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

Abstract

According to various embodiments, disclosed are a method for performing communication in a wireless communication system and a device therefor. The device may: measure first channel state information (CSI) related to a first measurement time point; transmit, to a base station, output information about an artificial intelligence (AI)-based inference model having the first CSI as an input; and report, to the base station, monitoring information related to the performance of the AI-based inference model on the basis of second CSI measured at a second measurement time point after the first measurement time point.

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 device to perform communication 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 monitoring of AI models related to channel measurement 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: a step of measuring a first CSI (channel state information) related to a first measurement point; a step of transmitting output information of an AI (Artificial Intelligence)-based inference model having the first CSI as input to a base station; and a step of reporting monitoring information related to the performance of the AI-based inference model to the base station based on a second CSI measured at a second measurement point after the first measurement point, wherein the monitoring information may include first monitoring information and second monitoring information corresponding to the results of monitoring the CSI prediction function and the CSI compression function of the AI-based inference model separately.

[0018] Alternatively, the first monitoring information may be calculated based on the output information of a first model separately configured to be linked with the CSI prediction function of the AI-based inference model and the second CSI, and the second monitoring information may be calculated based on the output information of a second model separately configured to be linked with the CSI compression function of the AI-based inference model and the second CSI.

[0019] Alternatively, the first model may be a model that receives the first CSI as input and outputs a predicted CSI for a future time point corresponding to the second measurement time point, and the second model may be a model that receives a precoding matrix corresponding to the predicted CSI as input and outputs latent information that is a compressed representation of the precoding matrix.

[0020] Alternatively, the first monitoring information may include a 1-bit first indicator determined based on a first value calculated using a first metric, and the second monitoring information may include a 1-bit second indicator determined based on a second value calculated using a second metric different from the first metric.

[0021] Alternatively, the first metric may be a metric that calculates a mean squared error (MSE) or normalized MSE (NMSE) value that quantifies the difference between the second CSI and the predicted CSI, and the second metric may be a metric that calculates a cosine similarity or squared generalized cosine similarity (SGCS) value that quantifies the similarity between a precoding matrix corresponding to the second CSI and a precoding matrix reconstructed based on the latent information.

[0022] Alternatively, the first monitoring information may include a first indicator of 1 bit set to 0 or 1 based on whether the MSE or NMSE value is below a first threshold, and the second monitoring information may include a second indicator of 1 bit set to 0 or 1 based on whether the cosine similarity or SGCS value is above a second threshold.

[0023] Alternatively, it may further include the step of receiving control information of the AI-based inference model determined based on the monitoring information.

[0024] Alternatively, the control information may include a first instruction information instructing to maintain or enable the inference operation of the first CSI using the AI-based inference model, a second instruction information instructing to switch or turn off the model for either the CSI prediction function or the CSI compression function of the AI-based inference model, or a third instruction information instructing to disable or fallback the AI-based inference model.

[0025] Alternatively, the AI-based inference model may be a JPC (Joint prediction and compression) model that combines the CSI prediction function.

[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 comprising: measuring a first CSI (channel state information) associated with a first measurement point in time; transmitting output information of an AI (Artificial Intelligence)-based inference model having the first CSI as input to a base station; and reporting monitoring information related to the performance of the AI-based inference model to the base station based on a second CSI measured at a second measurement point in time after the first measurement point in time, said monitoring information may include first monitoring information and second monitoring information corresponding to the results of monitoring the CSI prediction function and the CSI compression function of the AI-based inference model separately.

[0027] According to another aspect, the UE (User Equipment) includes an RF (Radio Frequency) transceiver; and a processor connected to the RF transceiver, wherein the processor measures a first CSI (channel state information) associated with a first measurement point in time and transmits output information of an AI (Artificial Intelligence)-based inference model having the first CSI as input to a base station, reports monitoring information related to the performance of the AI-based inference model to the base station based on a second CSI measured at a second measurement point in time after the first measurement point in time, and the monitoring information may include first monitoring information and second monitoring information corresponding to the results of monitoring the CSI prediction function and CSI compression function of the AI-based inference model separately.

[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: measure a first CSI (channel state information) associated with a first measurement point; transmit output information of an AI (Artificial Intelligence)-based inference model having the first CSI as input to a base station; and report monitoring information related to the performance of the AI-based inference model to the base station based on a second CSI measured at a second measurement point after the first measurement point, and wherein the monitoring information may include first monitoring information and second monitoring information corresponding to the results of monitoring the CSI prediction function and the CSI compression function of the AI-based inference model separately.

[0029] A method by a base station according to another aspect comprises: receiving output information of an AI (Artificial Intelligence)-based inference model that takes as input a first CSI (channel state information) measured in relation to a first measurement time point from a UE (User Equipment); and receiving monitoring information related to the performance of the AI-based inference model based on a second CSI measured at a second measurement time point after the first measurement time point, wherein the monitoring information may include first monitoring information and second monitoring information corresponding to the results of monitoring the CSI prediction function and the CSI compression function of the AI-based inference model separately.

[0030] A base station according to another aspect includes an RF (Radio Frequency) transceiver; and a processor connected to the RF transceiver, wherein the processor controls the RF transceiver to receive output information of an AI (Artificial Intelligence)-based inference model that takes as input a first CSI (channel state information) measured in relation to a first measurement time point from a UE (User Equipment), and receives monitoring information related to the performance of the AI-based inference model based on a second CSI measured at a second measurement time point after the first measurement time point, and the monitoring information may include first monitoring information and second monitoring information corresponding to the results of monitoring the CSI prediction function and the CSI compression function of the AI-based inference model separately.

[0031] According to one embodiment of the present invention, monitoring of an AI model related to channel measurement in a wireless communication system can be performed more accurately and efficiently. For example, by distinguishing between the CSI prediction function and the CSI compression function of an AI-based inference model and generating and reporting monitoring information corresponding to each, the causes of model performance degradation can be subdivided by function to identify them, and controls such as maintenance, activation, switching, off, deactivation, or fallback can be performed more precisely accordingly.

[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 how a UE performs monitoring of a CSI inference model.

[0052] FIG. 20 is a diagram illustrating how a base station receives monitoring information about a CSI inference model from a UE.

[0053] FIG. 21 illustrates a communication system to which the present invention is applied.

[0054] FIG. 22 illustrates a wireless device that can be applied to the present invention.

[0055] FIG. 23 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.

[0056] FIG. 24 illustrates a vehicle or autonomous vehicle to which the present invention is applied.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] Figure 3 shows the structure of the NR system.

[0070] 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.

[0071] Figure 4 shows the structure of a wireless frame of NR.

[0072] 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).

[0073] 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).

[0074] 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.

[0075] 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

[0076] 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.

[0077] SCS (15*2 u )N slot symb N frame,u slot N subframe,u slot 60KHz (u=2)12404

[0078] 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.

[0079] 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.

[0080] 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).

[0081] Frequency Range designationCorresponding frequency rangeSubcarrier Spacing (SCS)FR1450MHz - 6000MHz15, 30, 60kHzFR224250MHz - 52600MHz60, 120, 240kHz

[0082] 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).

[0083] Frequency Range designationCorresponding frequency rangeSubcarrier Spacing (SCS)FR1410MHz - 7125MHz15, 30, 60kHzFR224250MHz - 52600MHz60, 120, 240kHz

[0084] Figure 5 shows the slot structure of an NR frame.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] New network characteristics in 6G may be as follows.

[0090] - Satellite Integrated Network

[0091] - 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).

[0092] - Seamless integration of wireless information and energy transfer

[0093] - 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.

[0094] Some general requirements regarding the new network characteristics of 6G mentioned above may be as follows.

[0095] - Small cell networks

[0096] - Ultra-dense heterogeneous network

[0097] - High-capacity backhaul

[0098] - 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.

[0099] - Softwarization and virtualization

[0100] The core implementation technologies of the 6G system are described below.

[0101] - 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.

[0102] - 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.

[0103] 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.

[0104] - Large-scale MIMO technology

[0105] - Hologram beamforming (HBF)

[0106] - Optical wireless technology

[0107] - Free Space Optical Transmission Backhaul Network (FSO backhaul network)

[0108] - Quantum communication

[0109] - Cell-free communication

[0110] - Integration of wireless information and power transmission

[0111] - Integration of wireless communication and sensing

[0112] - Integrated access and backhaul network

[0113] - Big data analysis

[0114] - Reconfigurable intelligent metasurface

[0115] - Metaverse

[0116] - blockchain

[0117] - 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.

[0118] - 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 simply 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.

[0119] Figure 8 illustrates a radio protocol architecture for SL communication. Specifically, Figure 8 (a) shows the user plane protocol stack of NR, and Figure 8 (b) shows the control plane protocol stack of NR.

[0120] The Sidelink Synchronization Signal (SLSS) and synchronization information are described below.

[0121] 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 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Meanwhile, when the SCS is 60kHz, 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.

[0128] Figure 9 shows a terminal performing V2X or SL communication.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Figure 10 shows a resource unit for V2X or SL communication.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] (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 with the SL data. The SA may also be called the SL control channel.

[0138] (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.

[0139] (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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] CSI Measurement / Reporting

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] CSI-related operations can be summarized as follows.

[0165] 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.

[0166] - 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.

[0167] - 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.

[0168] - 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.

[0169] 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.

[0170] 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'.

[0171] 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.

[0172] 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.

[0173] The base station transmits precoded NZP CSI-RS to the terminal on each port of the configured NZP CSI-RS-based IMR.

[0174] The terminal assumes a channel / interference layer for each port in the resource set and measures interference.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Next, one or more CSI resource settings for channel measurement (CM) and interference measurement (IM) are established through higher layer signaling.

[0182] - CSI-IM resource for interference measurement.

[0183] - NZP CSI-RS resources for interference measurement.

[0184] - NZP CSI-RS resources for channel measurement.

[0185] 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.

[0186] Here, CSI-IM (or ZP CSI-RS for IM) is primarily used for inter-cell interference measurements.

[0187] Also, the NZP CSI-RS for IM is mainly used for intra-cell interference measurement from multi-users.

[0188] 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.

[0189] As examined, resource setting can refer to a resource set list.

[0190] 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.

[0191] One reporting setting can be linked to up to three resource settings.

[0192] - 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.

[0193] - 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.

[0194] - 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.

[0195] For semi-persistent or periodic CSI, each CSI-ReportConfig is linked to periodic or semi-persistent resource setting(s).

[0196] - 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.

[0197] - 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] For CSI measurement(s) other than L1-SINR, the terminal assumes the following:

[0202] - Each NZP CSI-RS port configured for interference measurement corresponds to the interference transport layer.

[0203] - All interference transmission layers of the NZP CSI-RS port for interference measurement consider the associated EPRE (energy per resource element) ratio.

[0204] - 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] For CSI reporting, the time and frequency resources available to the UE are controlled by the base station.

[0211] 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.

[0212] 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.

[0213] In addition, the time domain behavior of CSI reporting supports periodic, semi-persistent, and aperiodic.

[0214] 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.

[0215] ii) SP (semi-periodic) CSI reporting is performed on short PUCCH, long PUCCH, or PUSCH.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] Additionally, the terminal reports the number of CSIs that can be calculated simultaneously.

[0225] 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.

[0226] 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.

[0227] Integrated Sensing and Communication (ISAC)

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] - 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)

[0233] - 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)

[0234] - 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)

[0235] - 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)

[0236] - 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)

[0237] - 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)

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.).

[0245] 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.

[0246] 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.

[0247] 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.

[0248] artificial intelligence

[0249] 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.

[0250] The following describes a functional framework for AI / ML operations.

[0251] Below, to provide a more specific explanation of AI (or AI / ML), terms may be defined as follows.

[0252] - Data collection: Data collected from network nodes, management entities, or terminals, serving as a basis for AI model training, data analysis, and inference.

[0253] - 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.

[0254] - 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.

[0255] - AI / ML Inference: A process of making predictions or deriving decisions based on collected data and AI models using trained AI models.

[0256] 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.

[0257] Figure 16 illustrates a general functional architecture for an AI / ML model.

[0258] 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.

[0259] 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).

[0260] 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.

[0261] 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).

[0262] 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).

[0263] 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).

[0264] 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)).

[0265] 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).

[0266] A Model Transfer / Delivery Request (33) can be used to request model(s) from Model Storage (50).

[0267] 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).

[0268] 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).

[0269] 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.

[0270] 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.

[0271] Model Transfer / Delivery (51) is used to transfer an AI / ML model to an inference function.

[0272] 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.

[0273] Cat 0a) No collaboration framework: AI / ML algorithms are based on pure implementation and do not require changes to the wireless interface.

[0274] 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.

[0275] 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.

[0276] 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.

[0277] 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.

[0278] 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.

[0279] 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.

[0280] - 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.

[0281] - 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).

[0282] - 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 executes the AI / ML model first and shares the training data with the second node, the second node can execute 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.

[0283] Monitoring for joint prediction and compression

[0284] 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.

[0285] 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.

[0286] In the following sections, we describe in detail a method for dynamically performing CSI reporting on various CSI-RS resources using a single AI / ML model when wireless communication services are supported based on AI / ML, in order to support advanced network / base station-based dynamic systems. In particular, the proposed method can be effectively applied even in situations where there are constraints on the terminal's memory and computation costs.

[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] In the following, we consider a network-side performance monitoring method for AI / ML (Artificial Intelligence / Machine Learning)-based CSI compression and propose a monitoring method in which the UE does not need to report ground-truth (target) CSI to the NW.

[0290] In Rel-18, three major use cases regarding NR air interfaces via AI / ML were discussed as study items: CSI feedback enhancement, beam management, and positioning accuracy enhancement. Regarding CSI feedback enhancement, various methods were discussed to reduce overhead and improve accuracy in CSI reporting using AI / ML models. For the upcoming Rel-19, the study item description (SID) shown in Table 5 was agreed upon in RAN#102 as an additional study item.

[0291] CSI feedback enhancement[RAN1]:For CSI compression (two-sided model), further study ways to:-> Improve trade-off between performance and complexity / overhead- e.g., considering extending the spatial / frequency compression to spatial / temporal / frequency compression, cell / site specific models, CSI compression plus prediction (compared to Rel-18 non-AI / ML based approach), etc.-- Alleviate / resolve issues related to inter-vendor training collaboration.- while addressing other aspects requiring further study / conclusion as captured in the conclusions section of the TR 38.843.-> For CSI prediction (one-sided model), further study performance gain over Rel-18 non-AI / ML based approach and associated complexity, while addressing other aspects requiring further study / conclusion as captured in the conclusions section of the TR 38.843 (e.g., cell / site specific model could be considered to improve performance gain).

[0292] 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. Here, the output information is information in which the channel information is compressed and expressed through the AI-encoder, and may be latent information expressed in a latent space. The terminal can quantize the output information and feed it back to the base station as an AI-CSI. In this case, the base station can apply the de-quantized information of the fed-back AI-CSI as input to an AI-decoder to generate / obtain an output CSI of the AI-decoder. For example, the base station can obtain an output CSI (e.g., a reconstructed precoding matrix) by reconstructing the compressed output information using 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, feedback overhead can be reduced, which is why this is referred to as CSI compression.

[0293] Meanwhile, for the time domain evaluation of AI / ML-based CSI compression using a two-sided model in Release 19, application / adoption based on classifications / cases such as Table 6 may be considered.

[0294] CaseTarget CSI slot(s)Whether the UE uses past CSI informationWhether the network uses past CSI information0Present slotNoNo1Present slotYesNo2Present slotYesYes3Future slot(s)YesNo4Future slot(s)YesYes5Present slotNoYes

[0295] 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 AL / ML model) and estimates at least one future CSI (or at least one time point) as the output of the AI / ML model.

[0296] 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.

[0297] - csi-InferencePrediction-r19: Activation parameter that instructs the terminal to perform AI / ML-based inference functions for CSI prediction

[0298] - resourcesForChannelPrediction-r19: Indicates future target resources that need to be predicted by the model, rather than resources where actual measurements are performed.

[0299] - nrofTimeInstance-r19: Specifies the number of future time instances to report as prediction results.

[0300] - 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.

[0301] - nrofReportedPredictedRS-r19: Determines the number of predicted resources (RS) to report to the base station at each reporting time.

[0302] -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.

[0303] In addition, the CSI reporting settings may include monitoring setting information for monitoring the performance of the AI ​​model related to the aforementioned CSI prediction. Specific parameters for this may be as follows.

[0304] - configurationForBM-Monitoring-r19 and configurationForCSI-Monitoring-r19: Established a monitoring environment to compare actual measurements with predicted values ​​to verify the performance of the prediction model.

[0305] - refToPredictionConfig-r19: A reference ID indicating which prediction report configuration (CSI-ReportConfigId) the monitoring configuration is associated with.

[0306] - mappingToResourcesForChannelPrediction-r19: Bitmap mapping information to link actual measurable monitoring resources with previously performed prediction results on a 1:1 basis.

[0307] - timeInstanceForRS-PAI-r19 and timeInstanceForCSI-PAI-r19: Specifies specific time instances or Doppler domain units used for calculating Performance Indicators (PAI).

[0308] - nrofBestBeamForMonitoring-r19: Specifies the number of optimal beams to consider for performance measurement, and nrofTransmissionOccasion-r19 indicates the number of recent transmission opportunities to include in the performance metric calculation.

[0309] 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.

[0310] Meanwhile, for the UE, historical CSI information may include historical model inputs and / or any information derived therefrom. For the network, historical CSI information may include historical CSI feedback instances and / or any information derived therefrom (note 1). For Cases 3 and 4, the UE may perform prediction as a separate step or in conjunction with compression. Similarly, the network may perform prediction as a separate step or in conjunction with restoration. Each company must report the selected option, the number of future slots, and whether the prediction is AI / ML-based (Note 2). “Target CSI slot(s)” refers to the slots corresponding to the CSI feedback within the report. “Present slot” refers to the slot of the most recent CSI-RS measurement used to generate the CSI report. “Future slot(s)” include at least one slot following the present slot and may include the present slot itself (Note 3). Down-selection is not excluded (Note 4).

[0311] In the following, for the convenience of explaining, understanding, and discussing the complexity of life cycle management (LCM) regarding joint prediction and compression (JPC), the explanation will primarily consider the case according to Case 3 described above. (Meanwhile, although the complexity of the aforementioned LCM could also be discussed regarding the aforementioned case, Case 3 was prioritized in the aforementioned Rel-19 discussion.)

[0312] Since the format of the input for the UE-side model and the type / format of the output for the NW-side model may differ, it may be necessary to discuss how to set the input and label for monitoring (e.g., monitoring model performance). This ambiguity can lead to complexity in the LCM for the model. For instance, regarding CSI compression, the input for the UE-side model is given as (raw) channel matrices, while the input for the NW-side model may be precoding matrices. Due to the increased LCM complexity caused by such differences in input data types / formats between the UE-side and NW-side models, the use of SPC may be preferred, even though JPC is expected to provide higher performance than SPC (Separate Prediction and Compression).

[0313] Below, we explain in detail how to resolve the LCM complexity of JPC by utilizing SPC models that operate as JPC during inference / training but are separately provided / configured for JPC performance monitoring.

[0314] 1. Model Training Method

[0315] Regarding the model training method, UE-side model #1 may be a prediction model, and UE-side model #2 may be a compression model (e.g., an encoder). Additionally, in the following paths A and B, the parts enclosed in square brackets '[]' are module / model / functional blocks, and the parts before and after them may be the inputs / outputs of the corresponding module / model / functional block.

[0316] - Path A: Channel matrices (past + current) -> [Model #1] -> channel matrix (future)

[0317] - Path B: Channel matrix (future) -> [Pre-processing] -> Precoding matrix -> [Model #2] -> CSI feedback (latent) -> [NW-side model] -> Reconstructed precoding matrix

[0318] Here, Model #1 and Model #2 are models intended solely for monitoring purposes (e.g., for monitoring the performance evaluation of JPC for CSI inference), and the two models can be trained separately and then deployed to the UE as dedicated monitoring models. With Model #1 and Model #2 already existing, the two models can be grafted to create an integrated Model U (JPC), and then Model U can be further trained to deploy Model UE as an inference model. When Model U is constructed by grafting Model #1 and Model #2, the pre-processing part of Path B can be replaced with pre-processing using an AI / ML algorithm rather than the existing non-AI / ML algorithm-based pre-processing. For example, the pre-processing part can be configured as part of a neural network.

[0319] The NW-side model can refer to a model that reconstructs the target CSI, such as a CSI reconstruction part or a decoder. For the sake of convenience, the NW-side model will be defined as a decoder in the following explanation.

[0320] 2. Performance Monitoring Methods

[0321] For example, the performance monitoring of the JPC model U and the NW-side model (e.g., decoder) can utilize the SPC models #1 and #2. Functionality / model control may operate differently depending on the monitoring output of each path (in Table 7 below, whether there is a model abnormality), and Table 7 shows examples of the monitoring output of each path and the corresponding function / model control methods.

[0322] Monitoring Output Function / Model Control Model #1 Model #2 or / and NW-side Model Normal Maintain Normal or activation Normal Abnormal Function / Model switch switch to Prediction Abnormal Normal switch to Compression Abnormal Abnormal deactivation or / and fallback

[0323] 3. Monitoring metrics / outputs

[0324] In the above performance monitoring method, the monitoring metric for Model #1 (e.g., CSI prediction model) and the monitoring metric for Model #2 (CSI compression model) may be different. Specifically, each monitoring metric may be defined as follows. For instance, the terminal may perform monitoring of the CSI prediction function of Model U and monitoring of the CSI compression function of Model U, respectively, based on monitoring resources (CSI-RS or SSB) configured based on monitoring configuration information. At this time, Model #1 and Model #2 configured separately as described above may be used.

[0325] (1) Monitoring metric / output for prediction or prediction model

[0326] As a metric based on the output of Model #1, a metric based on the difference between measured raw channel matrices (e.g., raw channel matrices measured from a reference resource set for monitoring) and predicted raw channel matrices may be considered. For example, a monitoring metric for the prediction function of Model U may be a metric calculated based on the difference between the predicted raw channel matrix or multiple predicted raw channel matrices predicted by Model #1 for a future point in time using the raw channel matrix or multiple raw channel matrices input to Model U as input information, and the actual measured raw channel matrix or multiple raw channel matrices from a reference signal resource received for monitoring according to the monitoring setting information (e.g., a reference signal received at a point in time corresponding to the future point in time). For example, such a metric may be considered as a method of calculating values ​​such as average MSE (Mean Squared Error) and average NMSE (Normalized Mean Squared Error).

[0327] - When a single time slot is the target CSI, methods such as calculating MSE, NMSE, etc., rather than the average, can be used as performance monitoring metrics for Model 1.

[0328] - When multiple time slots (t = 1, 2, 3) correspond to prediction windows, the method of calculating the average of MSE / NMSE for multiple time slots can be used as a performance monitoring metric for Model #1. Meanwhile, the above average may be a weighted average according to the time slots. For example, the weights for t=1, 2, and 3 may be different.

[0329] The monitoring output for Model #1 (or, the monitoring output for model performance) may be configured based on the aforementioned monitoring metric. The monitoring output may be provided as a binary value having a value of 0 when the NMSE or average NMSE is greater than a specific value or a predetermined threshold (e.g., -15 dB), and a value of 1 when the NMSE or average NMSE is less than or equal to the specific value or the predetermined threshold. Meanwhile, the predetermined threshold may be a value that is pre-set or signaled from a base station.

[0330] (2) Monitoring metrics / outputs for compression or compression models

[0331] Metrics for monitoring the performance of the compression model may include metrics that calculate values ​​such as (average) cosine similarity and SGCS, which are metrics based on the difference between the input of Model #2 and the output of the NW-side model. Here, the input of Model #2 may be a precoding matrix from measurements, and the output of the NW-side model may be a precoding matrix from measurements and a reconstructed CSI (precoding matrix) corresponding to N. For example, a monitoring metric for the compression function of Model U may be a metric calculated based on the difference between the input information of Model #2 and the precoding matrix reconstructed by the NW-side model (such information may be provided by the network / base station to the UE) after the network / base station receives the output information of Model #2. Here, the output information of the above model #2 may be output information obtained by inputting a raw channel matrix actually measured for a reference signal resource received for monitoring according to the above monitoring setting information (e.g., a reference signal received at a time corresponding to the above future time point) or a raw precoding matrix calculated from a plurality of raw channel matrices.

[0332] - When a single time slot is the target CSI, performance metrics such as cosine similarity and SGCS (squared generalized cosine similarity) can be used instead of the average.

[0333] - When multiple time slots (t = 1, 2, 3) correspond to prediction windows, a method of calculating the average SGCS for multiple time slots can be used as a performance monitoring metric for Model #1. Meanwhile, the above average may be a weighted average based on the time slots. For example, the weights for t=1, 2, and 3 may be different.

[0334] The monitoring output for Model #2 and the NW-side model may be configured based on the monitoring metric. For example, the monitoring output for Model #2 and the NW-side model may be provided as a binary value in which 0 is set when the SGCS / average SGCS is less than a specific threshold (e.g., 0.9), and 1 is set when it is greater than or equal to the threshold. Meanwhile, the specific threshold may be a value that is pre-set or signaled from a base station.

[0335] (3) Monitoring output for E2E (end-to-end) JPC (hereinafter, final monitoring output)

[0336] The above final monitoring output may be expressed as a combination of the monitoring outputs for Model #1 and Model #2 (and / or, the decoder). For example, as shown in Table 7 above, the final monitoring output for the E2E JPC may be composed of a 2-bit sequence. For example, the final monitoring output may have the following meanings.

[0337] - 00: deactivation or / and fallback (e.g., deactivation or fallback of Model U)

[0338] - 01, 10: Function / Model switch (e.g., Function / Model switch for Model U; for 01, switch for the Prediction Model or Off for the Prediction Model, for 10, switch for the Compression Model or Off for the Compression Model)

[0339] - 11: Maintenance or activation (e.g., maintenance or activation of Model U)

[0340] The final monitoring output for such an E2E JPC can be determined by the monitoring entity (NW-side and / or UE-side). To this end, monitoring metrics calculated by the counterpart (UE-side / NW-side) of the monitoring entity (NW-side and / or UE-side) can be signaled to the monitoring entity.

[0341] The final monitoring output determined by the monitoring entity (NW-side / UE-side) may be directed to the counterpart (UE-side / NW-side) for the purpose of function / model control (indicators defined in Table 7 and the purpose of each indicator). Meanwhile, for the consistency of the two-sided model, the counterpart (UE-side / NW-side) may report an ACK / NACK regarding the indicators included in the final monitoring output to the monitoring entity (NW-side / UE-side).

[0342] As described above, prior to monitoring Model U with the separate metrics for each of the separately provided / configured Model #1 and Model #2, monitoring of Model U may be performed first. In this case, the performance metric for Model U can be calculated by comparing the output of the NW-side model (decoder) with a ground-truth label based on the CSI measured / predicted for future slot(s) on the UE-side.

[0343] Meanwhile, the above monitoring metrics or outputs are merely examples and should not be interpreted as limiting the scope of the proposed method. For instance, in the example described above, the monitoring output was set to be indicated via a bit indicator, but the value of the monitoring metric itself may also be signaled.

[0344] 4. Case of UE-assisted monitoring

[0345] For example, in UE-assisted monitoring where the monitoring entity is the NW and the UE provides auxiliary information, the monitoring metric corresponding to the binary bit / binary value in Table 7 above may be reported to the NW for Model #1, but the SGCS value may be reported to the NW as the monitoring metric for Model #2. In this case, it may be assumed that a nominal decoder exists on the UE side for calculating the monitoring metric for Model #2. Alternatively, the monitoring metric corresponding to the binary bit / binary value in Table 7 above may be reported to the NW only for Model #1, and no value may be reported for Model #2. In this case, all or part of the final monitoring output may be transmitted from the NW to the UE.

[0346] Alternatively, the values ​​classified as binary states (normal / abnormal) in the examples and Table 7 described above may be subdivided into three (or two) or more state values. For example, the monitoring output may be configured to be classified as 0 (anomaly in the model itself), 1 (problem due to external factors), and 2 (normal). For example, the monitoring output for Model #1 (prediction model) may include a 2-bit indicator, and / or the monitoring output for Model #2 (compression model) may also include a 2-bit indicator.

[0347] Meanwhile, since monitoring models #1 and #2 must be tied / associated with inference model U, pairing between {Models #1, 2} and model U may be required. As the mapping / association information between the monitoring models and the inference model must be aligned with the NW-side model (two-sided model), alignment between the monitoring models and the inference model can be performed through configuration from NW to UE or indication from UE to NW. For example, assuming a situation where models #1 and #2 preexist with model U, model U has a dependency on models #1 and #2, and simultaneously, model U may be paired with the NW-side model (two-sided model). Therefore, models #1 and #2 can be paired with the NW-side model through model U. For example, {Model #1 and #2}:Model U:NW-side models are not in a 1:1:1 correspondence relationship m:n:m (m <n) 또는 1:N:1의 관계일 수 있다. 이 경우, 모니터링 용인 {모델#1,2} 및 NW-측 모델을 참조 (reference) 모델 페어 (pair)로 하되, 단일한 참조 모델 페어를 기반으로 복수의 모델 U가 개발될 수 있다. 일반적으로, {모델#1,#2} : 모델 U : NW-측 mode이 x:y:z의 mapping이 가능할 수 있다.

[0348] In this way, the monitoring method for the CSI compression function or model allows the terminal to report a first monitoring metric (or monitoring output) and a second monitoring metric (or monitoring output) to the base station. The first monitoring metric (or monitoring output) may be a value associated with the CSI compression function of the model U, and the second monitoring metric (or monitoring output) may be a value associated with the CSI prediction function of the model U. The first monitoring metric (or monitoring output) and the second monitoring metric (or monitoring output) may be calculated in different ways. Additionally, the first monitoring metric (or monitoring output) and the second monitoring metric (or monitoring output) may be configured in different ways. Meanwhile, a setting for a third monitoring metric (or monitoring output) expressed as information in a concatenated or combined form based on the first monitoring metric (or monitoring output) and the second monitoring metric (or monitoring output) may be instructed from the network to the terminal for part or all of the first function (or model), which is a CSI compression model, and the second function (or model), which is a CSI prediction model.

[0349] As such, the proposed method can reduce the overhead for model monitoring. For example, Model #2 and the NW-side model can be monitored in the same way as the existing case 0, and the UE-side Model #1 can be monitored independently of the UE side. Additionally, superior performance improvement compared to SPC can be expected due to joint optimization / joint training between the prediction and compression models during inference.

[0350] Figure 19 is a diagram illustrating how a UE performs monitoring of a CSI inference model.

[0351] Referring to FIG. 19, the step (S191) of measuring a first CSI associated with the first measurement time point can be performed by the UE measuring a channel from a reference signal resource based on a CSI reporting setting or measurement setting provided by a base station. For example, the reference signal resource may include a CSI-RS resource or an SSB resource, and the UE may obtain a raw channel matrix or a plurality of raw channel matrices using a signal received from the resource. The first CSI may be a channel measurement value at a single time point, or a set of past and present channel measurements accumulated over multiple time points. For example, the first CSI may be configured in a preprocessed or aligned form to match the input format of an AI-based inference model, and may include channel information corresponding to a plurality of historical measurements, feature information derived therefrom, or a combination thereof.

[0352] The step (S193) of transmitting output information of an AI-based inference model that takes the first CSI as input to a base station may be a process in which the UE inputs the first CSI into the AI-based inference model and transmits output information generated as an inference result to the base station in the form of CSI feedback information. Here, the AI-based inference model may be an inference model operating on the UE side, and may be a single model U (e.g., JPC) that combines a CSI prediction function and a CSI compression function. For example, the model U may internally generate a predicted CSI corresponding to a future point in time (e.g., a future point in time set / instructed through the monitoring setting information described above) using the input first CSI, and may output a latent expression or compressed CSI feedback information by compressing the predicted CSI or the corresponding precoding matrix. The output information may include at least one of p-CRI, p-SSB-Index, compressed CSI expression, or other feedback information corresponding to the prediction result. For example, the output information may include latent information in which the predicted CSI or the corresponding precoding matrix is ​​compressed and represented in a latent space, and the latent information may be configured in a form recoverable by a base station or network-side NW-side model (e.g., a decoder).

[0353] For example, the operation of the above AI-based inference model can be controlled by reporting settings such as CSI-ReportConfig. For instance, parameters such as predictionConfiguration-r19, csi-InferencePrediction-r19, resourcesForChannelPrediction-r19, nrofTimeInstance-r19, timeGap-r19, and nrofReportedPredictedRS-r19 can instruct the UE to predict the CSI at a given point in time, and to generate and report information corresponding to a specific resource for several future points in time. Accordingly, the UE can infer a target CSI for a future slot or multiple future slots based on a first CSI measured at the current or past point in time, and transmit the result to the base station.

[0354] The step (S195) of reporting monitoring information related to the performance of the AI-based inference model to the base station based on the second CSI measured at the second measurement point after the first measurement point may be a process for indirectly evaluating the performance of the model U. For example, the second measurement point may be a point in time corresponding to a future point in time predicted by the model U, and the UE may measure the second CSI through a reference signal resource received at the second measurement point. For example, the second measurement point may be a time resource of a measurement point or measurement resource indicated / set through the monitoring setting information, and may be a point in time corresponding to a future point in time predicted by the model U in relation to the performance monitoring of the model U. The second CSI may be utilized as a ground-truth type CSI corresponding to the future point in time and may be used to evaluate the performance of the prediction function and compression function of the model U separately.

[0355] Specifically, the monitoring information may include first monitoring information and second monitoring information corresponding to the results of monitoring the CSI prediction function and CSI compression function of the AI-based inference model separately. Here, the first monitoring information may be calculated based on the output information of a first model separately configured to be linked with the CSI prediction function of the AI-based inference model and the second CSI, and the second monitoring information may be calculated based on the output information of a second model separately configured to be linked with the CSI compression function of the AI-based inference model and the second CSI. For example, even if the AI-based inference model operates in a JPC structure during inference, during performance monitoring, the performance of each function can be evaluated separately by using a first model corresponding to the prediction function and a second model corresponding to the compression function separately.

[0356] For example, the first model may be a model that receives the first CSI as input and outputs a predicted CSI for a future time point corresponding to the second measurement time point. For example, the first model may receive a raw channel matrix or a plurality of raw channel matrices (e.g., a channel matrix based on the first CSI measured at the first measurement time point) as input and generate a predicted raw channel matrix or a plurality of predicted raw channel matrices for a future slot (e.g., a future time point corresponding to the second measurement time point). In this case, the first monitoring information may be calculated based on the difference between the predicted CSI and the second CSI actually measured at the second measurement time point.

[0357] The first monitoring information may include a 1-bit first indicator determined based on a first value calculated using a first metric. The first metric may be a metric that calculates the mean squared error (MSE) or normalized mean squared error (NMSE) that quantifies the difference between the second CSI and the predicted CSI. For a single target time instance, the MSE or NMSE itself may be used, and if multiple future time points are the prediction targets, the average value or weighted average value of the MSE or NMSE for each time point may be used. For example, the first monitoring information may include a 1-bit first indicator set according to whether the MSE or NMSE value is below a first threshold value. For example, if the MSE or NMSE value is below the first threshold value, the first indicator may be set to 1, and if it exceeds the first threshold value, the first indicator may be set to 0. The first threshold value may be a preset value or a value signaled from a base station. Accordingly, the first monitoring information may be provided as a monitoring result in binary form indicating whether the prediction function operates within a normal range.

[0358] The second model may be a model that receives a precoding matrix calculated based on the second CSI as input and outputs latent information represented by a compressed expression of the precoding matrix. For example, the UE may calculate a precoding matrix from a raw channel matrix or a plurality of raw channel matrices measured at a second measurement point, and input the calculated precoding matrix into the second model to generate latent information. The latent information may be restored into a precoding matrix reconstructed by a decoder, etc. The reconstructed precoding matrix may be one in which the UE provides the output information of the second model to a base station, and the base station reconstructs it through a decoder and then provides it to the UE. Alternatively, the reconstructed precoding matrix may be one in which the UE obtains it through a nominal decoder based on the output information of the second model. Here, the nominal decoder may refer to a reference decoder provided on the UE side to have the same or substantially the same restoration logic, structure, parameter set, or corresponding restoration characteristics as the base station-side decoder. More specifically, the nominal decoder may be a functional block configured to receive latent information or a compressed CSI representation, which is the output information of the second model, from the UE, and to generate a reconstructed precoding matrix by simulating or emulating the restoration operation performed by the base station's NW-side model or the decoder on the UE side. In this case, the second monitoring information may be calculated based on the similarity or difference between the precoding matrix and the precoding matrix reconstructed based on the latent information.

[0359] For example, the second monitoring information may include a 1-bit second indicator determined based on a second value calculated using a second metric different from the first metric. The second metric may be a metric that calculates cosine similarity or squared generalized cosine similarity (SGCS) to quantify the similarity between the precoding matrix and the precoding matrix reconstructed based on the latent information. For a single target time instance, the cosine similarity or SGCS value may be used directly, and when monitoring compression performance for multiple future time points, the average value or weighted average value of the SGCS for each time point may be used. Additionally, the second monitoring information may include a 1-bit second indicator set based on whether the cosine similarity or SGCS value is greater than or equal to a second threshold. For example, if the similarity value is greater than or equal to the second threshold, the second indicator may be set to 1, and if it is less than the second threshold, the second indicator may be set to 0.

[0360] For example, the monitoring information may include the first monitoring information and the second monitoring information, respectively, or may include information in a combined form of the first monitoring information and the second monitoring information. For example, the monitoring information may include a 2-bit sequence in which the first indicator and the second indicator are concatenated or combined. The monitoring information may be provided as information indicating preference information related to the function control of the AI-based inference model. For example, “11” may be interpreted as a value indicating preference for maintaining or enabling the model, “01” or “10” as switching or turning off at least one of the prediction function or compression function, and “00” as preferring model deactivation or fallback. However, the form of expression of the monitoring information is not limited thereto, and multiple state values ​​or numeric values ​​of the metric itself may be reported as is instead of binary values.

[0361] Alternatively, the UE may further include the step of receiving control information for the AI-based inference model determined based on the monitoring information. The control information may be information for controlling the AI-based inference model based on the first monitoring information, the second monitoring information, or a combination thereof, which is included in the monitoring information by the base station or network. For example, the control information may include first instruction information instructing to maintain or enable the inference operation of the first CSI using the AI-based inference model, second instruction information instructing to switch or turn off the model for either the CSI prediction function or the CSI compression function of the AI-based inference model, or third instruction information instructing to disable or fallback the AI-based inference model. Accordingly, the UE may maintain, change, stop, or replace the operating state of Model U based on the control information.

[0362] According to the above method, while maintaining the benefits of joint optimization or joint training based on the JPC structure during the inference phase, performance can be evaluated more clearly by separating the CSI prediction and CSI compression functions during the monitoring phase. As a result, model anomalies can be determined by distinguishing them by function, and granular model control becomes possible, such as switching only the prediction function or turning off only the compression function when necessary. Furthermore, compared to evaluating the entire model directly using only end-to-end metrics, this method has the effect of reducing monitoring overhead while more accurately identifying the causes of degradation in each function.

[0363] FIG. 20 is a diagram illustrating how a base station receives monitoring information about a CSI inference model from a UE.

[0364] Referring to FIG. 20, the step (S201) of receiving output information of an AI-based inference model having a first CSI measured from a UE in relation to a first measurement time point as input may be a process in which a base station receives output information generated by the UE-side AI-based inference model. Here, the output information generated by the AI-based inference model may be output information that includes information corresponding to an inference result generated based on the first CSI obtained from the UE-side AI-based inference model, wherein the UE inputs the first CSI measured from a reference signal resource received at the first measurement time point into the UE-side AI-based inference model. For example, the output information may include at least one of a prediction result for a future time point, compressed CSI feedback information, latent information, or CSI report information based thereon. Additionally, the AI-based inference model may be a JPC (joint prediction and compression) model that combines a CSI prediction function and a CSI compression function. By receiving the output information, the base station may obtain an inference result performed by the UE-side AI-based inference model based on the first CSI. In addition, the base station can calculate / obtain a precoding matrix reconstructed from the output information by using the received output information to perform subsequent processing linked with a network-side model or a decoder that performs a restoration function.

[0365] The step (S203) of receiving monitoring information related to the performance of the AI-based inference model based on the second CSI measured at the second measurement point after the first measurement point may be a process in which the base station receives from the UE monitoring information generated based on the second CSI measured by the UE at the second measurement point after the first measurement point (e.g., a measurement resource or measurement point set based on monitoring setting information included in the CSI reporting setting). Here, the second measurement point may be a future time point set as a prediction target by the AI-based inference model or a time point corresponding thereto, and the second CSI may include channel state information actually measured at a reference signal resource corresponding to the future time point. Additionally, the monitoring information may include first monitoring information and second monitoring information corresponding to the results of monitoring the CSI prediction function and CSI compression function of the AI-based inference model separately.

[0366] For example, the first monitoring information may be information related to the CSI prediction function of the AI-based inference model, and may be information calculated based on the difference between the output of a separately configured prediction model and the second CSI. Additionally, the second monitoring information may be information related to the CSI compression function of the AI-based inference model, and may be information calculated based on the similarity or difference between the output of a separately configured compression model, or the corresponding restoration result (e.g., a precoding matrix reconstructed through decoding based on the output information of the second model) and the precoding matrix calculated based on the second CSI. Meanwhile, the reconstructed precoding matrix may be one provided to the UE after the UE provides the output information of the second model to the base station and the base station reconstructs it through a decoder. Alternatively, the reconstructed precoding matrix may be one obtained by the UE through a nominal decoder based on the output information of the second model.

[0367] For example, the first monitoring information and the second monitoring information may each be provided in the form of binary indicators, or may be provided in the form of the metric values ​​themselves. Additionally, the first monitoring information and the second monitoring information may each be included independently, or may be provided as information configured in a concatenated or combined form.

[0368] Accordingly, by receiving the monitoring information, the base station can distinguish and verify the performance status of the CSI prediction function and the CSI compression function of the AI-based inference model, and can generate control information related to the AI-based inference model based on the combination of the performance statuses. For example, the base station can generate control information instructing the model to be maintained or activated if both the first monitoring information and the second monitoring information indicate a normal state; can generate control information instructing the model to be switched or turned off if only one of them indicates an abnormal state; and can generate control information instructing the model to be deactivated or fallback if both indicate an abnormal state, and can transmit the generated control information to the UE.

[0369] As such, the proposed invention distinguishes the CSI prediction and CSI compression functions of an AI-based inference model to generate and report corresponding monitoring information. This allows for the detailed identification of the causes of model performance degradation by function, thereby enabling more precise control such as maintenance, activation, switching, off, deactivation, or fallback. Furthermore, the proposed invention utilizes a Joint Prediction and Compression (JPC) model during the inference phase to promote performance improvement through joint optimization of prediction and compression. In contrast, by using separately configured prediction and compression models during the monitoring phase, it enables monitoring. Consequently, this reduces the complexity of Model Lifecycle Management (LCM) and monitoring overhead while ensuring stable model operation.

[0370] Example of a communication system to which the invention is applied

[0371] 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.

[0372] 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.

[0373] FIG. 21 illustrates a communication system to which the present invention is applied.

[0374] Referring to FIG. 21, 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.

[0375] 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).

[0376] 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.

[0377] Example of a wireless device to which the present invention is applied

[0378] FIG. 22 illustrates a wireless device that can be applied to the present invention.

[0379] Referring to FIG. 22, 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. 21.

[0380] 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.

[0381] 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. 17 through 20 in the section “Monitoring for joint prediction and compression”. The operations include measuring a first CSI (channel state information) related to a first measurement point, transmitting output information of an AI (Artificial Intelligence) based inference model with the first CSI as input to a base station, and reporting monitoring information related to the performance of the AI ​​based inference model to the base station based on a second CSI measured at a second measurement point after the first measurement point, and the monitoring information may include first monitoring information and second monitoring information corresponding to the results of monitoring the CSI prediction function and CSI compression function of the AI ​​based inference model separately.

[0382] Alternatively, a processing device may be configured including a processor (102) and a memory (104) for controlling 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 measuring a first CSI (channel state information) related to a first measurement point, transmitting output information of an AI (Artificial Intelligence) based inference model with the first CSI as input to a base station, and reporting monitoring information related to the performance of the AI ​​based inference model to the base station based on a second CSI measured at a second measurement point after the first measurement point. The monitoring information may include first monitoring information and second monitoring information corresponding to the results of monitoring the CSI prediction function and CSI compression function of the AI ​​based inference model separately. Alternatively, at least one non-transient computer-readable medium may be configured that stores programs / instructions for performing the above-described operations.

[0383] 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.

[0384] 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. 17 through 20 in the section "Monitoring for joint prediction and compression". The operations include controlling the RF transceiver (206) to receive output information of an AI (Artificial Intelligence) based inference model that takes as input a first CSI (channel state information) measured in relation to a first measurement time from a UE (User Equipment), and receiving monitoring information related to the performance of the AI ​​based inference model based on a second CSI measured at a second measurement time after the first measurement time. The monitoring information may include first monitoring information and second monitoring information corresponding to the results of monitoring the CSI prediction function and CSI compression function of the AI ​​based inference model separately.

[0385] 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.

[0386] 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.

[0387] 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.

[0388] 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.

[0389] Examples of wireless device applications to which the present invention is applied

[0390] FIG. 23 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. 21).

[0391] Referring to FIG. 23, the wireless device (100, 200) corresponds to the wireless device (100, 200) of FIG. 22 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. 23. For example, the transceiver(s) (114) may include one or more transceivers (106, 206) and / or one or more antennas (108, 208) of FIG. 22. 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 the outside (e.g., another communication device) via a wireless / wired interface through the communication unit (110), or store information received from the outside (e.g., another communication device) via a wireless / wired interface through the communication unit (110) in the memory unit (130).

[0392] 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. 21, 100a), a vehicle (Fig. 21, 100b-1, 100b-2), an XR device (Fig. 21, 100c), a portable device (Fig. 21, 100d), a home appliance (Fig. 21, 100e), an IoT device (Fig. 21, 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. 21, 400), a base station (Fig. 21, 200), a network node, etc. Wireless devices can be used in a movable or fixed location depending on the use—e.g., service.

[0393] In FIG. 23, 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.

[0394] Examples of vehicles or autonomous vehicles to which the present invention is applied

[0395] FIG. 24 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.

[0396] Referring to FIG. 24, 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. 23, respectively.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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).

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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 measuring the first CSI (channel state information) associated with the first measurement point; A step of transmitting output information of an AI (Artificial Intelligence)-based inference model having the above-mentioned first CSI as input to a base station; and It includes the step of reporting monitoring information related to the performance of the AI-based inference model to the base station based on the second CSI measured at the second measurement point after the first measurement point, and A method comprising: the above monitoring information including first monitoring information and second monitoring information corresponding to the results of monitoring the CSI prediction function and CSI compression function of the AI-based inference model separately.

2. In Paragraph 1, The above first monitoring information is calculated based on the output information of the first model, which is separately configured to be linked with the CSI prediction function of the AI-based inference model, and the second CSI. A method in which the second monitoring information is calculated based on the output information of a second model separately configured to be linked with the CSI compression function of the AI-based inference model and the second CSI.

3. In Paragraph 2, The above-mentioned first model is a model that receives the above-mentioned first CSI as input and outputs a predicted CSI for a future time point corresponding to the above-mentioned second measurement time point, and A method in which the above-mentioned second model is a model that receives a precoding matrix calculated based on the above-mentioned second CSI as input and outputs latent information that compresses the precoding matrix.

4. In Paragraph 3, The above-mentioned first monitoring information includes a 1-bit first indicator determined based on a first value calculated using a first metric, and A method comprising the second monitoring information including a 1-bit second indicator determined based on a second value calculated using a second metric different from the first metric.

5. In Paragraph 3, The first metric is a metric that calculates an MSE (mean squared error) or NMSE (normalized MSE) value that quantifies the difference between the second CSI and the predicted CSI, and A method in which the second metric is a metric that calculates a cosine similarity or SGCS (squared generalized cosine similarity) value that quantifies the similarity between the precoding matrix and the precoding matrix reconstructed based on the latent information.

6. In Paragraph 4, The first monitoring information includes a first indicator of 1 bit set to 0 or 1 based on whether the MSE or NMSE value is below a first threshold value, and A method comprising the second monitoring information including the second indicator of 1 bit set to 0 or 1 based on whether the cosine similarity or the SGCS value is greater than or equal to the second threshold value.

7. In Paragraph 1, A method further comprising the step of receiving control information of the AI-based inference model determined based on the above monitoring information.

8. In Paragraph 7, A method comprising the above control information including a first instruction information instructing to maintain or enable the inference operation of the first CSI using the AI-based inference model, a second instruction information instructing to switch or turn off the model for either the CSI prediction function or the CSI compression function of the AI-based inference model, or a third instruction information instructing to disable or fallback the AI-based inference model.

9. In Paragraph 1, The above AI-based inference model is a JPC (Joint prediction and compression) model combined with the above CSI prediction function, a method.

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, Measure the first CSI (channel state information) associated with the first measurement point; Transmitting output information of an AI (Artificial Intelligence)-based inference model having the above-mentioned first CSI as input to a base station; and It includes reporting monitoring information related to the performance of the AI-based inference model to the base station based on the second CSI measured at the second measurement point after the first measurement point, and At least one non-transient computer-readable recording medium comprising: first monitoring information and second monitoring information corresponding to the results of monitoring the CSI prediction function and CSI compression function of the AI-based inference model separately, respectively.

11. Regarding UE (User Equipment), RF (Radio Frequency) transceiver; and It includes a processor connected to the above RF transceiver, and The processor measures a first CSI (channel state information) related to a first measurement point in time, transmits output information of an AI (Artificial Intelligence)-based inference model with the first CSI as input to a base station, and reports monitoring information related to the performance of the AI-based inference model to the base station based on a second CSI measured at a second measurement point in time after the first measurement point in time. A UE comprising a first monitoring information and a second monitoring information corresponding to the results of monitoring the CSI prediction function and the CSI compression function of the AI-based inference model separately, respectively.

12. In Paragraph 11, The above first monitoring information is calculated based on the output information of the first model, which is separately configured to be linked with the CSI prediction function of the AI-based inference model, and the second CSI. The above second monitoring information is output information of a second model separately configured to be linked with the CSI compression function of the AI-based inference model, and a UE calculated based on the second CSI.

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: Measure the first CSI (channel state information) associated with the first measurement point; Transmitting output information of an AI (Artificial Intelligence)-based inference model having the above-mentioned first CSI as input to a base station; and It includes reporting monitoring information related to the performance of the AI-based inference model to the base station based on the second CSI measured at the second measurement point after the first measurement point, and A processing device comprising a first monitoring information and a second monitoring information corresponding to the results of monitoring the CSI prediction function and the CSI compression function of the AI-based inference model separately, respectively.

14. In the method using a base station, A step of receiving output information of an AI (Artificial Intelligence)-based inference model having as input a first CSI (channel state information) measured in relation to a first measurement time point from a UE (User Equipment); and The method includes the step of receiving monitoring information related to the performance of the AI-based inference model based on the second CSI measured at the second measurement point after the first measurement point, and A method comprising: the above monitoring information including first monitoring information and second monitoring information corresponding to the results of monitoring the CSI prediction function and CSI compression function of the AI-based inference model separately.

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 receive output information of an AI (Artificial Intelligence)-based inference model, which takes as input a first CSI (channel state information) measured in relation to a first measurement point from the UE (User Equipment), and receives monitoring information related to the performance of the AI-based inference model based on a second CSI measured at a second measurement point after the first measurement point. A base station comprising a first monitoring information and a second monitoring information corresponding to the results of monitoring the CSI prediction function and the CSI compression function of the AI-based inference model separately, respectively.

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