodic transmission (e.g., SL SS (Synchronization Signal) / PSBCH block, hereinafter S-SSB (Sidelink-Synchronization Signal Block)). The S-SSB may have the same numerology (i.e., SCS and CP lengths) as the PSCCH (Physical Sidelink Control Channel) / PSSCH (Physical Sidelink Shared Channel) within the carrier, and the transmission bandwidth may be within a (pre-)set SL BWP (Sidelink BWP). For example, the bandwidth of the S-SSB may be 11 RB (Resource Block). For example, the PSBCH may span 11 RB. Additionally, the frequency position of the S-SSB may be (pre-)set. Therefore, the terminal does not need to perform hypothesis detection at the frequency to discover the S-SSB in the carrier.

[0126] Meanwhile, in an NR SL system, multiple pneumatics having different SCS and / or CP lengths may be supported. In this case, as the SCS increases, the length of the time resource for the transmitting terminal to transmit S-SSBs may decrease. Consequently, the coverage of S-SSBs may decrease. Therefore, to ensure S-SSB coverage, the transmitting terminal may transmit one or more S-SSBs to the receiving terminal within a single S-SSB transmission cycle according to the SCS. For example, the number of S-SSBs transmitted by the transmitting terminal to the receiving terminal within a single S-SSB transmission cycle may be pre-configured or configured for the transmitting terminal. For example, the S-SSB transmission cycle may be 160ms. For example, an S-SSB transmission cycle of 160ms may be supported for all SCSs.

[0127] For example, if the SCS is 15 kHz at FR1, the transmitting terminal may transmit one or two S-SSBs to the receiving terminal within one S-SSB transmission cycle. For example, if the SCS is 30 kHz at FR1, the transmitting terminal may transmit one or two S-SSBs to the receiving terminal within one S-SSB transmission cycle. For example, if the SCS is 60 kHz at FR1, the transmitting terminal may transmit one, two, or four S-SSBs to the receiving terminal within one S-SSB transmission cycle.

[0128] For example, if the SCS is 60 kHz at FR2, the transmitting terminal can transmit 1, 2, 4, 8, 16, or 32 S-SSBs to the receiving terminal within one S-SSB transmission cycle. For example, if the SCS is 120 kHz at FR2, the transmitting terminal can transmit 1, 2, 4, 8, 16, 32, or 64 S-SSBs to the receiving terminal within one S-SSB transmission cycle.

[0129] Meanwhile, when the SCS is 60 kHz, two types of CP may be supported. Additionally, depending on the CP type, the structure of the S-SSB transmitted by the transmitting terminal to the receiving terminal may differ. For example, the CP type may be Normal CP (NCP) or Extended CP (ECP). Specifically, for example, if the CP type is NCP, the number of symbols mapping PSBCH within the S-SSB transmitted by the transmitting terminal may be 9 or 8. On the other hand, for example, if the CP type is ECP, the number of symbols mapping PSBCH within the S-SSB transmitted by the transmitting terminal may be 7 or 6. For example, PSBCH may be mapped to the first symbol within the S-SSB transmitted by the transmitting terminal. For example, the receiving terminal receiving the S-SSB may perform Automatic Gain Control (AGC) operation during the first symbol interval of the S-SSB.

[0130] Figure 9 shows a terminal performing V2X or SL communication.

[0131] Referring to FIG. 9, in V2X or SL communication, the term terminal may primarily refer to a user's terminal. However, if network equipment such as a base station transmits and receives signals according to the communication method between terminals, the base station may also be considered a type of terminal. For example, terminal 1 may be a first device (100), and terminal 2 may be a second device (200).

[0132] For example, terminal 1 can select a resource unit corresponding to a specific resource within a resource pool, which represents a set of resources. Then, terminal 1 can transmit an SL signal using the said resource unit. For example, terminal 2, which is a receiving terminal, can be configured with a resource pool in which terminal 1 can transmit a signal, and can detect terminal 1's signal within said resource pool.

[0133] Here, if terminal 1 is within the connection range of the base station, the base station may inform terminal 1 of the resource pool. On the other hand, if terminal 1 is outside the connection range of the base station, another terminal may inform terminal 1 of the resource pool, or terminal 1 may use a pre-configured resource pool.

[0134] Generally, a resource pool can be composed of multiple resource units, and each terminal can select one or more resource units to use for its SL signal transmission.

[0135] Figure 10 shows a resource unit for V2X or SL communication.

[0136] Referring to FIG. 10, the total frequency resources of the resource pool can be divided into NF units, and the total time resources of the resource pool can be divided into NT units. Thus, a total of NF * NT resource units can be defined within the resource pool. FIG. 10 illustrates an example where the resource pool is repeated in a period of NT subframes.

[0137] As shown in FIG. 10, a single resource unit (e.g., Unit #0) may appear repeatedly over time. Alternatively, to obtain diversity effects in the time or frequency dimension, the index of the physical resource unit to which a single logical resource unit is mapped may change in a predetermined pattern over time. In this structure of resource units, a resource pool may refer to a set of resource units that a terminal intending to transmit an SL signal can use for transmission.

[0138] Resource pools can be subdivided into several types. For example, depending on the content of the SL signals transmitted from each resource pool, resource pools can be classified as follows.

[0139] (1) A Scheduling Assignment (SA) may be a signal containing information such as the location of the resource used by the transmitting terminal for transmission of the SL data channel, the Modulation and Coding Scheme (MCS) or Multiple Input Multiple Output (MIMO) transmission method required for demodulation of the data channel, and Timing Advance (TA). The SA may also be multiplexed and transmitted together with the SL data on the same resource unit, in which case the SA resource pool may refer to a resource pool in which the SA is multiplexed and transmitted together with the SL data. The SA may also be called the SL control channel.

[0140] (2) A Physical Sidelink Shared Channel (PSSCH) may be a resource pool used by a transmitting terminal to transmit user data. If SA is multiplexed and transmitted along with SL data on the same resource unit, only the form of the SL data channel excluding SA information can be transmitted from the resource pool for the SL data channel. In other words, REs (Resource Elements) that were used to transmit SA information on individual resource units within the SA resource pool can still be used to transmit SL data in the resource pool of the SL data channel. For example, the transmitting terminal can transmit by mapping the PSSCH to a succession of PRBs.

[0141] (3) The discovery channel may be a resource pool for a transmitting terminal to transmit information such as its ID. Through this, the transmitting terminal can enable adjacent terminals to discover it.

[0142] Even if the content of the SL signal described above is the same, different resource pools may be used depending on the transmission and reception attributes of the SL signal. For example, even if the same SL data channel or discovery message is used, it may be divided into different resource pools depending on the method of determining the transmission timing of the SL signal (e.g., whether it is transmitted at the time of reception of the synchronization reference signal or whether it is transmitted by applying a certain timing advance at the time of reception), the method of resource allocation (e.g., whether the base station assigns the transmission resource of an individual signal to the individual transmission terminal or whether the individual transmission terminal selects the individual signal transmission resource itself from within the resource pool), the signal format (e.g., the number of symbols occupied by each SL signal in one subframe, or the number of subframes used for the transmission of one SL signal), the signal strength from the base station, the transmission power strength of the SL terminal, etc.

[0143] FIG. 11 illustrates an example of a BWP according to an embodiment of the present disclosure. The embodiment of FIG. 11 may be combined with various embodiments of the present disclosure. In the embodiment of FIG. 11, it is assumed that there are three BWPs.

[0144] Referring to FIG. 11, the common resource block (CRB) may be a numbered carrier resource block extending from one end of the carrier band to the other. And, the PRB may be a numbered resource block within each BWP. Point A may indicate a common reference point for the resource block grid.

[0145] A BWP can be configured by point A, an offset from point A (NstartBWP), and a bandwidth (NsizeBWP). For example, point A may be an external reference point of the PRB of a carrier where the subcarrier 0 of all numerologies (e.g., all numerologies supported by the network on that carrier) is aligned. For example, the offset may be the PRB interval between the lowest subcarrier in a given numerology and point A. For example, the bandwidth may be the number of PRBs in a given numerology.

[0146] SLSS (Sidelink Synchronization Signal) is a sidelink-specific sequence and may include PSSS (Primary Sidelink Synchronization Signal) and SSSS (Secondary Sidelink Synchronization Signal). The PSSS may be referred to as S-PSS (Sidelink Primary Synchronization Signal), and the SSSS may be referred to as S-SSS (Sidelink Secondary Synchronization Signal). For example, length-127 M-sequences may be used for S-PSS, and length-127 Gold sequences may be used for S-SSS. For example, a terminal may use S-PSS to detect the initial signal and obtain synchronization. For example, a terminal may use S-PSS and S-SSS to obtain detailed synchronization and detect the synchronization signal ID.

[0147] The PSBCH (Physical Sidelink Broadcast Channel) may be a (broadcast) channel through which basic (system) information that the terminal must know first is transmitted before transmitting or receiving SL signals. For example, the basic information may include information related to SLSS, Duplex Mode (DM), TDD UL / DL (Time Division Duplex Uplink / Downlink) configuration, information related to resource pools, types of applications related to SLSS, subframe offsets, broadcast information, etc. For example, to evaluate PSBCH performance, in NR V2X, the payload size of the PSBCH may be 56 bits, including a 24-bit CRC (Cyclic Redundancy Check).

[0148] S-PSS, S-SSS, and PSBCH may be included in a block format that supports periodic transmission (e.g., SL SS (Synchronization Signal) / PSBCH block, hereinafter S-SSB (Sidelink-Synchronization Signal Block)). The S-SSB may have the same numerology (i.e., SCS and CP lengths) as the PSCCH (Physical Sidelink Control Channel) / PSSCH (Physical Sidelink Shared Channel) within the carrier, and the transmission bandwidth may be within a (pre-)set SL BWP (Sidelink BWP). For example, the bandwidth of the S-SSB may be 11 RB (Resource Block). For example, the PSBCH may span 11 RB. Additionally, the frequency position of the S-SSB may be (pre-)set. Therefore, the terminal does not need to perform hypothesis detection at the frequency to discover the S-SSB in the carrier.

[0149] FIG. 12 illustrates a procedure in which a terminal performs V2X or SL communication according to a resource allocation mode, according to one embodiment of the present disclosure. The embodiment of FIG. 12 may be combined with various embodiments of the present disclosure.

[0150] Referring to FIG. 12(a), in resource allocation mode 1, the base station may schedule SL resources to be used by the terminal for SL transmission. For example, in step S1200, the base station may transmit information related to SL resources and / or information related to UL resources to the first terminal. For example, the UL resources may include PUCCH resources and / or PUSCH resources. For example, the UL resources may be resources for reporting SL HARQ feedback to the base station.

[0151] For example, the first terminal may receive information related to a dynamic grant (DG) resource and / or information related to a configured grant (CG) resource from the base station. For example, the CG resource may include a CG type 1 resource or a CG type 2 resource. In this specification, the DG resource may be a resource that the base station sets / assigns to the first terminal via downlink control information (DCI). In this specification, the CG resource may be a (periodic) resource that the base station sets / assigns to the first terminal via DCI and / or RRC messages. For example, in the case of a CG type 1 resource, the base station may transmit an RRC message containing information related to the CG resource to the first terminal. For example, in the case of a CG type 2 resource, the base station may transmit an RRC message containing information related to the CG resource to the first terminal, and the base station may transmit DCI related to the activation or release of the CG resource to the first terminal.

[0152] In step S1210, the first terminal may transmit a PSCCH (e.g., Sidelink Control Information or 1st-stage SCI) to the second terminal based on the resource scheduling. In step S1220, the first terminal may transmit a PSSCH (e.g., 2nd-stage SCI, MAC PDU, data, etc.) associated with the PSCCH to the second terminal. In step S1230, the first terminal may receive a PSFCH associated with the PSCCH / PSSCH from the second terminal. For example, HARQ feedback information (e.g., NACK information or ACK information) may be received from the second terminal via the PSFCH. In step S1240, the first terminal may transmit / report the HARQ feedback information to the base station via a PUCCH or PUSCH. For example, the HARQ feedback information reported to the base station may be information generated by the first terminal based on HARQ feedback information received from the second terminal. For example, the HARQ feedback information reported to the base station may be information generated by the first terminal based on a pre-set rule. For example, the DCI may be a DCI for scheduling SL.

[0153] Referring to FIG. 12(b), in resource allocation mode 2, the terminal can determine an SL transmission resource within an SL resource set by the base station / network or a preset SL resource. For example, the set SL resource or the preset SL resource may be a resource pool. For example, the terminal may autonomously select or schedule a resource for SL transmission. For example, the terminal may perform SL communication by selecting a resource itself within the set resource pool. For example, the terminal may select a resource itself within a selection window by performing a sensing and resource (re)selection procedure. For example, the sensing may be performed on a subchannel basis. For example, in step S1210, the first terminal, having selected a resource itself within the resource pool, may use the resource to transmit PSCCH (e.g., SCI (Sidelink Control Information) or 1st-stage SCI) to the second terminal. In step S1220, the first terminal can transmit PSSCH (e.g., 2nd-stage SCI, MAC PDU, data, etc.) associated with the PSCCH to the second terminal. In step S1230, the first terminal can receive PSFCH associated with the PSCCH / PSSCH from the second terminal.

[0154] Referring to FIG. 12 (a) or (b), for example, the first terminal may transmit an SCI to the second terminal over the PSCCH. Or, for example, the first terminal may transmit two consecutive SCIs (e.g., 2-stage SCIs) to the second terminal over the PSCCH and / or PSSCH. In this case, the second terminal may decode the two consecutive SCIs (e.g., 2-stage SCIs) to receive the PSSCH from the first terminal. In this specification, an SCI transmitted over the PSCCH may be referred to as the 1st SCI, the 1st SCI, the 1st-stage SCI, or the 1st-stage SCI format, and an SCI transmitted over the PSSCH may be referred to as the 2nd SCI, the 2nd SCI, the 2nd-stage SCI, or the 2nd-stage SCI format.

[0155] Referring to FIG. 12 (a) or (b), in step S1230, the first terminal can receive PSFCH. For example, the first terminal and the second terminal can determine a PSFCH resource, and the second terminal can use the PSFCH resource to transmit HARQ feedback to the first terminal.

[0156] Referring to FIG. 12(a), in step S1240, the first terminal can transmit SL HARQ feedback to the base station via PUCCH and / or PUSCH.

[0157] CSI Measurement / Reporting

[0158] In NR (New Radio) systems, CSI-RS (channel state information-reference signal) is used for time and / or frequency tracking, CSI computation, L1 (layer 1)-RSRP (reference signal received power) computation, and mobility. Here, CSI computation is related to CSI acquisition, and L1-RSRP computation is related to beam management (BM). CSI (channel state information) is a general term for information that can indicate the quality of the radio channel (also called a link) formed between a terminal and an antenna port.

[0159] A base station can transmit CSI-RS to a terminal to determine the characteristics of the downlink channel, and can receive feedback from the terminal regarding channel measurement results based on CSI-RS.

[0160] CSI-RS may be configured for one or more terminals. Different CSI-RS configurations may be provided for each terminal, or the same CSI-RS configuration may be provided for multiple terminals. CSI-RS may support up to 32 antenna ports. CSI-RS corresponding to N (N is 1 or more) antenna ports may be mapped to N RE positions within a time-frequency unit corresponding to one slot and one RB. If N is 2 or more, N-port CSI-RS may be multiplexed in CDM, FDM, and / or TDM modes. A CDM group may include two antenna ports (CDM2) distinguished by code resources on the same two adjacent subcarriers, four antenna ports (CDM4) distinguished by code resources on the same two adjacent subcarriers and the same two adjacent OFDM slots, or eight antenna ports (CDM8) distinguished by code resources on the same two adjacent subcarriers and the same four adjacent OFDM slots. If multiple CDM groups exist, the CDM groups may not be mapped to adjacent subcarriers and / or adjacent OFDM symbols. CSI-RS antenna ports can be indexed in the order of CDM groups, frequency domain, and time domain. CSI-RS can be mapped to REs other than the REs to which CORESET, DMRS, and SSB are mapped.

[0161] In the frequency domain, CSI-RS can be configured for the entire bandwidth, a portion of the bandwidth (BWP), or a portion of the bandwidth. CSI-RS may be transmitted at each RB within the configured bandwidth (i.e., density=1), or at every second RB (e.g., the even or odd RB) (i.e., density=1 / 2). When CSI-RS is used as a Tracking Reference Signal (TRS), a single-port CSI-RS may be mapped onto three subcarriers in each resource block (i.e., density=3).

[0162] One or more sets of CSI-RS resources may be configured for a terminal in the time domain. Each set of CSI-RS resources may include one or more CSI-RS settings.

[0163] Each CSI-RS resource set can be configured to be periodic, semipersistent, or non-periodic. For periodic CSI-RS resource sets, the period can be configured to a number of slots ranging from 4 to 640. Additionally, a starting offset value for periodic CSI-RS resource sets can be configured. For semipersistent CSI-RS resource sets, an offset and period for CSI-RS resource set candidates can be configured. Here, actual CSI-RS transmission can be activated / deactivated based on a MAC Control Element (CE). When a CSI-RS resource set is activated, CSI-RS transmission may be performed according to the configured offset and period until it is deactivated. When a CSI-RS resource set is deactivated, CSI-RS transmission may not be performed until it is explicitly reactivated. For non-periodic CSI-RS transmission, information regarding each CSI-RS resource set may be explicitly provided by the DCI.

[0164] A CSI-IM resource may be configured for Interference Measurement (IM) of a terminal. A CSI-IM resource may contain four REs within one slot and one resource block. The four REs may correspond to two consecutive OFDM symbols and two consecutive subcarriers, or to one OFDM symbol and four consecutive subcarriers. In the frequency domain, the location of the CSI-IM REs may be determined by the CSI-IM configuration. In the time domain, the CSI-IM resource set may be configured periodic, semi-persistent, or non-periodically, similar to the CSI resource set. Generally, transmission may not be performed in the corresponding cell but may be performed in a neighboring cell. As such, the CSI-IM resource may be configured as Zero Power (ZP)-CSI-RS for the terminal.

[0165] ZP-CSI-RS can be configured to be distinct from Non-Zero Power (NZP)-CSI-RS. When a PDSCH is scheduled on a resource containing a CSI-RS RE, the first terminal may assume that rate matching considering the CSI-RS RE is applied to the PDSCH, so that the PDSCH is not mapped to the CSI-RS RE. Here, the CSI-RS may be configured for the first terminal or for the second terminal. In this case, the CSI-RS for the first terminal may be configured as NZP-CSI-RS for the first terminal, and an NZP-CSI-RS resource set may be configured for the first terminal. Meanwhile, the CSI-RS for the second terminal may be configured as ZP-CSI-RS for the first terminal, and a ZP-CSI-RS resource set may be configured for the first terminal. The NZP-CSI-RS resource set can be used for the CSI report configuration of the terminal. The NZP-CSI-RS resource set may also be associated with a CSI-RS or an SSB. Additionally, multiple periodic NZP-CSI-RS resource sets can be configured as TRS resource sets.

[0166] CSI-related operations can be summarized as follows.

[0167] To perform one of the uses of CSI-RS, a terminal (e.g., user equipment, UE) receives configuration information related to CSI from a base station (e.g., general Node B, gNB) via radio resource control (RRC) signaling. The configuration information related to CSI may include at least one of information related to CSI-IM (interference management) resources, information related to CSI measurement configuration, information related to CSI resource configuration, information related to CSI-RS resources, or information related to CSI report configuration.

[0168] - CSI-IM resource-related information may include CSI-IM resource information, CSI-IM resource set information, etc. A CSI-IM resource set is identified by a CSI-IM resource set ID (identifier), and one resource set includes at least one CSI-IM resource. Each CSI-IM resource is identified by a CSI-IM resource ID.

[0169] - Information related to CSI resource configuration can be expressed as CSI-ResourceConfig IE. Information related to CSI resource configuration defines a group that includes at least one of an NZP (non-zero power) CSI-RS resource set, a CSI-IM resource set, or a CSI-SSB resource set. That is, the information related to CSI resource configuration includes 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. Through the NZP CSI-RS resource set IE, parameters indicating the use of CSI-RS (e.g., a BM-related 'repetition' parameter, a tracking-related 'trs-Info' parameter) can be set for each NZP CSI-RS resource set. Also, the repetition parameter corresponding to the higher layer parameter corresponds to 'CSI-RS-ResourceRep' of the L1 parameter.

[0170] - Information related to CSI report configuration includes a reportConfigType parameter representing time domain behavior and a reportQuantity parameter representing the CSI-related quantity to be reported. The time domain behavior may be periodic, aperiodic, or semi-persistent. Information related to CSI report configuration may be expressed as CSI-ReportConfig IE.

[0171] The terminal measures the CSI based on configuration information related to the above CSI. The terminal's CSI measurement may include a process of receiving CSI-RS on a CSI-RS RE specified based on the higher layer parameter CSI-RS-ResourceMapping, and computing the CSI through the received CSI-RS. By CSI-RS-ResourceMapping, the RE (resource element) mapping of the CSI-RS resource in the time and frequency domains is set for the terminal. In CSI-RS-ResourceMapping, density (D) represents the density of the CSI-RS resource measured in the RE / port / PRB (physical resource block), and nrofPorts represents the number of antenna ports.

[0172] The terminal reports the measured CSI to the base station. If the quantity of CSI-ReportConfig is set to 'none (or No report)', the terminal may omit the CSI report. However, even if the quantity is set to 'none (or No report)', the terminal may still report to the base station. The case where the quantity is set to 'none' is when the aperiodic TRS is triggered or when repetition is enabled. Here, the terminal may omit the report only when repetition is set to 'ON'.

[0173] As time domain behaviors for 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.

[0174] NR's CSI-IM-based Interference Measurement Resource (IMR) has a design similar to LTE's CSI-IM and is configured independently of ZP CSI-RS resources for PDSCH rate matching. In addition, in the NZP CSI-RS-based IMR, each port emulates an interference layer with (desired channel and) precoded NZP CSI-RS. This is for intra-cell interference measurement in the multi-user case and primarily targets MU interference.

[0175] The base station transmits precoded NZP CSI-RS to the terminal on each port of the configured NZP CSI-RS-based IMR.

[0176] The terminal assumes a channel / interference layer for each port in the resource set and measures interference.

[0177] For a channel, if there is no PMI and RI feedback, multiple resources are set, and the base station or network indicates a subset of NZP CSI-RS resources for channel / interference measurement via DCI.

[0178] Each CSI resource setting 'CSI-ResourceConfig' contains a configuration for S≥1 CSI resource sets (given by the higher layer parameter csi-RS-ResourceSetList). The CSI resource setting corresponds to the CSI-RS-resourcesetlist, where S represents the number of configured CSI-RS resource sets. Here, the list of S≥1 CSI resource sets includes either or both of the NZP CSI-RS resource set(s) and the SS / PBCH block (SSB) set(s) used for L1-RSRP computation, or includes CSI-IM resource set(s).

[0179] Each CSI resource setting is located in a DL BWP (bandwidth part) identified by a higher layer parameter BWP-id. Additionally, all CSI resource settings linked to a CSI reporting setting have the same DL BWP.

[0180] Within the CSI resource setting included in CSI-ResourceConfig IE, the time domain behavior of the CSI-RS resource is dictated by the higher layer parameter resourceType and can be set to aperiodic, periodic, or semi-persistent. For periodic and semi-persistent CSI resource settings, the number of configured CSI-RS resource sets (S) is limited to '1'. For periodic and semi-persistent CSI resource settings, the configured periodicity and slot offset are given from the numerology of the associated DL BWP, as given by the BWP-id.

[0181] When a UE is configured with multiple CSI-ResourceConfigs containing the same NZP CSI-RS resource ID, the same time domain behavior is configured for the multiple CSI-ResourceConfigs.

[0182] When a UE is configured with multiple CSI-ResourceConfigs containing the same CSI-IM resource ID, the same time domain behavior is configured for the multiple CSI-ResourceConfigs.

[0183] Next, one or more CSI resource settings for channel measurement (CM) and interference measurement (IM) are established through higher layer signaling.

[0184] - CSI-IM resource for interference measurement.

[0185] - NZP CSI-RS resources for interference measurement.

[0186] - NZP CSI-RS resources for channel measurement.

[0187] That is, the CMR (channel measurement resource) may be an NZP CSI-RS for CSI acquisition, and the IMR (Interference measurement resource) may be an NZP CSI-RS for CSI-IM and IM.

[0188] Here, CSI-IM (or ZP CSI-RS for IM) is primarily used for inter-cell interference measurements.

[0189] Also, the NZP CSI-RS for IM is mainly used for intra-cell interference measurement from multi-users.

[0190] A UE can assume that the CSI-RS resource(s) for channel measurement set for one CSI reporting and the CSI-IM / NZP CSI-RS resource(s) for interference measurement (when the NZP CSI-RS resource(s) are used for interference measurement) have a QCL relationship with respect to 'QCL-TypeD' on a resource-by-resource basis.

[0191] As examined, resource setting can refer to a resource set list.

[0192] For aperiodic CSI, each trigger state set using the higher layer parameter CSI-AperiodicTriggerState is associated with one or more CSI-ReportConfigs, and each CSI-ReportConfig is linked to a periodic, semi-persistent, or aperiodic resource setting.

[0193] One reporting setting can be linked to up to three resource settings.

[0194] - When a resource setting is set, that resource setting (given by the higher layer parameter resourcesForChannelMeasurement) is for channel measurement for L1-RSRP computation or channel and interference measurement for L1-SINR computation.

[0195] - When two resource settings are set, the first resource setting (given by the higher layer parameter resourcesForChannelMeasurement) is for channel measurement, and the second resource setting (given by csi-IM-ResourcesForInterference or nzp-CSI-RS-ResourcesForInterference) is for interference measurement performed on CSI-IM or NZP CSI-RS.

[0196] - When three resource settings are set, the first resource setting (given by resourcesForChannelMeasurement) is for channel measurement, the second resource setting (given by csi-IM-ResourcesForInterference) is for CSI-IM based interference measurement, and the third resource setting (given by nzp-CSI-RS-ResourcesForInterference) is for NZP CSI-RS based interference measurement.

[0197] For semi-persistent or periodic CSI, each CSI-ReportConfig is linked to periodic or semi-persistent resource setting(s).

[0198] - When a resource setting (given by resourcesForChannelMeasurement) is configured, said resource setting is for channel measurement for L1-RSRP computation or channel and interference measurement for L1-SINR computation.

[0199] - When two resource settings are configured, the first resource setting (given by resourcesForChannelMeasurement) is for channel measurement, and the second resource setting (given by higher layer parameter csi-IM-ResourcesForInterference or nzp-CSI-RS-ResourcesForInterference) is used for interference measurement performed on CSI-IM or NZP CSI-RS.

[0200] When interference measurements are performed on CSI-IM, each CSI-RS resource for channel measurement is associated with a CSI-IM resource by resource in the order of CSI-RS resources and CSI-IM resources within the corresponding resource set. The number of CSI-RS resources for channel measurement is equal to the number of CSI-IM resources.

[0201] And, when interference measurement is performed in NZP CSI-RS, the UE does not expect to be set to one or more NZP CSI-RS resources in the associated resource set within the resource setting for channel measurement.

[0202] A terminal with the Higher layer parameter nzp-CSI-RS-ResourcesForInterference configured does not expect more than 18 NZP CSI-RS ports to be configured within a single NZP CSI-RS resource set.

[0203] For CSI measurement(s) other than L1-SINR, the terminal assumes the following:

[0204] - Each NZP CSI-RS port configured for interference measurement corresponds to the interference transport layer.

[0205] - All interference transmission layers of the NZP CSI-RS port for interference measurement consider the associated EPRE (energy per resource element) ratio.

[0206] - Other interference signals on the RE(s) of the NZP CSI-RS resource for channel measurement, the NZP CSI-RS resource for interference measurement, or the CSI-IM resource for interference measurement.

[0207] The terminal can perform measurements of channel characteristics based on CSI-RS and feed back a CSI report to the base station as a result. To this end, a CSI report configuration may be provided to the terminal. Each CSI report configuration may include settings for the feedback type, measurement resource, report type, etc.

[0208] Feedback types may include Channel Quality Indicator (CQI), Precoding Matrix Indicator (PMI), CSI-RS Resource Indicator (CRI), SSBRI (SSB Resource block Indicator), Layer Indicator (LI), Rank Indicator (RI), Layer 1-Reference Signal Received Strength (RSRP), etc.

[0209] The measurement resource may include settings for downlink signals and / or downlink resources for which the terminal will perform measurements to determine feedback information. The measurement resource may be set as a set of ZP and / or NZP CSI-RS resources associated with a CSI reporting setting. The NZP CSI-RS resource set may include a CSI-RS set or an SSB set. For example, L1-RSRP may be measured against a CSI-RS set or against an SSB set.

[0210] The reporting type may include settings for the timing at which the terminal performs the report and the uplink channel, etc. The reporting timing may be set to periodic, semi-persistent, or non-periodic. Periodic CSI reporting may be transmitted over the PUCCH. Semi-persistent CSI reporting may be transmitted over the PUCCH or PUSCH based on MAC CE indicating activation / deactivation. Non-periodic CSI reporting may be indicated by DCI signaling. For example, the CSI request field of an uplink grant may indicate one of various report trigger sizes. Non-periodic CSI reporting may be transmitted over the PUSCH.

[0211] CSI can be defined as two types. Type 1 CSI may be associated with cases where a single user is scheduled, and Type 2 CSI may be associated with cases where multiple users are scheduled simultaneously on the same resource. Type 1 CSI may include single-panel CSI and multi-panel CSI, each of which may correspond to a different codebook. The precoding matrix in the codebook may be specified by a combination of w1 and w2. Long-term and wideband characteristics may correspond to w1, and short-term and subband characteristics may correspond to w2. While Type 1 CSI reports one precoding matrix selected by the terminal, Type 2 CSI may report information on the magnitude and phase of up to four beams.

[0212] For CSI reporting, the time and frequency resources available to the UE are controlled by the base station.

[0213] Channel state information (CSI) may include at least one of a channel quality indicator (CQI), a precoding matrix indicator (PMI), a CSI-RS resource indicator (CRI), an SS / PBCH block resource indicator (SSBRI), a layer indicator (LI), a rank indicator (RI), L1-RSRP, and / or L-SINR.

[0214] For CQI, PMI, CRI, SSBRI, LI, RI, and L1-RSRP, the terminal is configured by a higher layer with N≥1 CSI-ReportConfig reporting settings, M≥1 CSI-ResourceConfig resource settings, and a list of one or two trigger states (provided by CSI-AperiodicTriggerStateList and CSI-SemiPersistentOnPUSCH-TriggerStateList). In the CSI-AperiodicTriggerStateList, each trigger state includes an associated list of CSI-ReportConfigs indicating resource set IDs for the channel and optionally interference. In the CSI-SemiPersistentOnPUSCH-TriggerStateList, each trigger state includes one associated CSI-ReportConfig.

[0215] In addition, the time domain behavior of CSI reporting supports periodic, semi-persistent, and aperiodic.

[0216] i) Periodic CSI reporting is performed on short PUCCH and long PUCCH. The periodicity and slot offset of Periodic CSI reporting can be set to RRC, and refer to CSI-ReportConfig IE.

[0217] ii) SP (semi-periodic) CSI reporting is performed on short PUCCH, long PUCCH, or PUSCH.

[0218] In the case of SP CSI on Short / long PUCCH, periodicity and slot offset are set to RRC, and CSI reporting is activated / deactivated with separate MAC CE / DCI.

[0219] In the case of SP CSI on PUSCH, the periodicity of SP CSI reporting is set to RRC, but the slot offset is not set to RRC, and SP CSI reporting is activated / deactivated by DCI(format 0_1). For SP CSI reporting on PUSCH, a separate RNTI (SP-CSI C-RNTI) is used.

[0220] The initial CSI reporting timing follows the PUSCH time domain allocation value specified by DCI, and subsequent CSI reporting timing follows the period set by RRC.

[0221] DCI format 0_1 ​​includes a CSI request field and can activate / deactivate a specific configured SP-CSI trigger state. SP CSI reporting has the same or similar activation / deactivation mechanism as the data transmission mechanism on SPS PUSCH.

[0222] iii) aperiodic CSI reporting is performed on PUSCH and triggered by DCI. In this case, information related to the trigger of aperiodic CSI reporting can be transmitted / instructed / set via MAC-CE.

[0223] In the case of an AP CSI with AP CSI-RS, the AP CSI-RS timing is set by the RRC, and the timing for AP CSI reporting is dynamically controlled by the DCI.

[0224] NR does not apply the method of splitting CSIs across multiple reporting instances (e.g., transmitting in the order of RI, WB PMI / CQI, SB PMI / CQI) that was applied to PUCCH-based CSI reporting in LTE. Instead, NR restricts the setting of specific CSI reports in short / long PUCCHs, and CSI omission rules are defined. Regarding AP CSI reporting timing, the PUSCH symbol / slot location is dynamically determined by the DCI, and candidate slot offsets are set by the RRC. For CSI reporting, the slot offset (Y) is set per reporting setting. For UL-SCH, the slot offset K2 is set separately.

[0225] Two CSI latency classes (low latency class and high latency class) are defined in terms of CSI computation complexity. Low-latency CSI refers to WB CSIs that include up to 4-port Type-I codebooks or up to 4-port non-PMI feedback CSIs. High-latency CSI refers to any CSI other than low-latency CSIs. For a normal terminal, (Z, Z') is defined in the unit of OFDM symbols. Here, Z represents the minimum CSI processing time from receiving an Aperiodic CSI-triggering DCI to performing a CSI report. Additionally, Z' represents the minimum CSI processing time from receiving a CSI-RS for channel / interference to performing a CSI report.

[0226] Additionally, the terminal reports the number of CSIs that can be calculated simultaneously.

[0227] The activation / deactivation of a semi-persistent CSI-RS / CSI-IM resource set is directed by the network via a specific MAC CE. The configured semi-persistent CSI-RS / CSI-IM resource set is initially deactivated at the time of configuration and after handover. The MAC entity receiving the corresponding MAC CE instructs the lower layer (e.g., PHY) with the information related to that MAC CE.

[0228] Aperiodic CSI Trigger State subselection is directed from the network via a specific MAC CE, and an Aperiodic CSI Trigger State can be selected from the configured AP CSI trigger states of the serving cell. The MAC entity receiving the corresponding MAC CE instructs the lower layer (e.g., PHY) with information related to that MAC CE.

[0229] Integrated Sensing and Communication (ISAC)

[0230] Wireless sensing is a technology that utilizes radio frequencies to determine the instantaneous linear velocity, angle, and distance (range) of an object, thereby obtaining information about the characteristics of the environment and / or objects within that environment. Since radio frequency sensing capabilities do not require connecting to objects via devices within a network, they can provide services for determining object locations without the need for devices. The ability to obtain range, velocity, and angle information from radio frequency signals can provide a wide range of new functions, such as various object detection, object recognition (e.g., vehicles, humans, animals, UAVs), and high-precision localization, tracking, and activity recognition. Wireless sensing services can provide information to various industries (e.g., unmanned aerial vehicles, smart homes, V2X, factories, railways, public safety, etc.) that enable applications such as intruder detection, assisted vehicle steering and navigation, trajectory tracking, collision avoidance, traffic management, and health and traffic management. In some cases, wireless sensing may utilize non-3GPP type sensors (e.g., radar, cameras) to further support 3GPP-based sensing. For example, the operation of a wireless sensing service, that is, the sensing operation, may depend on the transmission, reflection, and scattering processing of wireless sensing signals. Therefore, wireless sensing can provide an opportunity to enhance existing communication systems from communication networks into wireless communication and sensing networks.

[0231] FIGS. 13 and FIGS. 14 illustrate examples of sensing operations according to an embodiment of the present disclosure. Specifically, FIG. 13 illustrates an example of sensing using a sensing receiver and a sensing transmitter located at the same position (e.g., monostatic sensing), and FIG. 14 illustrates an example of sensing using a separated sensing receiver and a sensing transmitter (e.g., bistatic sensing).

[0232] For example, in a wireless communication system based on a 6G network of the present specification, referring to FIG. 13, the sensing transmitter and the sensing receiver may be configured to be included in a single base station (i.e., the same base station) or a single terminal (i.e., the same terminal). Alternatively, referring to FIG. 14, the sensing transmitter and the sensing receiver may be configured to be included in different base stations, in different terminals, or in a terminal and a base station, respectively.

[0233] In this regard, based on whether the sensing transmitter and the sensing receiver are each included in a base station or a terminal, the following six types of sensing modes can be defined.

[0234] - Mode 1: A mode in which the sensing transmitter and sensing receiver are included in a single base station (e.g., base station-based sensing mode in monostatic mode)

[0235] - Second mode: A mode in which the sensing transmitter is included in the first base station and the sensing receiver is included in a second base station different from the first base station (e.g., base station-based sensing mode in bistatic mode)

[0236] - 3rd Mode: A mode in which the sensing transmitter is included in the base station and the sensing receiver is included in the terminal (e.g., base station-terminal sensing mode)

[0237] - 4th Mode: A mode in which the sensing transmitter is included in the terminal and the sensing receiver is included in the base station (e.g., terminal-base station sensing mode)

[0238] - 5th Mode: A mode in which the sensing transmitter and the sensing receiver are contained in a single terminal (e.g., terminal-based sensing mode in monostatic mode)

[0239] - 6th mode: A mode in which the sensing transmitter is included in the first terminal and the sensing receiver is included in a second terminal different from the first terminal (e.g., terminal-based sensing mode in bistatic mode)

[0240] In a wireless communication system based on a 6G network of the present specification, one or more of the six types of sensing modes described above may be utilized independently or in combination.

[0241] In relation to the sensing operation in FIGS. 13 and 14, a sensing transmitter may transmit a sensing signal for sensing one or more objects (and / or the environment surrounding the objects). For example, the sensing signal may correspond to a radio (frequency) signal defined to be transmittable by a base station / terminal in a wireless communication system based on a 6G network of the present specification. A sensing receiver may receive a signal that is scattered / reflected by one or more objects (and / or the environment surrounding the objects) from the sensing signal transmitted from the sensing transmitter. In the sensing receiver, sensing data may be derived from the scattered / reflected signal, and sensing results may be generated / obtained through processing of the sensing data. Here, the sensing result may include characteristic information (e.g., location, distance, speed, angle, etc.) about one or more objects (and / or the environment surrounding the objects). The sensing result thus generated / acquired may be utilized for wireless sensing services (e.g., detection, tracking, etc. of objects and / or environments) provided by a wireless communication system based on a 6G network of the present specification, or may be provided / disclosed to a trusted third party.

[0242] Additionally, the sensing operation in FIGS. 13 and 14 is described using a wireless communication system based on a 6G network as a representative example, but it can be extended and applied to cases where terminals / base stations / signals based on previous generations (e.g., 4G, 5G, etc.) networks are utilized.

[0243] Additionally, with respect to the wireless sensing described in this specification, in a wireless communication system based on a 6G network of this specification, time / frequency resources for sensing operations and time / frequency resources for general communication (e.g., UL / DL / sidelink-based communication, etc.) may be scheduled / configured separately.

[0244] FIG. 15 illustrates a time / frequency resource for a sensing operation according to one embodiment of the present specification. The embodiment of FIG. 15 may be combined with various embodiments of the present disclosure.

[0245] Referring to FIG. 15, the time / frequency resources (hereinafter, sensing resources) for the aforementioned sensing operation (e.g., sensing operation based on FIG. 13 and FIG. 14) can be set / assigned separately from the time / frequency resources (hereinafter, communication resources) for general communication.

[0246] For example, as illustrated in FIG. 15, sensing resources may be configured / assigned in units of symbols in the time domain and / or in units of resource blocks in the frequency domain. Resources other than those configured / assigned to the sensing resources may be utilized as resources for general communication. That is, sensing resources and communication resources may be configured / assigned based on time-division multiplexing (TDM) and / or frequency-division multiplexing (FDM) methods in terms of base station / terminal operation. Additionally or alternatively, unlike those illustrated in FIG. 13 and FIG. 14, sensing resources may also be configured / assigned based on other units in the time domain (e.g., slot, frame, absolute time (ms, us), etc.) and / or other units in the frequency domain (e.g., subcarrier, carrier, absolute frequency (MHz, GHz), etc.).

[0247] Additionally or alternatively, in relation to the setup / allocation / scheduling of resources for general communication described herein, it may be necessary to consider the relationship between said resources and the aforementioned sensing resources. For example, when setting / allocating resources for general communication according to the embodiments of the present disclosure, said resources may be set / allocated to rate-match or puncturing resource areas corresponding to the sensing resources. For example, when scheduling resources for general communication according to the embodiments of the present disclosure, said resources may be scheduled so as not to overlap with resource areas corresponding to the sensing resources. If resources for general communication and resource areas corresponding to the sensing resources are set / allocated / scheduled to overlap according to the embodiments of the present disclosure, either one or both operations may be dropped, skipped, or postponed based on priority, predefined rules, etc. That is, in the embodiments of this specification, resources related to general communication (e.g., resources for signals / channels related to UL / DL / Sidelink-based data / control, etc.) may be configured / assigned / scheduled so as not to overlap with the aforementioned sensing resources.

[0248] Additionally, various channel modeling methods may be applied in relation to the wireless sensing described herein. Channel modeling related to sensing may mean constructing a path for transmitting and receiving sensing signals and / or scattered / reflected signals by considering the object to be sensed and / or the environment to which the object belongs. Since channel modeling may be related to the performance / requirements of sensing in a wireless communication system, it may be an important matter for verifying the validity of the sensing function.

[0249] Channels related to sensing can be classified into channels between an object (e.g., target of interest) and a sensing transmitter / receiver, and channels between the environment to which the object belongs and a sensing transmitter / receiver. In this regard, channel modeling related to sensing can be classified based on the sensing mode (e.g., the six types of modes mentioned above), whether it is an object or an environment, and / or sensing scenarios. For example, channel modeling for a target in a base station / terminal-based monostatic sensing mode, channel modeling for a target in a base station / terminal-based bistatic sensing mode, channel modeling for an environment in a base station / terminal-based monostatic sensing mode, and channel modeling for an environment in a base station / terminal-based bistatic sensing mode can be optimized and configured differently. For example, when various sensing scenarios are classified, they can be divided into channel modeling for detection, location, and tracking scenarios, channel modeling for motion recognition, and channel modeling for imaging / environment reconstruction scenarios. Additionally, channel modeling related to sensing may be based on statistical channel modeling techniques and / or deterministic channel modeling techniques. For example, modeling for sensing in a wireless communication system based on a 6G network of this specification may be based on stochastic geometry channel modeling techniques and / or hybrid with ray tracing channel modeling techniques. Here, the stochastic geometry channel model may be based on various statistical characteristics of the channel state. Furthermore, the hybrid channel model may be based on both ray tracing techniques and stochastic techniques.In the case of a hybrid approach, channels for objects requiring high accuracy and consistency (e.g., targets of interest) can be modeled using ray tracing techniques, while channels for the environment can be modeled using probabilistic techniques.

[0250] artificial intelligence

[0251] The introduction of AI into communications can streamline and enhance real-time data transmission. AI can determine how complex target tasks are performed using numerous analyses. In other words, AI can increase efficiency and reduce processing latency. Time-consuming tasks such as handover, network selection, and resource scheduling can be performed instantly using AI. AI can also play a significant role in M2M, machine-to-human, and human-to-machine communication. Furthermore, AI can enable rapid communication in Brain-Computer Interfaces (BCI). AI-based communication systems can be supported by metamaterials, intelligent structures, intelligent networks, intelligent devices, intelligent cognitive radios, self-sustaining wireless networks, and machine learning.

[0252] The following describes a functional framework for AI / ML operations.

[0253] Below, to provide a more specific explanation of AI (or AI / ML), terms may be defined as follows.

[0254] - Data collection: Data collected from network nodes, management entities, or terminals, serving as a basis for AI model training, data analysis, and inference.

[0255] - AI Model: A data-driven algorithm that applies AI technology to generate a set of outputs containing predictive information and / or decision parameters based on a set of inputs.

[0256] - AI / ML Training: An online or offline process of training an AI model by learning features and patterns that best represent data and acquire an AI / ML model trained for inference.

[0257] - AI / ML Inference: A process of making predictions or deriving decisions based on collected data and AI models using trained AI models.

[0258] Life Cycle Management (LCM) procedures for AI / ML models (i.e., model training, model deployment, model inference, model monitoring, model updating, etc.) can be classified into functionality-based LCM and model-based LCM. In functionality-based LCM, AI / ML models may not be identifiable within the network, and the network can direct the activation, deactivation, fallback, or switching of AI / ML functionality. In model-ID (identifier)-based LCM, AI / ML models can be identified within the network, and the network or terminal can activate, deactivate, select, or switch AI / ML models via the model ID.

[0259] Figure 16 illustrates a general functional architecture for an AI / ML model.

[0260] In particular, FIG. 16 illustrates a general functional architecture related to both Functionality-based LCM and Model-based LCM. Some functions or some data / information / command flows (i.e., arrows) illustrated in FIG. 16 may be omitted.

[0261] Referring to FIG. 16, a general functional framework may be configured to include a data collection function (10), a model training function (20), a management function (30), an inference function (40), and a model storage function (50).

[0262] The Data Collection function (10) is a function that provides input data to the Model Training function (20), Management function (30), and Inference function (40). The Data Collection function (10) performs data preparation based on raw data and can provide input data processed through data preparation. Examples of raw data may include received data / measurement data from terminals or other network entities, inference / output of AI / ML models, etc. The Data Collection function (10) may be performed by a single entity (e.g., terminal, network node, etc.) but may also be performed by multiple entities.

[0263] Here, training data (11) refers to data required as input for the AI / ML model training function (20). monitoring data (12) refers to data required as input for the management (30) of the AI / ML model or AI / ML function. inference data (13) refers to data required as input for the AI / ML inference function (30).

[0264] The Model Training function (20) is a function that performs AI / ML model training, validation, and testing, which can generate model performance metrics that can be used as part of the AI / ML model testing procedure. If necessary, the Model Training function (20) can perform data preparation (e.g., data pre-processing and cleaning, forming and transformation) based on the Training Data (11) delivered from the Data Collection function (10).

[0265] Trained / Updated Model (21): If there is a Model Storage function (50), it is used to transfer trained, validated, and tested AI / ML models to the Model Storage function (50) or to transfer updated versions of the models to the Model Storage function (50).

[0266] The Management function (30) is a function that supervises the operation of an AI / ML model or an AI / ML function. Additionally, the Management function (30) may make decisions to ensure appropriate inference operations based on data received from the Data Collection function (10) (i.e., Monitoring Data (12)) and / or data received from the Inference function (40) (i.e., Inference Output (41)).

[0267] Management Instruction (32) is information required as input to manage the Inference function (40). The relevant information may include the selection / (de)activation / switching of an AI / ML model or an AI / ML-based function, and may also include a fallback to a non-AI / ML operation (i.e., not relying on the inference process).

[0268] A Model Transfer / Delivery Request (33) can be used to request model(s) from Model Storage (50).

[0269] Performance Feedback / Retraining Request (31) refers to information required as input to Model Training function (20) (e.g., for the purpose of retraining or updating the model).

[0270] The inference function (40) is a function that provides output from the process of applying an AI / ML model or AI / ML function using data (i.e., inference data (13)) provided by the data collection (10) as input. Data preparation (e.g., data preprocessing and cleaning, formatting and transformation) may also be performed based on the inference data (13) delivered by the data collection (10). If necessary, the inference function (40) may also perform data preparation (e.g., data pre-processing and cleaning, forming and transformation) based on the inference data (13) provided by the data collection function (10).

[0271] Inference Output (41) is data used in the Management function (30) to monitor the performance of an AI / ML model or AI / ML function. Inference Output (41) may include the inference output of an AI / ML model generated by the Inference function (30), and the details of the inference output may vary depending on the use case.

[0272] The Model Storage function (50) is a function that stores a trained / updated model that can be used to perform the Inference function (40). The Model Storage function (50) exemplified in FIG. 15 can be used as a reference point (if any) applicable to protocol termination, model transmission / delivery, and related processes. Additionally, the Model Storage function (50) is an example and is not intended to restrict the storage location of the actual AI / ML model and may be omitted.

[0273] Model Transfer / Delivery (51) is used to transfer an AI / ML model to an inference function.

[0274] Cooperation levels can be defined as follows based on the capability of AI / ML functions among multiple nodes, and variations resulting from the combination of multiple levels or the separation of any one level are also possible.

[0275] Cat 0a) No collaboration framework: AI / ML algorithms are based on pure implementation and do not require changes to the wireless interface.

[0276] Cat 0b) This level corresponds to a framework that involves a wireless interface modified to fit efficient implementation-based AI / ML algorithms but without cooperation.

[0277] Cat 1) Inter-node support is involved to improve the AI / ML algorithms of each node. For example, this applies when a specific node receives support from other nodes (for training, adaptation, etc.) and vice versa. At this level, model exchange between network nodes is not required.

[0278] Cat 2) Collaborative AI / ML tasks can be performed among multiple nodes. This level requires the exchange of AI / ML model commands or network nodes.

[0279] AI / ML models can be classified into one-side models and two-side models depending on whether training and / or inference are performed on a single node or jointly / sequentially on multiple nodes.

[0280] A one-side model can refer to an AI / ML model where inference is performed entirely by a single node (e.g., a terminal or a network). Here, the training of the AI / ML model can also be performed entirely by a single node. The training and inference of the AI / ML model may be performed by the same node, or they may be performed by different nodes.

[0281] A two-side model can refer to an AI / ML model in which joint inference is performed across multiple nodes (e.g., terminals and networks). Joint inference means that inference is performed collaboratively across multiple nodes; for example, the first part of the inference may be performed by the first node, and the remainder by the second node. Two-side models can be classified into various types as follows, depending on the training method of the AI / ML model.

[0282] - First type: An AI / ML model can be trained on a single node. In this case, joint training can be performed. The trained model can then be distributed to other nodes / entities.

[0283] - Second type: Joint training of AI / ML models can be performed on multiple nodes / entities (e.g., networks and terminals). Joint training can mean that model generation (e.g., CSI generation) and model reconstruction (CSI compression by sub-use cases) are trained in the same loop for forward activation and backward gradient. In this type, joint training can include both simultaneous training (i.e., model generation training and model reconstruction training are performed simultaneously) and sequential training (i.e., model reconstruction training is performed after model generation training).

[0284] - Third Type: Separate training of AI / ML models can be performed at multiple nodes (e.g., networks and terminals). Separate training may mean that training starts sequentially at one node and continues at another node. In this case, if the first node performs the AI / ML model first and shares the training data with the second node, the second node can perform the AI / ML model using the shared training data. For example, training for the CSI generation part may be performed by the terminal, while CSI reconstruction may be performed by the network.

[0285] Figure 17 is a diagram illustrating an inference method during AI / ML-based CSI compression, and Figure 18 is a diagram illustrating a method for performing AI / ML-based CSI prediction.

[0286] Due to the advancement of computational processing technology and AI (artificial intelligence) / ML (machine learning) technologies as described above, the nodes and terminals constituting wireless communication networks are becoming more intelligent and sophisticated. In particular, due to the intelligence of the network, it is expected that various network decision parameter values ​​can be rapidly optimized, derived, and applied according to various network environment parameters (e.g., distribution / location of base stations, distribution / location / material of buildings / furniture, location / movement direction / speed of terminals, weather information, etc.). Here, these various network decision parameter values ​​may include the transmit / receive power of each base station, the transmit power of each terminal, the precoders and beams of base stations and terminals, time / frequency resource allocation for each terminal, and the duplex method of each base station.

[0287] In the Rel-18 AI / ML study item, discussions were held regarding CSI compression based on AI / ML models to improve CSI accuracy and reduce feedback overhead in existing NR systems. As illustrated in Fig. 17, CSI compression can be operated / performed based on two-sided models. For example, a terminal can generate compressed channel information by performing inference based on a CSI generation sub-model using channel information or channel state information measured based on CSI-RS as input data, and can quantize the compressed channel information and feed it back to the base station. The base station can restore CSI (e.g., channel information measured by the terminal based on CSI-RS) by performing inference based on a CSI reconstruction sub-model using information obtained by de-quantizing feedback bits (e.g., quantized compressed channel information) received from the terminal as input data.

[0288] AI / ML models have a structural characteristic in which input and output dimensions are fixed. Therefore, multiple configuration-specific models may be required to perform CSI reporting for various CSI-RS resources, and such multiple configuration-specific models can cause storage issues for terminals, base stations, and networks. NR CSI reporting measurement / reporting can be performed based on CSI-RS(s) of configured N-ports ({1,2,4,8,16,24,32,48,64,128}, N_max=128) and M-subbands ({1,...,19}, M_max=19). In this case, a total of 10*19=190 configuration-specific models may be required to perform CSI reporting for all CSI-RS resources. Moreover, considering the massive MIMO (e.g., N_max=256, 512, etc.) and wide bandwidth environments of 6G, including Upper mid-band (e.g., 7-24GHz) and Sub-Thz (e.g., 90-300GHz), it is expected that even more models will be needed.

[0289] Referring to FIG. 17, CSI compression is operated based on a two-sided model (in terms of inference) in which AI / ML models are provided at the terminal and the base station, respectively, and operate as a pair. For convenience, the terminal-side model can be defined as an AI-encoder and the base station-side model as an AI-decoder. As illustrated in FIG. 17, the terminal applies channel information (e.g., raw channel matrix or precoder-type channel information vector (e.g., eigenvector)) measured / estimated (or, if necessary, pre-processing may be performed) as input to the AI-encoder and can generate / obtain output information from the AI-encoder. The terminal can quantize the output information and feed it back to the base station as AI-CSI. In this case, the base station can generate / acquire the output CSI of the AI-decoder by applying the de-quantized information of the feedbacked AI-CSI as input to the AI-decoder. Since the channel information to be actually sent is compressed and fed back through an AI / ML-based model through this process, the feedback overhead can be reduced, so this is called CSI compression.

[0290] Referring to FIG. 18, a UE-side model is illustrated that performs model inference in the sense that the CSI prediction is equipped with an AI / ML model only on the terminal-side. For example, the CSI prediction may be an AI / ML-based channel estimation method that applies a plurality of historical measurements as input to the AI / ML (or AI / ML model) and estimates at least one future CSI (or at least one time point) as the output of the AI / ML model.

[0291] Specifically, the CSI report configuration may include configuration information for CSI prediction. For example, configuration information for the CSI prediction may be provided through predictionConfiguration-r19 within the CSI-ReportConfig information element. Specific parameters may be as follows.

[0292] - csi-InferencePrediction-r19 may be an activation parameter that instructs the terminal to perform an AI / ML-based inference function for CSI prediction. Meanwhile, the execution of CSI prediction based on Re1-18 (e.g., Doppler CSI-based CSI prediction) and configuration information for this can be instructed / configured through predictionDelay-r18 of CodebookConfig-r18. Such a CSI prediction / reporting method can be instructed at the CSI report setting level. For example, the base station can transmit multiple report settings to the terminal through csi-ReportConfigToAddModList within CSI-MeasConfig, and for each report setting (reportConfigId), it can independently set whether to perform Rel 18-based CSI prediction (e.g., non-AI / ML-based CSI prediction) and / or Rel-19-based CSI prediction (e.g., CSI prediction using AI / ML-based inference).

[0293] - resourcesForChannelPrediction-r19: Indicates future target resources that need to be predicted by the model, rather than resources where actual measurements are performed.

[0294] - nrofTimeInstance-r19: Specifies the number of future time instances to report as prediction results.

[0295] - timeGap-r19: Defines the interval between a reference time point and the first prediction time point, or the time interval between consecutive prediction time points.

[0296] - nrofReportedPredictedRS-r19: Determines the number of predicted resources (RS) to report to the base station at each reporting time.

[0297] -associatedIdForChannelPrediction-r19 and associatedIdForChannelMeasurement-r19: Represent identifiers associated with the resource to be predicted and the resource to be measured, respectively, and are configured as necessary.

[0298] In addition, the CSI report configuration may include monitoring configuration information for monitoring the performance of the AI ​​model related to the aforementioned CSI prediction. For example, configuration information for CSI monitoring may be provided through the parameters of predictionConfiguration-r19 of the CSI report configuration (CSI-ReportConfig), and specific parameters may be as follows.

[0299] - configurationForBM-Monitoring-r19 includes configuration information for monitoring beam management performance, and configurationForCSI-Monitoring-r19: may include configuration information for monitoring / verifying the prediction performance of the CSI prediction model.

[0300] - refToPredictionConfig-r19 may include information indicating a CSI prediction report configuration ID (CSI-ReportConfigId) connected to / associated with monitoring configuration information.

[0301] - mappingToResourcesForChannelPrediction-r19 may include bitmap mapping information to link actual measurable monitoring resources with previously performed prediction results on a one-to-one basis. For example, mappingToResourcesForChannelPrediction-r19 may indicate which resources among the actual channel measurements (resourcesForChannelMeasurement) performed by the terminal correspond to which prediction target resources (resourcesForChannelPrediction) of the referenced prediction report (refToPredictionConfig).

[0302] - timeInstanceForRS-PAI-r19 and timeInstanceForCSI-PAI-r19 may be parameters for specifying a specific time instance or Doppler domain unit used for calculating the Performance Indicator (PAI).

[0303] - nrofBestBeamForMonitoring-r19 specifies the number of optimal beams to consider for performance measurement, and nrofTransmissionOccasion-r19 may include information on the number of recent transmission opportunities to include in the calculation of performance metrics.

[0304] Furthermore, the CSI reporting settings include parameters for "reportQuantity-r19," which may contain information to determine which of the predicted outcomes to report. "reportQuantity-r19" may indicate predicted resource indicators (p-CRI (predicted CSI-RS resource indicator), p-SSB-Index (predicted SSB index)) or prediction accuracy information (rs-PAI, csi-PAI), etc., as information to report / feedback. Each of rs-PAI and csi-PAI may be an indicator that indicates the reporting of accuracy information for beam management (BM) monitoring and general CSI prediction monitoring.

[0305] UE behavior depending on AIML based CSI prediction capability

[0306] Figure 19 is a diagram illustrating a signaling procedure related to the reporting of capability information and applicability in relation to AI / ML-based CSI prediction.

[0307] To support dynamic systems based on advanced networks / base stations, it is possible to assume a case where wireless communication services are supported based on AI / ML. For example, AI / ML-based CSI prediction can be considered. Currently, for Rel-19 AI / ML-based CSI prediction, there is a consensus to report predicted CSIs using the Rel-18 codebook. In this case, there may be no explicit signaling from the base station to the Rel-19 UE (e.g., a terminal supporting both Rel-18 and Rel-19) regarding the CSI prediction method (e.g., AI / ML-based prediction or non-AI / ML-based prediction). In this case, it may be ambiguous whether the terminal should operate based on Rel-18 CSI or Rel-19 CSI for CSI prediction. Considering these issues, the following describes in detail how to determine which CSI prediction method to use for CSI prediction operations through implicit signaling for terminals that support both Rel-18 and Rel-19.

[0308] Standardization of AI / ML-based CSI prediction is currently under discussion in Rel-19. In this regard, the consensus points shown in Table 5 below were discussed.

[0309] AgreementFor CSI prediction using UE-side model, at least for inference, Rel-18 CSI framework is reused.- For CSI-RS resource type for CMR, periodic, semi-persistent, and aperiodic CSI-RS are supported.- For inference report,--for N4>=1, support Rel-18 codebook ('typeII-Doppler-r18')

[0310] Referring to Table 5, terminals supporting Rel-19 AI / ML-based CSI report predicted CSIs by reusing the Rel-18 codebook ('typeII-Doppler-r18', hereinafter collectively referred to as the Rel-18 codebook). In this case, the biggest difference between terminals supporting Rel-18 predicted PMI and terminals supporting Rel-19 AI / ML may be the difference in the algorithm for CSI prediction. For example, in the case of Rel-18 terminals, CSI prediction is performed based on non-AI / ML, typically based on adaptive filters such as Kalman filters and auto-regression, and the predicted CSI is generated and reported to the base station using the Rel-18 predicted PMI. In contrast, for a Rel-19 terminal that supports AI / ML-based CSI prediction, the terminal can predict the CSI based on a UE-sided model to generate the predicted CSI (via inference operation) and reuse a Rel-18 codebook to report information about the predicted CSI to the base station.

[0311] Meanwhile, regarding functionality-based Life Cycle Management (LCM) for UE-side models for beam management use cases in reporting the availability or applicability of AI / ML functionality, RAN2 reviewed and discussed the signaling procedures for applicable functionality reporting. RAN2 agreed on the terms as set forth in Table 6 below.

[0312] Supported functionalitiesrefer to functionalities that UE can indicate by using UE capability information (via RRC / LPP signaling)Applicable functionalitiesrefer to functionalities that the UE is ready to apply for inferenceActivated functionalitiesrefer to functionalities already enabled for performing inference

[0313] RAN2 agreed on a signaling scheme as illustrated in FIG. 19 regarding applicable functionality reporting for the beam management UE-side model.

[0314] Specifically, referring to FIG. 19, in step 1, the network may send a UECapabilityEnquiry message to the UE to initiate a reporting procedure for the UE's AI / ML support capabilities.

[0315] In step 2, the UE can send a UECapabilityInformation message containing the capabilities supported on the UE side to the network.

[0316] In Step 3, the following settings may be provided from the NW to the UE.

[0317] - UEs can be allowed to perform UAI (UE Assistance Information) reporting through OtherConfig.

[0318] - The network may provide additional network-side conditions. The RRC signaling method and whether it is mandatory or optional will be reviewed later (FFS).

[0319] - Settings for supported functions (e.g., inference configuration) will be reviewed later (FFS).

[0320] (Between Step 3 and Step 4) The UE may determine applicable functionalities based on additional NW-side conditions (if provided), additional UE-side conditions (conditions recognized internally by the UE), and model availability within the terminal. Whether other configurations (e.g., inference configurations) can also be considered by the UE may be reviewed later (FFS). Additionally, if additional NW-side conditions are not provided in Step 3, how applicable functionalities are determined may also be reviewed later (FFS).

[0321] In Step 4, the UE can report applicable functionality in the following scenarios.

[0322] - When configured to provide applicable functionality via UAI, and when a change occurs in the applicable functionality

[0323] In response to additional conditions on the NW side requesting a report on applicable functionality in Step 3, other network configurations (e.g., inference configuration) may be reviewed later (FFS).

[0324] In step 5, if an inference configuration based on supported functionality is not provided in step 3 (e.g., if an inference configuration is provided in step 5), the network may provide an inference configuration to the UE after reporting on applicable functionality. If an inference configuration based on supported functionality is provided in step 3, whether to provide an updated inference configuration may depend on the network implementation.

[0325] RAN2 also agreed that if an inference setting is provided in step 5, an applicable functionality may be enabled as the UE receives the inference setting. Additionally, if an inference setting for a supported functionality is provided in step 3, further review may be required regarding the initial state of the applicable functionality, whether additional L1 / L2 signaling is required for enabling / disabling, whether multiple applicable functions can be enabled simultaneously, and functionality granularity.

[0326] Tables 7 through 9 below describe the agreements related to RAN 2.

[0327] 1. UAI is supported and RRCReconfigurationComplete message can be used to report applicable functionality. We should aim to align the design on how the applicable functionality are signaled. FFS on the applicability reporting content.2. FFS if inference configuration can be signalled in step3.3. UE can report to the network when an applicable AI functionality becomes non-applicable. FFS how this is signaled (e.g. explicitly / implicitly). Consider different scenarios, whether it is regarding an active functionality)4. Data collection initiation and configuration for data collection is under network control. FFS how the NW determines whether data collection should be initiated (e.g. via UE requests (UE directly or UE server)5.For the purpose of discussion of AI / ML BM LCM operations, existing procedures and terminologies from the CSI Framework should be used, including those defined for aperiodic, semipersistent on PUCCH, semipersistent on PUSCH, and periodic reporting configurations (as / if defined in RAN1 pending response LS from RAN1).6. For now, RAN2 will not define terminology specific to the activation or deactivation for AI / ML models. Can come back to this discussion later.

[0328] Agreements1. When a functionality configured by the network to be reported via UAI, becomes from non-applicable to applicable, the UE can reports it to the network. FFS detailed design2. When a functionality becomes non-applicable the UE doesn't autonomously deactivate. NW is expected to deactivate active functionality when it receives report from UE that it is non-applicable.3. FFS whether the UE reports explicitly "non-applicable" functionality when there is a change of applicability. Verify this aligns with RAN1 configuration design4. Applicable functionality reporting at handover is supported with the same RRC procedure that will be specified within a cell, as a baseline, i.e. the NW-side additional conditions and / or the inference configuration related to the target gNB are transmitted by the target gNB as part of the HO command, and the UE in response transmits the applicability report (either in RRCReconfigurationComplete or in UAI) to the target gNB after completing the handover.5. Source cell UAI (as is) can be sent from source cell to target cell using existing signaling. No further optimizations will be considered in RAN2 related to UAI.6. For BM use case for UE-side model, data collection related configuration(s) (e.g., measurement resources configuration) and associated ID(s) can be included in training data collection configuration.7. For data collection configuration UE-side model training, the UE can send a request for data collection. FFS what the request contains.8. The network can provide the data collection configuration (at any point in time), with or without UE request.9. The following methods for network control of the initiation and configuration for data collection:- The network can decide when to start / stop the data collection and send configuration.- The network can configure whether UE is allowed to initiate request for data collection.10.FFS whether an indication from UE to network is needed when UE can't perform data collection based on received configuration.

[0329] Agreements1. Inference configuration / parameters can be signalled in step 3 and / or Inference configuration can be signalled in step 5 (i.e. option a and option b from RAN1).2. The full inference configuration is sent in CSI-ReportConfig.3. Upon receiving a full inference configuration, the UE sends the initial applicability report in RRCReconfigurationComplete. UAI can be sent to update applicability.4. FFS signaling details for option B (e.g. whether it is signaling in CSI-Report Config or otherconfig)Agreements applicability reporting and managementSupport the explicit reporting of applicability / inapplicability in initial report and subsequent reporting it reports only applicability it changed. FFS if we report explicit causeIf option A is configured in Step 3, for periodic CSI reporting, the UE autonomously activate the applicable functionalities upon reporting applicable functionalities via RRCReconfigurationComplete in step 4 (i.e. without need to wait RRCReconfiguration in Step 5).The provided periodic CSI configuration should be consistent with reported UE capabilitiesFFS option BSemi-persistent and aperiodic CSI reporting of applicable functionality is activated following legacy CSI framework:Semi-persistent reporting, activated by MAC CE / DCIAperiodic CSI reporting, activated by DCI.

[0330] Tables 10 and 11 below describe the agreements related to RAN 1.

[0331] Agreement>In Step 3, following configurations are provided from NW to UE:- UE is allowed to do UAI reporting via OtherConfig,- The applicability report is based on A) and / or B)-- It is up to RAN 2 to design the container-- A) one or more of CSI-ReportConfig for inference configuration (wherein the associated ID may be configured in CSI framework as working assumption applied). Note: CSI report configuration for UE-side model inference can't be activated immediately upon receiving Step 3-- B) One set or multiple sets of inference related parameters for applicability report only (not for inference)--- It is up to RAN2 to design the container.--- The set of inference related parameters selected from the IEs in / or the IEs referred by CSI-ReportConfig as a starting point, e.g., the associated ID (Note: this doesn't imply the associated ID is mandatory), Set A related information, Set B related information, Report content related information, For BM-Case 2 (Time instances related information for measurements or Time instances related information for prediction)>In Step 4,UE reports applicability for all the above A) one or more CSI-ReportConfig and / or B) set(s) of inference related parameters- FFS on whether / what other information along with the applicability is needed- If A) is configured in Step 3,-- Applicable aperiodic CSI Report and semi-persistent CSI report can be activated / triggered by NW after the applicability reported.-- Applicable periodic CSI Report is considered as activated only if the applicability of the corresponding CSI-ReportConfig is reported in RRCReconfigurationComplete.> In Step 5,NW can optionally configure CSI-ReportConfig for inference configuration in RRCReconfiguration, where the associated ID may be configured in CSI framework as working assumption applied.- Note: Step 5 may be optional if UE has already been configured with CSI-ReportConfig in Step 3Conclusion> For the CSI-ReportConfig for inference configuration provided in Step 5,- aperiodic CSI Report and semi-persistent CSI report can be activated / triggered by NW after RRCReconfigurationComplete.- periodic CSI Report is considered as activated after RRCReconfigurationComplete.- Note: UE is not expected to be configured with a CSI-ReportConfig for inference configuration for a non-applicable set of inference parameters or a non-applicable CSI-ReportConfig. Any specification impact is a separate discussion.

[0332] AgreementFor UE sided model in beam management, support associated ID[Working Assumption]> The associated ID at least can be configured within CSI framework- FFS on details- FFS on whether / how to configure / indicate the associated ID via other signal(s) and / or in other procedure(s) / framework(s)> UE may assume the similar properties of a DL Tx beam or beam set / list associated with the same associated ID- FFS: whether / how to define similar properties of a DL Tx beam or beam set / listAgreementFor UE-sided model, for configuring the resource for data collection purpose, support> CSI-ReportConfig can used for configuring the resources for data collection purpose without CSI report.- One CSI-ResourceConfigId is configured for Set A.- One CSI-ResourceConfigId is configured for Set B.- Note: UE performs measurement on all resources- One or two associated IDs can be configured in CSI-ReportConfig-- When Set B is equal or a subset of set A (i.e., NZP-CSI-RS-ResourceId / SSB-Index in the resource set for Set B is within the NZP-CSI-RS-ResourceId / SSB-Index in the resource set for Set A), one associated ID is configured,-- Otherwise, one associated ID is configured for Set A and another one associated ID is configured for Set B> FFS: whether / how to support 'aperiodic' CSI RSNote: This is not related to whether / how to support delivery / transmission of the collected data for training for UE-sided model.AgreementFor UE-sided model, in CSI-ReportConfig for inference> One or two associated IDs can be configured in CSI-ReportConfig- When Set B is equal or a subset of set A (i.e., NZP-CSI-RS-ResourceId / SSB-Index in the resource set for Set B is within the NZP-CSI-RS-ResourceId / SSB-Index in the resource set for Set A), one associated ID is configured,- Otherwise, one associated ID is configured for Set A and another one associated ID is configured for Set B> FFS: At least BM-Case 1, the applicability for 'aperiodic' CSI RS.

[0333] To summarize the aforementioned discussion and / or agreement points, similar to existing UE capability reporting, the terminal can perform reporting on AI / ML supported functionality (functionalities) in Step 2, and in Step 4, report to the NW regarding currently available functionality among these AI / ML supported functionality (functionalities) through applicable functionality reporting. This reporting can be performed via UAI reporting through OtherConfig or / and signaling of RRCReconfigurationComplete. This reporting can be utilized to report available functionality (functionalities) and / or unavailable functionality (functionalities). Additionally, since the terminal's status regarding available / unavailable functionality may change after the initial report, a subsequent report may be performed. Although the aforementioned agreement points are intended for beam management (BM), they can also be applied to CSI forecasting, and associated IDs may not be introduced.

[0334] In the following, taking into account the aforementioned agreements, a method is described in detail for a Rel-19 terminal that supports both Rel-18 CSI prediction (or non-AI / ML prediction) and Rel-19 AI / ML CSI prediction (or AI / ML CSI prediction) to implicitly determine which method, Rel-18 CSI prediction or Rel-19 AI / ML CSI prediction, to perform CSI prediction without explicit instruction.

[0335] As described above, for a Rel-19 terminal that supports both Rel-18 CSI prediction (or, non-AI / ML prediction) and Rel-19 AI / ML CSI prediction (or, AI / ML CSI prediction), it may be ambiguous whether to operate based on Rel-18 CSI or Rel-19 CSI for CSI prediction.

[0336] In this regard, if a Rel-19 (or release-19) terminal supports both Rel-18 CSI prediction and Rel-19 AI / ML CSI prediction, the base station may explicitly instruct / configure how the Rel-19 terminal performs CSI prediction through CSI reporting settings. The RRC parameters for this may be as follows.

[0337] - codebookConfig-r19: Introduces / defines new parameters for codebookConfig-r19 identically to codebookConfig-r18, and sets only the tag for release separately.

[0338] - AI-function: Newly introduced / defined parameters specifying functionality units such as AI-CSI-prediction, AI-BM-CASE1 (spatial domain beam prediction), and AI-BM-CASE2 (temporal domain prediction).

[0339] - AI-CSI-prediction: Newly introduced / defined a parameter as a separate indicator to direct the performance of Rel-19 AI-CSI prediction (since BM cases can be distinguished by reporting quantity, only AI-CSI prediction can be introduced)

[0340] Alternatively, for CSI reporting regarding Rel-19 AI functions, separate CSI reporting settings can be defined / introduced as follows.

[0341] - AI-CSI-reportConfig: Defines / introduces separate CSI reporting settings for AI CSI / BM

[0342] If separate parameters or explicit indicators as described above are introduced, ambiguity in CSI prediction for Rel-19 terminals that support the capability for Rel-18 CSI prediction and Rel-19 AI-CSI prediction can be resolved. However, if the aforementioned explicit indicators / parameters are not introduced, it is necessary to distinguish between Rel-18 CSI prediction and Rel-19 AI-CSI prediction through an implicit method as described in Proposal 1 below.

[0343] In Proposal 1, the Rel-19 terminal may be implicitly instructed whether to base the CSI prediction on the Rel-18 CSI prediction or the Rel-19 CSI prediction based on the implicit instruction method described below.

[0344] (1) Option 1

[0345] In Step 4 of FIG. 19, the terminal performs a CSI prediction operation based on Rel-19 AI-CSI prediction for CSI reports that are activated / triggered after performing the terminal's Applicability report, and for the remaining CSI reports other than the above CSI reports, if the Rel-18 codebook is set, it can perform a CSI prediction operation based on Rel-18 CSI prediction.

[0346] Alternatively, for candidate CSI reports that can be set based on method A) or method B) in step 3 of FIG. 19, a CSI prediction operation based on Rel-19 AI-CSI prediction may be performed, and for the remaining CSI reports other than the above CSI reports, a CSI prediction operation based on Rel-18 CSI prediction may be performed.

[0347] (2) Option 2

[0348] In Option 2, a CSI report configuration ID (config id) associated with an inference report may be configured within the CSI report configuration configured for monitoring reporting, and for the inference CSI report linked to the monitoring CSI report, a CSI prediction operation based on Rel-19 AI-CSI prediction may be performed, and for the remaining CSI reports other than the CSI report, a Rel-18 codebook may be configured, and a CSI prediction operation based on Rel-18 CSI prediction may be performed.

[0349] (3) Option 3

[0350] In Option 3, a CSI report configuration ID associated with a monitoring report may be configured within a CSI report configuration configured for inference reporting. In this case, the terminal may perform a CSI prediction operation based on Rel-19 AI-CSI prediction (or perform Rel-19 AI-CSI prediction) for an inference CSI report linked to the monitoring CSI report (e.g., an inference CSI report reported based on a CSI report configuration corresponding to the CSI report configuration ID), and may perform a CSI prediction operation based on Rel-18 CSI prediction when a Rel-18 codebook is configured for the remaining CSI reports other than the CSI report.

[0351] In the above-mentioned Option 1, the following two methods may be supported in the Applicability report.

[0352] - Method A) one or more of CSI-reportConfig for inference configuration

[0353] - Method B) One set or multiple sets of inference related parameters for applicability report only (not for inference)

[0354] The above-described method A) may mean setting various parameter combinations for inference in one or more CSI-reporting configurations, and the terminal may perform a report on whether one or more of the CSI-reporting configurations is applicable or non-applicable at a specific point in time (e.g., at the time of reporting or at the time of the Applicability report). For example, in method A, one or more CSI-reportConfigs with inference-related parameter combinations may be configured, and the UE may report the applicable CSI-reportConfig and / or the non-applicable CSI-reportConfig among the one or more CSI-reportConfigs at a specific point in time (e.g., at the time of the Applicability report).

[0355] In the case of Method B), it may mean performing a report on whether an inference reporting setting based on a single or multiple sets of inference-related parameters that can be set within a CSI-reporting setting for inference is applicable or non-applicable at a specific point in time (e.g., at the time of reporting). For example, according to Method B, one or more sets of inference-related parameters that can be included in a CSI-reporting setting for inference may be set in the terminal, and the terminal may report whether each parameter set or the reporting setting associated therewith is applicable at the time of Applicability reporting.

[0356] In the case of the aforementioned Options 2 and 3, it was assumed that the CSI reporting settings for inference and monitoring are configured to be separate. For example, in the case of Option 3, if the terminal does not perform capability reporting for Rel-18 CSI prediction (e.g., if the terminal does not set Rel-18 CSI prediction as a prerequisite for Rel-19 AI CSI prediction and reports capability separately for each feature, the terminal may not perform capability reporting for Rel-18 CSI prediction), then in a situation like Options 2 / 3, the CSI reporting related to monitoring may not be linked to the inference reporting settings. In this case, even if the terminal lacks capability for Rel-18 CSI prediction, the execution of predicted CSI reporting based on Rel-18 CSI prediction may be compelled. In other words, the above options 2 and 3 are based on the premise that the CSI reporting settings for inference and the CSI reporting settings for monitoring are configured separately. In option 3, if the monitoring-related CSI reporting is not linked to the inference reporting settings, a prediction CSI reporting based on Rel-18 CSI prediction may be enforced even if the terminal does not report Rel-18 CSI prediction capability.

[0357] Alternatively, if Rel-18 CSI prediction is set by the explicit method described above (e.g., if non-AI / ML based CSI prediction is explicitly set for the first CSI reporting setting), the terminal may perform non-AI based CSI prediction. However, similar to Option 2 of Proposal 1, the CSI reporting ID of the non-AI based CSI prediction may be included within the monitoring CSI reporting setting. In this case, the terminal may calculate a monitoring metric (indicator; e.g., SGCS (squared generalized cosine similarity), NMSE (normalized mean squared error)) and / or an output (e.g., a count value indicating how many times the threshold has been exceeded, calculated based on the threshold when a request for functionality fallback and / or a threshold for monitoring is indicated) based on the non-AI CSI prediction, and report this to the base station. For example, the terminal may calculate the value (or indicator value) of a monitoring metric, such as SGCS or NMSE, for non-AI CSI prediction, or calculate a count value for the number of times the metric value exceeds (or falls below) the threshold based on the threshold. In this case, the terminal may report the value of the metric calculated for the non-AI CSI prediction and / or the count value to the base station. The purpose of this configuration is that when a Rel-18 terminal performs Rel-18 CSI prediction, the base station may not be able to properly determine the effectiveness of the Rel-18 CSI prediction due to the absence of a monitoring mechanism. However, when a Rel-19 terminal is capable of operating Rel-18 CSI prediction, the base station receives performance monitoring reports regarding non-AI-based CSI prediction and AI-based CSI prediction through the method described above, and a specific mode (e.g.A prediction method (determined by judging which of the non-AI-based prediction method and the AI-based prediction method shows better performance) can be instructed / set to the terminal. When the terminal calculates the value of the above monitoring metric or metric, the terminal may additionally report a preferred mode to the base station regarding whether to operate with Rel-18 CSI prediction or Rel-19 CSI prediction. For example, setting / instructing the monitoring setting information to report monitoring information even for Rel-18 prediction-based CSI measurement reports in this manner may be intended to allow the base station to determine which prediction method, Rel-18 CSI prediction or Rel-19 CSI prediction, is appropriate to apply to the Rel-19 terminal that also supports Rel-18 CSI prediction. Alternatively, it may be intended to have the terminal report its preferred prediction method or preferred mode to the base station.

[0358] For example, Rel-18 CSI prediction (non-AI / ML-based CSI prediction) may be explicitly set for a specific CSI reporting setting (or, specific CSI reporting setting ID). In this case, the terminal may perform non-AI-based CSI prediction for the specific CSI reporting setting. At this time, information regarding the specific CSI reporting setting ID may be included in the monitoring setting information (e.g., as in Option 2 of Proposal 1). In this case, the terminal may also perform a monitoring operation for the execution of non-AI-based CSI prediction. For example, based on the specific CSI reporting setting, the terminal may use a non-AI CSI prediction method to calculate an SGCS or NMSE value between a predicted CSI value for a future RS resource (e.g., future RS slot) and an actual measured CSI value for a specific RS resource associated with or corresponding to the future RS resource (e.g., a monitoring RS resource set by the monitoring setting information), and report the calculated SGCS or NMSE value to the base station. Alternatively, if the terminal receives a first threshold related to SGCS and / or a second threshold related to NMSE from the base station, the terminal may count the number of times the calculated SGCS value is less than or equal to the first threshold and / or the number of times the calculated NMSE value is greater than or equal to the second threshold, and report the count information to the base station as output information of the monitoring or monitoring information.

[0359] Alternatively, the terminal may not expect the CSI report ID for non-AI-based CSI prediction to be included in the monitoring setting (or monitoring CSI report setting) or the CSI report setting configured for data collection (e.g., CSI report ConfigType='none' or where the purpose of data collection is specified).

[0360] For example, the terminal performs a report on the capability and applicability related to CSI prediction, receives CSI-RS information related to the CSI report, and can predict the CSI at a future point in time based on the received configuration information. In this case, the terminal may determine whether to perform AI-based CSI prediction and whether to perform non-AI-based CSI prediction based on specific indicators, or based on the presence or absence of applicability report and monitoring report settings. Additionally, if the reporting setting for non-AI-based CSI prediction is associated with a monitoring report, the terminal may calculate monitoring metrics and / or monitoring outputs based on the CSI prediction values ​​according to the non-AI-based CSI prediction and report them to the base station.

[0361] Figures 20 and 21 are diagrams illustrating the signaling procedure between a base station and a terminal in relation to the reporting of predicted CSI.

[0362] Referring to FIG. 20 (a), the terminal may report to the network terminal capabilities related to AI-based or non-AI-based CSI prediction and may report on applicable functions for AI-based CSI prediction (S201-1). Subsequently, the terminal may receive configuration information related to CSI reporting and a plurality of CSI-RS from the base station (S202-1). The terminal may perform measurement, prediction, and / or calculation on the received CSI-RS based on an AI / ML model (e.g., inference operation based on an AI / ML model) (S203-1). The terminal may report the measured, predicted, and / or calculated CSI (e.g., predicted CSI) to the base station (S204-1). Subsequently, the terminal may receive scheduling for a downlink channel from the base station (e.g., receiving DCI or PDCCH) and receive the downlink channel and / or signal (e.g., PDSCH) transmitted by the base station (S205-1). At this time, some steps in the above process may be omitted or the order may be changed.

[0363] Referring to FIG. 20 (b), the base station may receive an applicability report and an capability report from the terminal, which include information regarding the capability of supported AI / ML models or functions, and the capability of performance monitoring (S201-2). Subsequently, the base station may transmit the activation of an AI / ML model configured or installed on the terminal, and may transmit configuration information related to the CSI report and CSI-RS(s) based on said configuration information (S203-2). Additionally, the base station may receive measured, predicted, and / or calculated CSI information from the terminal (S205-2). Subsequently, the base station may schedule a downlink channel based on the CSI reported by the terminal and transmit said downlink channel to the terminal (S201-2). At this time, some steps in the above process may be omitted or the order may be changed.

[0364] Referring to FIG. 21, the terminal may report terminal capability information and AI applicability function information to the base station, and the base station may transmit CSI-RS configuration information (and / or, monitoring configuration information or CSI monitoring configuration information) and CSI-RS to the terminal. The terminal may report CSI prediction results to the base station. And / or, the terminal may report monitoring information or monitoring output information related to the CSI prediction to the base station based on the monitoring configuration information. The base station may transmit PDSCH scheduling information and PDSCH based on the reported CSI prediction results. Additionally, the base station may determine whether to change the CSI prediction method for the UE based on the monitoring output information and transmit control information regarding this to the UE.

[0365] Figure 22 is a diagram illustrating how a UE reports monitoring information related to CSI prediction to a base station.

[0366] Referring to FIG. 22, the UE can receive monitoring setting information related to CSI prediction from a base station (S221). The monitoring setting information may be a setting for performing performance monitoring for a specific CSI prediction and may include information for identifying which CSI prediction the UE will monitor. For example, the monitoring setting information may include a CSI reporting setting ID that identifies an associated CSI reporting setting, and the UE can determine which CSI reporting setting the monitoring setting information corresponds to / is associated with based on the CSI reporting setting ID. For example, the UE can identify the CSI reporting setting to be monitored using the CSI reporting setting ID included in the monitoring setting information. Accordingly, even if multiple CSI reporting settings are configured, the UE can use the CSI reporting setting ID to identify the reporting setting related to a specific CSI prediction and perform monitoring of the performance of the corresponding CSI prediction. Here, the CSI reporting setting associated with the monitoring setting information may be either a first CSI reporting setting for AI / ML-based CSI prediction or a second CSI reporting setting for non-AI / ML-based CSI prediction. Accordingly, the UE can determine, solely by receiving the monitoring setting information, which prediction method—AI / ML-based CSI prediction or non-AI / ML-based CSI prediction—corresponds to the performance evaluation monitoring performed thereafter.

[0367] And / or, the CSI reporting setting may include instruction information (e.g., predictionConfiguration-r19) regarding whether it is the first CSI reporting setting or the second CSI reporting setting. For example, a base station may configure the CSI reporting setting as the first CSI reporting setting when AI / ML-based CSI prediction is applied to a specific CSI reporting setting, and may configure the CSI reporting setting as the second CSI reporting setting when Rel-18-based or non-AI / ML-based CSI prediction is applied. Accordingly, the UE may determine whether the CSI prediction is based on AI / ML-based CSI prediction or non-AI / ML-based CSI prediction based on the instruction information included in the CSI reporting setting. For example, the CSI reporting setting may include a parameter indicating that it is the first CSI reporting setting for AI / ML-based CSI prediction. Alternatively, the UE may determine whether the CSI reporting setting is the first CSI reporting setting for AI / ML-based CSI prediction based on the reporting time of the applicable function described above.

[0368] Meanwhile, the above monitoring setting information can be set through CSI reporting settings. For example, a CSI reporting setting including the above monitoring setting information may include parameters of configurationForCSI-Monitoring-r19 and may include refToPredictionConfig-r19 which includes information indicating a CSI prediction report setting ID (CSI-ReportConfigId) connected / associated with the above monitoring setting information.

[0369] Additionally, the monitoring setting information may include information about the monitoring resource on which monitoring is performed. The monitoring resource may be a resource for the UE to measure the actual channel state, and the actual measurement result may be the past or current measurement value itself, or may be used as a reference value for comparison with a previously performed prediction result. For example, the UE may evaluate prediction performance using the actual CSI measured from the monitoring resource and the predicted CSI (or CSI prediction value) obtained according to a specific CSI prediction. For example, as described above, the monitoring setting information may include bitmap mapping information (mappingToResourcesForChannelPrediction-r19) for connecting the actual measurable monitoring resource and the previously performed prediction result on a 1:1 basis, and based thereon, the monitoring resource to perform monitoring may be indicated / set.

[0370] and / or, the UE can predict a CSI value for a set / directed future point in time based on the CSI reporting setting (e.g., inferred CSI reporting setting) and can report information about the predicted CSI value for the future point in time to the base station as feedback information.

[0371] Next, the UE can monitor the performance of the CSI prediction performed based on the CSI reporting settings associated with the monitoring setting information (S223). For example, the UE can perform CSI prediction according to the CSI prediction method indicated by the CSI reporting settings associated with the monitoring setting information, and evaluate the prediction accuracy or performance of the CSI prediction using the prediction result (e.g., CSI prediction value) and the actual measurement result obtained from the monitoring resource. Here, whether the CSI reporting settings associated with the monitoring setting information are for AI / ML-based CSI prediction or for non-AI / ML-based CSI prediction can be determined based on separate instruction information, related settings, reporting time, activation time, or trigger time, etc., as described above.

[0372] For example, if the above CSI reporting setting is a second CSI reporting setting for non-AI / ML-based CSI prediction, the UE can calculate prediction accuracy information (e.g., values ​​of monitoring metrics, monitoring output information) based on the predicted value according to the non-AI / ML-based CSI prediction and the actual measured value of the monitoring resource. For example, the UE can calculate the squared generalized cosine similarity (SGCS) value or the normalized mean squared error (NMSE) value between the predicted CSI and the actual measured CSI. The SGCS or NMSE can be used as an indicator representing the accuracy or performance of the non-AI / ML-based CSI prediction. Accordingly, this provides an opportunity for the UE to measure and report the performance of non-AI / ML-based CSI predictions, which were not previously subject to performance monitoring.

[0373] The UE may transmit prediction accuracy information to the base station based on the results of the above monitoring (S225). The prediction accuracy information may include information regarding SGCS values, NMSE values, or at least one of these. For example, the UE may directly report performance indicators calculated at each monitoring point, or may report information summarizing multiple measurement results. In addition, the prediction accuracy information may include not only simple numerical values ​​but also information indicating whether specific criteria are satisfied or whether there is repetitive performance degradation. For example, the prediction accuracy information may further include information regarding the number of times the SGCS value is calculated to be below (or less than) the first performance threshold or the number of times the NMSE value is calculated to be above (or greater than) the second performance threshold. The first performance threshold and the second performance threshold may be set in the UE by the base station or may be pre-set and stored in the UE. The UE may count the number of times the threshold conditions are satisfied using the SGCS or NMSE calculated for multiple monitoring points and report the count result to the base station as part of the prediction accuracy information. Such count information can be used as information indicating the stability or frequency of performance degradation of a specific CSI prediction method.

[0374] And / or, the UE may report to the base station information regarding its preferred prediction method between AI / ML-based CSI prediction and non-AI / ML-based CSI prediction based on the prediction accuracy information. For example, if the UE determines, based on monitoring results, that a particular prediction method has superior performance or is more stable, it may report that method as the preferred method. The base station may use the prediction accuracy information and / or preferred method information reported by the UE to set or change the CSI prediction method to be applied to the UE in the future. Accordingly, the base station may determine which method, AI / ML-based CSI prediction or non-AI / ML-based CSI prediction, is more suitable for the UE.

[0375] Alternatively, the UE may additionally report on applicable functionality related to CSI prediction. For example, the UE may report information regarding currently available AI / ML supported functionality or unavailable functionality to the network via an applicability report. This reporting may be performed as an initial report, as well as as a subsequent report depending on changes in the UE's situation. Furthermore, the applicability report may indicate the applicability of each of one or more CSI reporting settings, or the applicability of one or more sets of inference-related parameters or reporting settings associated therewith. For instance, among multiple CSI-reportConfigs, applicable or inapplicable CSI-reportConfigs may be reported at a specific point in time, such as at the time of the applicability report.

[0376] In this case, the UE may determine the CSI reporting setting associated with the monitoring setting information as the first CSI reporting setting or the second CSI reporting setting based on the reporting time of the applicable function. For example, even in the absence of separate explicit indicators or parameters, the UE may implicitly determine whether the CSI reporting setting is a setting for AI / ML-based CSI prediction or a setting for non-AI / ML-based CSI prediction by utilizing the relationship between the time of the applicability report and the time of activation or triggering of a specific CSI reporting setting. For example, a CSI report activated or triggered after the applicability report may be interpreted as the first CSI reporting setting corresponding to AI / ML-based CSI prediction, and if the Rel-18 codebook is set as the remaining CSI report other than the above CSI report, it may be interpreted as the second CSI reporting setting corresponding to non-AI / ML-based CSI prediction. Alternatively, among the candidate CSI reports, the CSI report setting reported as applicable at the time of the Applicability report may be determined as the first CSI report setting, and the other CSI report settings may be determined as the second CSI report setting. For example, based on the fact that the CSI report setting associated with the monitoring setting information is activated or triggered after the time of the report of the applicable function, the UE may determine the CSI report setting associated with the monitoring setting information as the first CSI report setting. For example, if the CSI report setting associated with the monitoring setting information is activated after the applicability report, the UE may determine that CSI report setting as the first CSI report setting for Rel-19 AI / ML-based CSI prediction.In contrast, a CSI reporting setting that operates based on existing codebook settings regardless of the applicability report or corresponds to Rel-18 CSI prediction may be determined as a second CSI reporting setting. Accordingly, even when no explicit indication of AI / ML status is provided, the UE can appropriately distinguish the type of associated CSI reporting setting by utilizing the relationship between the applicability report timing and the activation or trigger of the CSI reporting setting.

[0377] As described above, the proposed method can instruct the UE to perform performance monitoring for non-AI / ML based CSI prediction as well as AI / ML based CSI prediction. For example, the base station may set up non-AI / ML based CSI prediction for a specific CSI reporting setting and provide monitoring setting information associated with the CSI reporting setting to the UE. Accordingly, the UE can measure the performance of the results of non-AI / ML based CSI prediction and report performance information regarding non-AI / ML based CSI prediction to the base station. This may be intended to enable the Rel-19 UE to report the performance of both AI / ML based CSI prediction and non-AI / ML based CSI prediction, thereby allowing the base station to compare the performance of the two prediction methods and select an appropriate prediction method.

[0378] Furthermore, the base station may determine the CSI prediction method to apply to the UE by utilizing prediction accuracy information and / or information regarding the preferred prediction method reported by the UE. For example, based on the monitoring results, the base station may determine whether AI / ML-based CSI prediction or non-AI / ML-based CSI prediction is more appropriate, and depending on the result, may maintain, change, or reset a specific prediction method for the UE. Accordingly, the UE may report CSI prediction results using an appropriate CSI prediction method according to the base station's settings or control, and the base station may use the reported results to perform more efficient downlink transmission or scheduling.

[0379] FIG. 23 is a diagram illustrating how a base station receives monitoring information related to CSI prediction from a UE.

[0380] Referring to FIG. 23, a base station may transmit monitoring setting information related to CSI prediction to a UE (S231). The monitoring setting information may be information for enabling the UE to perform monitoring for performance evaluation of a specific CSI prediction, and may include information for identifying a CSI reporting setting associated with a specific CSI prediction. For example, the base station may designate at least one of a plurality of CSI reporting settings as a monitoring target, and the UE may determine which CSI prediction to perform performance evaluation monitoring for based on the monitoring setting information. At this time, the CSI reporting setting associated with the monitoring setting information may be either a first CSI reporting setting for AI / ML-based CSI prediction or a second CSI reporting setting for non-AI / ML-based CSI prediction.

[0381] The base station may include a CSI report setting ID in the monitoring setting information to identify the CSI report setting associated with the monitoring setting information and transmit it to the UE. Accordingly, the UE can identify the CSI report setting subject to monitoring using the CSI report setting ID, even when multiple CSI report settings are configured. In other words, the base station can provide the UE with the relationship between the monitoring setting and the CSI prediction report subject to monitoring through the CSI report setting ID. Additionally, the monitoring setting information may include information regarding the monitoring resource on which monitoring is performed. The monitoring resource may be a resource for the UE to measure the actual channel state, and the UE can evaluate the performance of the CSI prediction performed based on the CSI report setting using the actual CSI measured from the monitoring resource. For example, the base station may configure the monitoring resource to obtain the actual CSI corresponding to the CSI predicted by the UE, and the UE can calculate prediction accuracy information by comparing the actual measured value and the predicted value for the monitoring resource.

[0382] Alternatively, the CSI reporting setting may include instruction information regarding whether it is the first CSI reporting setting or the second CSI reporting setting. For example, a base station may transmit to a UE a CSI reporting setting that includes instruction information indicating whether the CSI reporting setting is the first CSI reporting setting for AI / ML-based CSI prediction or the second CSI reporting setting for non-AI / ML-based CSI prediction. Accordingly, the UE can explicitly determine the type of CSI reporting setting associated with the monitoring setting information based on the instruction information.

[0383] Alternatively, the base station may receive a report from the UE regarding applicable functionality related to CSI prediction. Based on the reporting time of the applicable functionality, the UE may determine the CSI reporting setting associated with the monitoring setting information as the first CSI reporting setting or the second CSI reporting setting. For example, even if no separate explicit indicator or parameter is provided, the UE may implicitly determine the type of the CSI reporting setting by utilizing the relationship between the reporting time of the applicable functionality and the activation or trigger time of the CSI reporting setting. For example, based on the fact that the CSI reporting setting associated with the monitoring setting information is activated or triggered after the reporting time of the applicable functionality, the UE may determine the CSI reporting setting associated with the monitoring setting information as the first CSI reporting setting. For example, a CSI reporting setting that is activated or triggered after the report on the applicable functionality may be interpreted as the first CSI reporting setting for AI / ML-based CSI prediction. On the other hand, a CSI reporting setting that operates independently of the reporting of the above-mentioned applicable functions or is associated with existing non-AI / ML-based CSI prediction may be interpreted as a second CSI reporting setting. Accordingly, the base station can enable the UE to appropriately distinguish the type of CSI reporting setting associated with the monitoring setting information without separate explicit instructions.

[0384] The base station may receive from the UE prediction accuracy information regarding the results of monitoring for evaluating the performance of the CSI prediction performed based on the CSI reporting setting associated with the monitoring setting information (S233). Here, the CSI reporting setting associated with the monitoring setting information may be either a first CSI reporting setting or a second CSI reporting setting. For example, the base station may configure a first CSI reporting setting that applies AI / ML-based CSI prediction to a specific UE, or configure a second CSI reporting setting that applies non-AI / ML-based CSI prediction. Accordingly, the base station may receive monitoring results corresponding to the CSI prediction method performed by the UE.

[0385] For example, if the CSI reporting setting associated with the above monitoring setting information is the second CSI reporting setting, the base station may receive prediction accuracy information calculated based on the predicted value according to the non-AI / ML-based CSI prediction and the actual measured value of the monitoring resource from the UE. Here, the prediction accuracy information may include a squared generalized cosine similarity (SGCS) value or a normalized mean squared error (NMSE) value. Accordingly, the base station may receive performance information of the non-AI / ML-based CSI prediction from the UE, which was previously difficult to determine separately, and determine the effectiveness of the prediction method.

[0386] The above prediction accuracy information may further include information regarding the number of times an SGCS value is calculated that is below (or less than) a first performance threshold, or the number of times an NMSE value is calculated that exceeds (or is greater than) a second performance threshold. The first performance threshold and the second performance threshold may be included in the monitoring setting information transmitted by the base station to the UE, or they may be preset values. The base station can determine whether repetitive performance degradation of a specific CSI prediction method has occurred by receiving the count information from the UE. For example, if the number of threshold violations in a specific CSI prediction method is repeatedly reported as high, the base station may review whether to maintain the prediction method.

[0387] Alternatively, in addition to the prediction accuracy information received from the UE, the base station may receive information regarding the preferred prediction method between AI / ML-based CSI prediction and non-AI / ML-based CSI prediction. For example, if the UE determines, based on monitoring results, that a particular prediction method has superior performance or is more stable, it may report that method as the preferred prediction method. The base station may maintain, change, or reset the CSI prediction method to be applied to the UE in the future by considering the information regarding the preferred prediction method reported by the UE along with the prediction accuracy information.

[0388] In this way, the proposed invention supports monitoring of non-AI / ML-based CSI prediction, thereby providing a base station with the opportunity to quantitatively determine the performance of a non-AI / ML-based CSI prediction method. Furthermore, the proposed invention enables the calculation and reporting of prediction accuracy information for non-AI / ML-based CSI prediction, thereby enabling an evaluation of utility between AI / ML-based CSI prediction and non-AI / ML-based CSI prediction, and has the effect of appropriately directing or setting a CSI prediction method suitable for the UE based on the said utility evaluation.

[0389] Example of a communication system to which the invention is applied

[0390] Although not limited thereto, the various descriptions, functions, procedures, proposals, methods, and / or flowcharts of the invention disclosed in this document may be applied to various fields requiring wireless communication / connection (e.g., 5G) between devices.

[0391] Examples are provided in more detail below with reference to the drawings. In the following drawings and descriptions, the same reference numerals may represent the same or corresponding hardware blocks, software blocks, or function blocks unless otherwise described.

[0392] FIG. 24 illustrates a communication system to which the present invention is applied.

[0393] Referring to FIG. 24, the communication system (1) to which the present invention applies includes a wireless device, a base station, and a network. Here, the wireless device refers to a device that performs communication using wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution)) and may be referred to as a communication / wireless / 5G device. Although not limited thereto, the wireless device may include a robot (100a), a vehicle (100b-1, 100b-2), an XR (eXtended Reality) device (100c), a hand-held device (100d), a home appliance (100e), an IoT (Internet of Thing) device (100f), and an AI device / server (400). For example, the vehicle may include a vehicle equipped with wireless communication functions, an autonomous vehicle, a vehicle capable of performing inter-vehicle communication, etc. Here, the vehicle may include an Unmanned Aerial Vehicle (UAV) (e.g., a drone). XR devices include AR (Augmented Reality) / VR (Virtual Reality) / MR (Mixed Reality) devices and can be implemented in the form of HMDs (Head-Mounted Devices), HUDs (Head-Up Displays) equipped in vehicles, televisions, smartphones, computers, wearable devices, home appliances, digital signage, vehicles, robots, etc. Portable devices may include smartphones, smartpads, wearable devices (e.g., smartwatches, smart glasses), computers (e.g., laptops, etc.). Home appliances may include TVs, refrigerators, washing machines, etc. IoT devices may include sensors, smart meters, etc. For example, base stations and networks may be implemented as wireless devices, and a specific wireless device (200a) may operate as a base station / network node to other wireless devices.

[0394] Wireless devices (100a to 100f) can be connected to a network (300) through a base station (200). Artificial Intelligence (AI) technology may be applied to the wireless devices (100a to 100f), and the wireless devices (100a to 100f) can be connected to an AI server (400) through the network (300). The network (300) can be configured using a 3G network, a 4G (e.g., LTE) network, or a 5G (e.g., NR) network. The wireless devices (100a to 100f) may communicate with each other through the base station (200) / network (300), but they may also communicate directly (e.g., sidelink communication) without going through the base station / network. For example, vehicles (100b-1, 100b-2) can communicate directly (e.g., V2V (Vehicle to Vehicle) / V2X (Vehicle to everything) communication). Also, IoT devices (e.g., sensors) can communicate directly with other IoT devices (e.g., sensors) or other wireless devices (100a to 100f).

[0395] Wireless communication / connection (150a, 150b, 150c) can be established between wireless devices (100a~100f) / base station (200) and base station (200) / base station (200). Here, wireless communication / connection can be achieved through various wireless access technologies (e.g., 5G NR), such as uplink / downlink communication (150a), sidelink communication (150b) (or D2D communication), and inter-base station communication (150c) (e.g., relay, IAB (Integrated Access Backhaul)). Through wireless communication / connection (150a, 150b, 150c), wireless devices and base stations / wireless devices, and base stations and base stations can transmit / receive wireless signals to / from each other. For example, wireless communication / connection (150a, 150b, 150c) can transmit / receive signals through various physical channels. To this end, based on various proposals of the present invention, at least some of the following may be performed: various configuration information setting processes for transmitting / receiving wireless signals, various signal processing processes (e.g., channel encoding / decoding, modulation / demodulation, resource mapping / demapping, etc.), resource allocation processes, etc.

[0396] Example of a wireless device to which the present invention is applied

[0397] FIG. 25 illustrates a wireless device that can be applied to the present invention.

[0398] Referring to FIG. 25, the first wireless device (100) and the second wireless device (200) can transmit and receive wireless signals through various wireless access technologies (e.g., LTE, NR). Here, {the first wireless device (100), the second wireless device (200)} may correspond to {wireless device (100x), base station (200)} and / or {wireless device (100x), wireless device (100x)} of FIG. 24.

[0399] The first wireless device (100) includes one or more processors (102) and one or more memories (104), and may additionally include one or more transceivers (106) and / or one or more antennas (108). The processor (102) controls the memory (104) and / or transceivers (106) and may be configured to implement the descriptions, functions, procedures, proposals, methods and / or flowcharts of operation disclosed in this document. For example, the processor (102) may process information within the memory (104) to generate a first information / signal and then transmit a wireless signal containing the first information / signal through the transceiver (106). Additionally, the processor (102) may receive a wireless signal containing a second information / signal through the transceiver (106) and then store information obtained from the signal processing of the second information / signal in the memory (104). The memory (104) may be connected to the processor (102) and may store various information related to the operation of the processor (102). For example, the memory (104) may store software code containing instructions for performing some or all of the processes controlled by the processor (102) or for performing the descriptions, functions, procedures, proposals, methods, and / or operation sequence diagrams disclosed in this document. Here, the processor (102) and the memory (104) may be part of a communication modem / circuit / chipset designed to implement wireless communication technology (e.g., LTE, NR). The transceiver (106) may be connected to the processor (102) and may transmit and / or receive wireless signals through one or more antennas (108). The transceiver (106) may include a transmitter and / or receiver. The transceiver (106) may be combined with an RF (Radio Frequency) unit. In the present invention, the wireless device may refer to a communication modem / circuit / chipset.

[0400] Specifically, the first wireless device or UE (100) may include a processor (102) connected to a transceiver (106) and a memory (104). The memory (104) may include at least one program capable of performing operations related to the embodiments described with reference to FIGS. 19 through 23 in the section “UE behavior depending on AIML based CSI prediction capability”. The operations include controlling the transceiver (106) to receive monitoring setting information related to CSI prediction (channel state information prediction); performing monitoring for evaluating the performance of said CSI prediction based on a CSI reporting setting associated with said monitoring setting information; and transmitting prediction accuracy information to a base station based on the results of said monitoring. The said CSI reporting setting associated with said monitoring setting information may be either a first CSI reporting setting for AI / ML (artificial intelligence / machine learning) based CSI prediction or a second CSI reporting setting for non-AI / ML based CSI prediction.

[0401] Alternatively, a processing device may be configured including a processor (102) and a memory (104) that control the UE. In this case, the processing device may include at least one processor; and at least one memory connected to the at least one processor and storing instructions that perform operations when executed by the at least one processor. The operations include receiving monitoring setting information related to CSI prediction (channel state information prediction); performing monitoring for evaluating the performance of the CSI prediction based on a CSI reporting setting associated with the monitoring setting information; and transmitting prediction accuracy information to a base station based on the results of the monitoring. The CSI reporting setting associated with the monitoring setting information may be either a first CSI reporting setting for AI / ML (artificial intelligence / machine learning) based CSI prediction or a second CSI reporting setting for non-AI / ML based CSI prediction.

[0402] Alternatively, at least one non-transient computer-readable medium may be configured, in which a program / instruction for performing the above-described operations is stored.

[0403] The second wireless device (200) includes one or more processors (202) and one or more memories (204), and may additionally include one or more transceivers (206) and / or one or more antennas (208). The processor (202) controls the memory (204) and / or transceivers (206) and may be configured to implement the descriptions, functions, procedures, proposals, methods and / or operation sequences disclosed in this document. For example, the processor (202) may process information within the memory (204) to generate a third information / signal and then transmit a wireless signal containing the third information / signal through the transceiver (206). Additionally, the processor (202) may receive a wireless signal containing a fourth information / signal through the transceiver (206) and then store information obtained from the signal processing of the fourth information / signal in the memory (204). 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 containing instructions for performing some or all of the processes controlled by the processor (202) or for performing the descriptions, functions, procedures, proposals, methods, and / or operation sequence diagrams disclosed in this document. 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 transmit and / or receive wireless signals through one or more antennas (208). The transceiver (206) may include a transmitter and / or receiver. The transceiver (206) may be interchangeable with an RF unit. In the present invention, the wireless device may refer to a communication modem / circuit / chip.

[0404] Specifically, the second wireless device or base station (200) may include a processor (202) and a memory (204) connected to a transceiver or RF transceiver (206). The memory (204) may include at least one program capable of performing operations related to the embodiments described with reference to FIGS. 19 through 23 in the section “UE behavior depending on AIML based CSI prediction capability”. The operations include controlling the RF transceiver (206) to transmit monitoring setting information related to CSI prediction (channel state information prediction) to the UE (User Equipment), and receiving prediction accuracy information from the UE regarding the monitoring result of the performance evaluation of the CSI prediction performed based on the CSI reporting setting associated with the monitoring setting information, wherein the CSI reporting setting associated with the monitoring setting information may be either a first CSI reporting setting for AI / ML (artificial intelligence / machine learning) based CSI prediction or a second CSI reporting setting for non-AI / ML based CSI prediction.

[0405] Hereinafter, hardware elements of the wireless device (100, 200) will be described in more detail. Although not limited thereto, one or more protocol layers may be implemented by one or more processors (102, 202). For example, one or more processors (102, 202) may implement one or more layers (e.g., functional layers such as PHY, MAC, RLC, PDCP, RRC, SDAP). One or more processors (102, 202) may generate one or more Protocol Data Units (PDUs) and / or Service Data Units (SDUs) according to the descriptions, functions, procedures, proposals, methods, and / or flowcharts of operation disclosed in this document. One or more processors (102, 202) may generate messages, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or flowcharts of operation disclosed in this document. One or more processors (102, 202) may generate a signal (e.g., baseband signal) containing a PDU, SDU, message, control information, data, or information according to the functions, procedures, proposals, and / or methods disclosed in this document and provide it to one or more transceivers (106, 206). One or more processors (102, 202) may receive a signal (e.g., baseband signal) from one or more transceivers (106, 206) and may obtain a PDU, SDU, message, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or flowcharts disclosed in this document.

[0406] One or more processors (102, 202) may be referred to as a controller, microcontroller, microprocessor, or microcomputer. One or more processors (102, 202) may be implemented by hardware, firmware, software, or a combination thereof. For example, one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), one or more Digital Signal Processing Devices (DSPDs), one or more Programmable Logic Devices (PLDs), or one or more Field Programmable Gate Arrays (FPGAs) may be included in one or more processors (102, 202). The descriptions, functions, procedures, proposals, methods, and / or flowcharts disclosed in this document may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, etc. Firmware or software configured to perform the descriptions, functions, procedures, proposals, methods, and / or operation sequences disclosed in this document may be contained in one or more processors (102, 202) or stored in one or more memories (104, 204) and driven by one or more processors (102, 202). The descriptions, functions, procedures, proposals, methods, and / or operation sequences disclosed in this document may be implemented using firmware or software in the form of code, instructions, and / or sets of instructions.

[0407] One or more memories (104, 204) may be connected to one or more processors (102, 202) and may store various forms of data, signals, messages, information, programs, codes, instructions, and / or commands. One or more memories (104, 204) may be composed of ROM, RAM, EPROM, flash memory, hard drive, registers, cache memory, computer read storage media, and / or combinations thereof. One or more memories (104, 204) may be located inside and / or outside of one or more processors (102, 202). Additionally, one or more memories (104, 204) may be connected to one or more processors (102, 202) through various technologies such as wired or wireless connections.

[0408] One or more transceivers (106, 206) may transmit user data, control information, wireless signals / channels, etc., as mentioned in the methods and / or operation flowcharts, etc., of this document to one or more other devices. One or more transceivers (106, 206) may receive user data, control information, wireless signals / channels, etc., as mentioned in the descriptions, functions, procedures, proposals, methods and / or operation flowcharts, etc., disclosed in this document from one or more other devices. For example, one or more transceivers (106, 206) may be connected to one or more processors (102, 202) and may transmit and receive wireless signals. For example, one or more processors (102, 202) may control one or more transceivers (106, 206) to transmit user data, control information, or wireless signals to one or more other devices. Additionally, one or more processors (102, 202) may control one or more transceivers (106, 206) to receive user data, control information, or wireless signals from one or more other devices. Additionally, one or more transceivers (106, 206) may be connected to one or more antennas (108, 208), and one or more transceivers (106, 206) may be configured to transmit and receive user data, control information, wireless signals / channels, etc., as described in the descriptions, functions, procedures, proposals, methods, and / or flowcharts of operation disclosed in this document through one or more antennas (108, 208). In this document, one or more antennas may be multiple physical antennas or multiple logical antennas (e.g., antenna ports). One or more transceivers (106, 206) can convert the received wireless signal / channel, etc. from an RF band signal to a baseband signal in order to process the received user data, control information, wireless signal / channel, etc. using one or more processors (102, 202).One or more transceivers (106, 206) can convert user data, control information, wireless signals / channels, etc. processed using one or more processors (102, 202) from baseband signals to RF band signals. To this end, one or more transceivers (106, 206) may include (analog) oscillators and / or filters.

[0409] Examples of wireless device applications to which the present invention is applied

[0410] FIG. 26 illustrates another example of a wireless device to which the present invention applies. The wireless device may be implemented in various forms depending on the use-example / service (see FIG. 24).

[0411] Referring to FIG. 26, the wireless device (100, 200) corresponds to the wireless device (100, 200) of FIG. 25 and may be composed of various elements, components, units / parts, and / or modules. For example, the wireless device (100, 200) may include a communication unit (110), a control unit (120), a memory unit (130), and additional elements (140). The communication unit may include a communication circuit (112) and transceiver(s) (114). For example, the communication circuit (112) may include one or more processors (102, 202) and / or one or more memories (104, 204) of FIG. 26. For example, the transceiver(s) (114) may include one or more transceivers (106, 206) and / or one or more antennas (108, 208) of FIG. 25. The control unit (120) is electrically connected to the communication unit (110), the memory unit (130), and additional elements (140) and controls the general operation of the wireless device. For example, the control unit (120) may control the electrical / mechanical operation of the wireless device based on a program / code / command / information stored in the memory unit (130). Additionally, the control unit (120) may transmit information stored in the memory unit (130) to an external (e.g., another communication device) via a wireless / wired interface through the communication unit (110), or store information received from an external (e.g., another communication device) via a wireless / wired interface through the communication unit (110) in the memory unit (130).

[0412] The additional element (140) can be configured in various ways depending on the type of wireless device. For example, the additional element (140) may include at least one of a power unit / battery, an input / output unit (I / O unit), a driving unit, and a computing unit. Although not limited thereto, the wireless device may be implemented in the form of a robot (Fig. 24, 100a), a vehicle (Fig. 24, 100b-1, 100b-2), an XR device (Fig. 24, 100c), a portable device (Fig. 24, 100d), a home appliance (Fig. 24, 100e), an IoT device (Fig. 24, 100f), a digital broadcasting terminal, a hologram device, a public safety device, an MTC device, a medical device, a fintech device (or financial device), a security device, a climate / environment device, an AI server / device (Fig. 24, 400), a base station (Fig. 24, 200), a network node, etc. Wireless devices can be used in a movable or fixed location depending on the use—e.g., service.

[0413] In FIG. 26, various elements, components, units / parts, and / or modules within the wireless device (100, 200) may be entirely interconnected via a wired interface, or at least a portion may be wirelessly connected via a communication unit (110). For example, within the wireless device (100, 200), the control unit (120) and the communication unit (110) may be wired, and the control unit (120) and the first unit (e.g., 130, 140) may be wirelessly connected via the communication unit (110). Additionally, each element, component, unit / part, and / or module within the wireless device (100, 200) may include one or more additional elements. For example, the control unit (120) may be composed of one or more sets of processors. For example, the control unit (120) may be composed of a set of a communication control processor, an application processor, an Electronic Control Unit (ECU), a graphics processing processor, a memory control processor, etc. As another example, the memory unit (130) may be composed of RAM (Random Access Memory), DRAM (Dynamic RAM), ROM (Read Only Memory), flash memory, volatile memory, non-volatile memory and / or a combination thereof.

[0414] Examples of vehicles or autonomous vehicles to which the present invention is applied

[0415] FIG. 27 illustrates a vehicle or autonomous vehicle to which the present invention applies. The vehicle or autonomous vehicle may be implemented as a mobile robot, a vehicle, a train, an aerial vehicle (AV), a ship, etc.

[0416] Referring to FIG. 27, a vehicle or autonomous vehicle (100) may include an antenna unit (108), a communication unit (110), a control unit (120), a driving unit (140a), a power supply unit (140b), a sensor unit (140c), and an autonomous driving unit (140d). The antenna unit (108) may be configured as part of the communication unit (110). Blocks 110 / 130 / 140a to 140d correspond to blocks 110 / 130 / 140 of FIG. 26, respectively.

[0417] The communication unit (110) can transmit and receive signals (e.g., data, control signals, etc.) with external devices such as other vehicles, base stations (e.g., base stations, roadside base stations (Roadside units), etc.), and servers. The control unit (120) can perform various operations by controlling elements of the vehicle or autonomous vehicle (100). The control unit (120) may include an Electronic Control Unit (ECU). The driving unit (140a) can drive the vehicle or autonomous vehicle (100) on the ground. The driving unit (140a) may include an engine, motor, power train, wheels, brakes, steering device, etc. The power supply unit (140b) supplies power to the vehicle or autonomous vehicle (100) and may include wired / wireless charging circuits, batteries, etc. The sensor unit (140c) can obtain vehicle status, surrounding environment information, user information, etc. The sensor unit (140c) may include an IMU (inertial measurement unit) sensor, a collision sensor, a wheel sensor, a speed sensor, an inclination sensor, a weight detection sensor, a heading sensor, a position module, a vehicle forward / reverse sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor, a temperature sensor, a humidity sensor, an ultrasonic sensor, an illuminance sensor, a pedal position sensor, etc. The autonomous driving unit (140d) may implement technologies such as maintaining the driving lane, technologies for automatically adjusting speed such as adaptive cruise control, technologies for automatically driving along a predetermined path, and technologies for automatically setting a path and driving when a destination is set.

[0418] For example, the communication unit (110) can receive map data, traffic information data, etc. from an external server. The autonomous driving unit (140d) can generate an autonomous driving path and a driving plan based on the acquired data. The control unit (120) can control the drive unit (140a) so that the vehicle or the autonomous vehicle (100) moves along the autonomous driving path according to the driving plan (e.g., speed / direction control). During autonomous driving, the communication unit (110) can acquire the latest traffic information data from an external server non-periodically and can acquire surrounding traffic information data from surrounding vehicles. Additionally, during autonomous driving, the sensor unit (140c) can acquire vehicle status and surrounding environment information. The autonomous driving unit (140d) can update the autonomous driving path and the driving plan based on the newly acquired data / information. The communication unit (110) can transmit information regarding the vehicle location, autonomous driving path, driving plan, etc. to an external server. An external server can predict traffic information data in advance using AI technology, etc., based on information collected from vehicles or autonomous vehicles, and can provide the predicted traffic information data to vehicles or autonomous vehicles.

[0419] Here, the wireless communication technology implemented in the wireless device (XXX, YYY) of this specification may include LTE, NR, and 6G, as well as Narrowband Internet of Things for low-power communication. For example, NB-IoT technology may be an example of LPWAN (Low Power Wide Area Network) technology and may be implemented according to standards such as LTE Cat NB1 and / or LTE Cat NB2, but is not limited to the names mentioned above. Additionally, or generally, the wireless communication technology implemented in the wireless device (XXX, YYY) of this specification may perform communication based on LTE-M technology. For example, LTE-M technology may be an example of LPWAN technology and may be referred to by various names such as eMTC (enhanced Machine Type Communication). For example, LTE-M technology may be implemented in at least one of various standards such as 1) LTE CAT 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-BL (non-Bandwidth Limited), 5) LTE-MTC, 6) LTE Machine Type Communication, and / or 7) LTE M, and is not limited to the names mentioned above. Additionally or generally, wireless communication technology implemented in the wireless device (XXX, YYY) of this specification may include at least one of ZigBee, Bluetooth, and Low Power Wide Area Network (LPWAN) with consideration for low-power communication, and is not limited to the names mentioned above. As an example, ZigBee technology can create personal area networks (PANs) related to small / low-power digital communication based on various standards such as IEEE 802.15.4, and may be referred to by various names.

[0420] The embodiments described above are combinations of the components and features of the present invention in a specific form. Each component or feature should be considered optional unless otherwise explicitly stated. Each component or feature may be implemented in a form not combined with other components or features. Additionally, it is possible to construct embodiments of the present invention by combining some components and / or features. The order of operations described in the embodiments of the present invention may be changed. Some components or features of one embodiment may be included in another embodiment, or may be replaced with corresponding components or features of another embodiment. It is obvious that embodiments may be constructed by combining claims that do not have an explicit citation relationship in the claims, or that new claims may be included by amendment after filing.

[0421] In this document, embodiments of the present invention are described primarily with a focus on the signal transmission and reception relationship between a terminal and a base station. This transmission and reception relationship is extended in the same or similar manner to signal transmission and reception between a terminal and a relay or between a base station and a relay. Specific operations described in this document as being performed by a base station may, in some cases, be performed by an upper node. That is, it is self-evident that various operations performed for communication with a terminal in a network consisting of multiple network nodes including a base station may be performed by the base station or other network nodes other than the base station. The base station may be replaced by terms such as fixed station, Node B, eNode B (eNB), and access point. Additionally, the terminal may be replaced by terms such as User Equipment (UE), Mobile Station (MS), and Mobile Subscriber Station (MSS).

[0422] Embodiments according to the present invention may be implemented by various means, for example, hardware, firmware, software, or a combination thereof. In the case of implementation by hardware, one embodiment of the present invention may be implemented by one or more ASICs (application specific integrated circuits), DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs (field programmable gate arrays), processors, controllers, microcontrollers, microprocessors, etc.

[0423] In the case of implementation by firmware or software, an embodiment of the present invention may be implemented in the form of a module, procedure, function, etc., that performs the functions or operations described above. Software code may be stored in a memory unit and executed by a processor. The memory unit may be located inside or outside the processor and may exchange data with the processor by various means already known.

[0424] It is obvious to those skilled in the art that the present invention may be embodied in other specific forms without departing from the features of the invention. Accordingly, the foregoing detailed description should not be interpreted restrictively in all respects but should be considered exemplary. The scope of the invention shall be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the invention are included within the scope of the invention.

[0425] The embodiments of the present invention as described above can be applied to various mobile communication systems.

Claims

1. In a method using UE (User Equipment), A step of receiving monitoring configuration information related to CSI prediction (channel state information prediction); A step of performing monitoring for evaluating the performance of the CSI prediction performed based on the CSI reporting settings associated with the above monitoring setting information; and It includes the step of transmitting prediction accuracy information to a base station based on the results of the above monitoring, A method in which the CSI reporting setting associated with the above monitoring setting information is either a first CSI reporting setting for AI / ML (artificial intelligence / machine learning) based CSI prediction or a second CSI reporting setting for non-AI / ML based CSI prediction.

2. In Paragraph 1, A method in which the above monitoring setting information includes a CSI reporting setting ID (identifier) ​​that identifies the associated CSI reporting setting.

3. In Paragraph 1, A method in which the above monitoring setting information includes information about a monitoring resource on which monitoring is performed.

4. In Paragraph 3, A method based on the fact that the CSI reporting setting associated with the above monitoring setting information is the second CSI reporting setting, wherein the prediction accuracy information includes a squared generalized cosine similarity (SGCS) value or a normalized mean squared error (NMSE) value calculated based on a predicted value according to the non-AI / ML-based CSI prediction and an actual measured value of the monitoring resource.

5. In Paragraph 4, The above prediction accuracy information further includes information on the number of times the SGCS value below the first performance threshold is calculated or the number of times the NMSE value exceeding the second performance threshold is calculated, and The first performance threshold and the second performance threshold are included in the monitoring setting information or are preset, method.

6. In Paragraph 1, A method further comprising the step of reporting to the base station information regarding a preferred prediction method among the AI / ML-based CSI prediction or the non-AI / ML-based CSI prediction based on the prediction accuracy information.

7. In Paragraph 1, A method comprising: the above CSI reporting setting including instruction information regarding whether the above CSI reporting setting is the first CSI reporting setting or the above second CSI reporting setting.

8. In Paragraph 1, It further includes the step of performing a report on applicable functionality related to the above CSI prediction, and A method in which the above UE determines the above CSI reporting setting associated with the above monitoring setting information as the first CSI reporting setting or the second CSI reporting setting based on the reporting time of the above applicable function.

9. In Paragraph 8, A method in which the UE determines the CSI reporting setting associated with the monitoring setting information as the first CSI reporting setting based on the fact that the CSI reporting setting associated with the monitoring setting information is activated or triggered after the reporting time of the applicable function.

10. In at least one non-transient computer-readable recording medium, Includes instructions that perform operations when executed by at least one processor, The above operations are, Receive monitoring configuration information related to CSI prediction (channel state information prediction); Performing monitoring for performance evaluation of the CSI prediction performed based on the CSI reporting settings associated with the above monitoring setting information; and It includes transmitting prediction accuracy information to a base station based on the results of the above monitoring, At least one non-transient computer-readable recording medium in which the CSI reporting setting associated with the above monitoring setting information is either a first CSI reporting setting for AI / ML (artificial intelligence / machine learning) based CSI prediction or a second CSI reporting setting for non-AI / ML based CSI prediction.

11. Regarding UE (User Equipment), RF (Radio Frequency) transceiver; and It includes a processor connected to the above RF transceiver, and The processor controls the RF transceiver to receive monitoring setting information related to CSI prediction (channel state information prediction), performs monitoring for evaluating the performance of the CSI prediction based on the CSI reporting settings associated with the monitoring setting information, and transmits prediction accuracy information to a base station based on the results of the monitoring. The above CSI reporting setting associated with the above monitoring setting information is either a first CSI reporting setting for AI / ML (artificial intelligence / machine learning) based CSI prediction or a second CSI reporting setting for non-AI / ML based CSI prediction, UE.

12. In Paragraph 11, The above monitoring setting information includes a CSI reporting setting ID (identifier) ​​that identifies the associated CSI reporting setting, UE.

13. In a processing device for controlling UE (User Equipment), At least one processor; and It includes at least one memory that stores instructions connected to the above at least one processor and performing operations when executed by the at least one processor, The above operations cause the UE to: Receive monitoring configuration information related to CSI prediction (channel state information prediction); Performing monitoring for performance evaluation of the CSI prediction performed based on the CSI reporting settings associated with the above monitoring setting information; and It includes transmitting prediction accuracy information to a base station based on the results of the above monitoring, A processing device in which the CSI reporting setting associated with the above monitoring setting information is either a first CSI reporting setting for AI / ML (artificial intelligence / machine learning) based CSI prediction or a second CSI reporting setting for non-AI / ML based CSI prediction.

14. In the method using a base station, A step of transmitting monitoring configuration information related to CSI prediction (channel state information prediction) to the UE (User Equipment); and The method includes the step of receiving prediction accuracy information from the UE regarding the monitoring result of the performance evaluation of the CSI prediction performed based on the CSI reporting settings associated with the monitoring setting information. A method in which the CSI reporting setting associated with the above monitoring setting information is either a first CSI reporting setting for AI / ML (artificial intelligence / machine learning) based CSI prediction or a second CSI reporting setting for non-AI / ML based CSI prediction.

15. Regarding base stations, RF (Radio Frequency) transceiver; and It includes a processor connected to the above RF transceiver, and The processor controls the RF transceiver to transmit monitoring setting information related to CSI prediction (channel state information prediction) to the UE (User Equipment), and receives from the UE prediction accuracy information regarding the monitoring results of the performance evaluation of the CSI prediction performed based on the CSI reporting settings associated with the monitoring setting information. A base station in which the CSI reporting setting associated with the above monitoring setting information is either a first CSI reporting setting for AI / ML (artificial intelligence / machine learning) based CSI prediction or a second CSI reporting setting for non-AI / ML based CSI prediction.

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