Method and apparatus for performance monitoring

Abstract

A method, according to one embodiment of the present specification, comprises the steps of: receiving configuration information related to channel state information (CSI) from a base station; and reporting the CSI to the base station. The CSI includes first CSI related to performance monitoring. A rank of the first CSI is determined on the basis of a rank indicator (RI) of second CSI which is the most recent among one or more pieces of CSI related to prediction.

Description

Method and apparatus for performance monitoring

[0001] This specification relates to a method and apparatus for performance monitoring.

[0002] Mobile communication systems were developed to provide voice services while ensuring user mobility. However, mobile communication systems have expanded their scope to include data services as well as voice. Currently, due to the explosive increase in traffic leading to resource shortages and users demanding higher-speed services, more advanced mobile communication systems are required.

[0003] The requirements for next-generation mobile communication systems largely include the ability to accommodate explosive data traffic, a dramatic increase in transmission rates per user, a significantly increased number of connected devices, very low end-to-end latency, and high energy efficiency. To achieve this, various technologies are being researched, such as dual connectivity, massive multiple input multiple output (MMIMO), in-band full duplex, non-orthogonal multiple access (NOMA), super wideband support, and device networking.

[0004] Meanwhile, a CSI prediction operation based on a terminal-side model (UE-side model) is defined. Performance monitoring of the CSI prediction may be performed to evaluate the accuracy of the CSI prediction. Specifically, the terminal may report the predicted CSI and the ground-truth CSI actually measured for performance monitoring to the base station.

[0005] Methods for calculating performance metrics have been proposed to effectively monitor the performance of the UE-side model for channel state information prediction (CSI prediction). Specifically, a method for performance monitoring has been proposed in which i) a terminal calculates a performance metric based on a predicted CSI and a actually measured CSI (e.g., ground-truth CSI) and reports a performance monitoring output based on the performance metric (type 1); ii) a terminal reports the predicted CSI and ground-truth CSI to a base station, and the base station calculates a performance metric based on the predicted CSI and ground-truth CSI to perform performance monitoring (type 2); and iii) a terminal calculates a performance metric based on a predicted CSI and a actually measured CSI (e.g., ground-truth CSI) and reports it to a base station, and the base station performs performance monitoring based on the performance metric (type 3).

[0006] In particular, in the case of type 2 among performance monitoring types, the reporting settings for the predicted CSI reported by the terminal and the reporting settings for the ground-truth CSI may differ, and as a result, the performance metric may be calculated abnormally low, and consequently, there is a problem in that the base station cannot perform accurate performance comparison and judgment based on the predicted CSI and ground-truth CSI.

[0007] The purpose of this specification is to propose a method for aligning reporting settings for ground-truth CSI based on reporting settings for predicted CSI in order to solve the aforementioned problem.

[0008] The technical problems to be solved in this specification 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 this invention belongs from the description below.

[0009] To solve the aforementioned technical problem, a method according to one embodiment of the present specification includes the steps of receiving configuration information related to channel state information (CSI) from a base station and reporting the CSI to the base station.

[0010] The above CSI includes a first CSI related to performance monitoring.

[0011] The rank of the first CSI is determined based on the rank indicator (RI) of the most recent second CSI among one or more CSIs related to the prediction.

[0012] Through this, the terminal can report a ground-truth CSI that maintains correlation with the predicted CSI without receiving separate signaling from the base station, and the base station can perform more accurate performance monitoring based on this, thereby achieving the effects of reducing signaling overhead and improving the accuracy of performance monitoring.

[0013] The above prediction may be related to the terminal-side model (UE-side model).

[0014] The results of the above performance monitoring may be based on performance metrics.

[0015] The above performance metrics can be calculated by the base station.

[0016] The RI of the first CSI above may be the same as the RI of the second CSI above.

[0017] One or more layers of the first CSI can be determined based on one or more layers of the second CSI.

[0018] One or more layers of the first CSI may include a layer corresponding to a layer indicator (LI) of the second CSI.

[0019] The above performance monitoring can be performed based on a layer corresponding to the LI.

[0020] The precoding matrix of the first CSI can be determined based on the spatial-domain vector and / or frequency-domain coefficient of the precoding matrix of the second CSI.

[0021] A terminal (user equipment, UE) according to another embodiment of the present specification comprises one or more transceivers, one or more processors, and one or more memories connected to the one or more processors and storing instructions. The instructions are characterized by causing the terminal to perform all steps of any one of the methods based on execution by the one or more processors.

[0022] An apparatus according to another embodiment of the present specification comprises one or more memories and one or more processors connected to the one or more memories. The one or more memories are characterized by storing instructions that cause the apparatus to perform all steps of any one of the methods based on execution by the one or more processors.

[0023] A non-transitory computer-readable storage medium according to another embodiment of the present specification stores instructions. The instructions, executable by one or more processors, are characterized by enabling a terminal to perform all steps of any one of the methods.

[0024] A method according to another embodiment of the present specification includes the steps of transmitting configuration information related to channel state information (CSI) to a terminal (user equipment, UE) and receiving the CSI from the terminal.

[0025] The above CSI includes a first CSI related to performance monitoring.

[0026] The rank of the first CSI is determined based on the rank indicator (RI) of the most recent second CSI among one or more CSIs related to the prediction.

[0027] A base station according to another embodiment of the present specification includes one or more transceivers, one or more processors, and one or more memories connected to the one or more processors and storing instructions. The instructions are characterized by causing the base station to perform all steps of any one of the methods based on execution by the one or more processors.

[0028] According to the prior art, the predicted CSI reported by the terminal and the ground-truth CSI actually measured may be generated and reported based on different reporting settings, which results in a problem where the performance metric is calculated abnormally low even when reflecting the same channel state. According to the embodiments of the present specification, the ground-truth CSI is aligned and reported based on the predicted CSI, thereby preventing performance distortion caused by the discrepancy in representation between the two CSIs, which has the effect of improving the accuracy of the base station's performance metric calculation and performance monitoring.

[0029] According to the prior art, additional signaling or complex configuration adjustments may be required to align reporting settings for predicted CSI and ground-truth CSI, which leads to problems such as increased system complexity and overhead. According to the embodiments of this specification, by aligning the ground-truth CSI based on the most recent predicted CSI, the terminal enables performance monitoring without separate additional signaling, thereby improving system efficiency.

[0030] The effects obtainable in this specification 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.

[0031] The drawings attached below are intended to aid in understanding the present disclosure and may provide embodiments of the present disclosure together with the detailed description. However, the technical features of the present disclosure are not limited to specific drawings, and the features disclosed in each drawing may be combined with one another to form new embodiments. Reference numerals in each drawing may denote structural elements.

[0032] Figure 1 is a flowchart illustrating an example of a CSI-related procedure.

[0033] Figure 2 is a diagram illustrating the overall functions from the perspective of an AI / ML model.

[0034] Figure 3 is a flowchart showing the general form of an AI / ML-related procedure performed between a network and a terminal.

[0035] Figure 4 is a flowchart illustrating an example of an AI / ML-based CSI measurement / reporting operation.

[0036] Figure 5 is a diagram showing an example of a CSI report based on an AI / ML model.

[0037] Figure 6 is a diagram showing an example of CSI prediction based on an AI / ML model.

[0038] Figure 7 is a diagram showing an example of CSI prediction for multiple time instances.

[0039] FIG. 8 is a flowchart illustrating a method according to one embodiment of the present specification.

[0040] FIG. 9 is a flowchart illustrating a method according to another embodiment of the present specification.

[0041] FIG. 10 is a drawing showing the configuration of a first device and a second device according to an embodiment of the present specification.

[0042] Hereinafter, preferred embodiments according to the present specification will be described in detail with reference to the accompanying drawings. The detailed description disclosed below, together with the accompanying drawings, is intended to describe exemplary embodiments of the present specification and is not intended to represent the only embodiment in which the invention according to the present specification can be practiced. The following detailed description includes specific details to provide a complete understanding of the present specification.

[0043] In some cases, to avoid obscuring the concept of the invention according to the embodiments of this specification, known structures and devices may be omitted or illustrated in the form of a block diagram focusing on the core functions of each structure and device.

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

[0045] < CSI Related Operations >

[0046] 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).

[0047] CSI (channel state information) is a general term for information that can indicate the quality of the wireless channel (also called a link) formed between a terminal and an antenna port.

[0048] The base station transmits CSI-RS to the terminal to determine the characteristics of the downlink channel and can receive feedback from the terminal regarding the channel measurement results based on CSI-RS.

[0049] 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.

[0050] 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).

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] Figure 1 is a flowchart illustrating an example of a CSI-related procedure.

[0056] 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 (S110).

[0057] The configuration information related to the above CSI may include at least one of CSI-IM (interference management) resource-related information, CSI measurement configuration-related information, CSI resource configuration-related information (e.g., M≥1 CSI-ResourceConfig resource setting), CSI-RS resource-related information, or CSI report configuration-related information (e.g., N≥1 CSI-ReportConfig reporting setting).

[0058] For example, the configuration information may include a first CSI resource setting for measurement and a second CSI resource setting for prediction. As a specific example, the measurement related to the prediction of CSI described below may be performed based on the first CSI resource setting. The terminal may perform L1-RSRP measurements on CSI-RS resources or SS / PBCH block resources associated with the first CSI resource setting. As a specific example, the prediction of CSI described below may be performed based on the second CSI resource setting. Based on the L1-RSRP measurements, the terminal may perform predictions on CSI-RS resources or SS / PBCH block resources associated with the second CSI resource setting. In other words, using L1-RSRPs as measurement metrics, the best CRI / best SSBRI (e.g., P-CRI(s), P-SSBRI(s)) may be predicted.

[0059] For example, the above configuration information may include a first CSI reporting setting related to prediction and a second CSI reporting setting related to prediction accuracy.

[0060] i) Information related to CSI-IM resources may include CSI-IM resource information, CSI-IM resource set information, etc. A CSI-IM resource set is identified by a CSI-IM resource set ID (identifier), and one resource set includes at least one CSI-IM resource. Each CSI-IM resource is identified by a CSI-IM resource ID.

[0061] ii) Information related to CSI resource configuration may be expressed as CSI-ResourceConfig IE. Information related to CSI resource configuration defines a group including at least one of an NZP (non-zero power) CSI-RS resource set, a CSI-IM resource set, or a CSI-SSB resource set. 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.

[0062] Table 1 shows an example of an NZP CSI-RS resource set IE. As shown in Table 1, parameters indicating the use of CSI-RS (e.g., BM-related 'repetition' parameter, tracking-related 'trs-Info' parameter) can be set for each NZP CSI-RS resource set.

[0063]

[0064] Also, the repetition parameter corresponding to the higher layer parameter corresponds to 'CSI-RS-ResourceRep' of the L1 parameter.

[0065] iii) Information related to the CSI report configuration includes a reportConfigType parameter representing the time domain behavior and a reportQuantity parameter representing the quantity of CSI to be reported. The time domain behavior may be periodic, aperiodic, or semi-persistent.

[0066] The above reportQuantity parameter includes the channel quality indicator (CQI), precoding matrix indicator (PMI), CSI-RS resource indicator (CRI), SS / PBCH block resource indicator (SSBRI), layer indicator (LI), rank indicator (RI), L1-RSRP (Layer 1-Reference Signal Received Power), L1-SINR (Layer 1-Signal-to-Interference-plus-Noise Ratio), predicted CQI (P-CQI), predicted PMI (P-PMI), predicted CRI (P-CRI), predicted SSBRI (P-SSBRI), predicted LI (P-LI), predicted RI (P-RI), predicted L1-RSRP (P-L1-RSRP), and / or predicted L1-SINR (predicted It can be set to a value representing at least one of L1-SINR, P-L1-SINR) and / or Prediction Accuracy Indicator (PAI) (or RS-PAI).

[0067] For example, the reportQuantity parameter can be set to cri, ssb-Index, cri-RSRP, or ssb-Index-RSRP. cri represents the CSI-RS resource indicator (CRI). RSRP represents the L1-RSRP (Layer 1-Reference Signal Received Power). ssb-Index represents the SS / PBCH block resource indicator (SSBRI).

[0068] For example, the reportQuantity parameter can be set to p-cri, p-ssb-index, p-cri-RSRP, or p-ssb-index-RSRP. p-cri represents the predicted CRI (predicted CRI, P-CRI). p-ssb-index represents the predicted SSBRI (predicted SSBRI, P-SSBRI). p-cri-RSRP represents the predicted CRI (predicted CRI, P-CRI) and predicted L1-RSRP (predicted L1-RSRP, P-L1-RSRP). p-ssb-index-RSRP represents the predicted SSBRI (predicted SSBRI, P-SSBRI) and predicted L1-RSRP (predicted L1-RSRP, P-L1-RSRP).

[0069] For example, the reportQuantity parameter can be set to pai (or rs-pai). pai (or rs-pai) represents PA (or RS-PAI).

[0070] Information related to CSI report configuration can be expressed as CSI-ReportConfig IE, and Table 2 below shows an example of CSI-ReportConfig IE.

[0071]

[0072] - The terminal measures the CSI based on configuration information related to the above CSI (S120).

[0073] The above CSI measurement may include (1) a process of receiving CSI-RS from a terminal (S121) and (2) a process of computing CSI through the received CSI-RS (S122), and a detailed explanation thereof will be provided later.

[0074] In CSI-RS, the mapping of resource elements (RE) of CSI-RS resources in the time and frequency domains is established by the higher layer parameter CSI-RS-ResourceMapping.

[0075] Table 3 shows an example of CSI-RS-ResourceMapping IE.

[0076]

[0077] In Table 3, density (D) represents the density of CSI-RS resources measured in RE / port / PRB (physical resource block), and nrofPorts represents the number of antenna ports.

[0078] - The terminal reports the measured CSI to the base station (S130).

[0079] Here, if the quantity of CSI-ReportConfig in Table E is set to 'none (or No report)', the terminal may omit the report.

[0080] However, even if the above quantity is set to 'none (or No report)', the terminal may still report to the base station.

[0081] The case where the above quantity is set to 'none' is when aperiodic TRS is triggered or when repetition is set.

[0082] Here, the report of the above terminal can be omitted only when repetition is set to 'ON'.

[0083] CSI measurement

[0084] The NR system supports more flexible and dynamic CSI measurement and reporting. Here, the CSI measurement may include a procedure for receiving CSI-RS and acquiring CSI by computationing the received CSI-RS.

[0085] 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.

[0086] NR's CSI-IM-based 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.

[0087] The base station transmits precoded NZP CSI-RS to the terminal on each port of the configured NZP CSI-RS-based IMR.

[0088] The terminal assumes a channel / interference layer for each port in the resource set and measures interference.

[0089] 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.

[0090] We will examine resource setting and resource setting configuration in more detail.

[0091] resource setting

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] Next, one or more CSI resource settings for channel measurement (CM) and interference measurement (IM) are established through higher layer signaling.

[0098] - CSI-IM resource for interference measurement.

[0099] - NZP CSI-RS resources for interference measurement.

[0100] - NZP CSI-RS resources for channel measurement.

[0101] 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.

[0102] Here, CSI-IM (or ZP CSI-RS for IM) is primarily used for inter-cell interference measurements.

[0103] Also, the NZP CSI-RS for IM is mainly used for intra-cell interference measurement from multi-users.

[0104] 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.

[0105] resource setting configuration

[0106] As examined, resource setting can refer to a resource set list.

[0107] 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.

[0108] One reporting setting can be linked to up to three resource settings.

[0109] - 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.

[0110] - 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.

[0111] - 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.

[0112] For semi-persistent or periodic CSI, each CSI-ReportConfig is linked to periodic or semi-persistent resource setting(s).

[0113] - 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.

[0114] - 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.

[0115] CSI computation

[0116] 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.

[0117] 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.

[0118] 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.

[0119] For CSI measurement(s) other than L1-SINR, the terminal assumes the following:

[0120] - Each NZP CSI-RS port configured for interference measurement corresponds to the interference transport layer.

[0121] - All interference transmission layers of the NZP CSI-RS port for interference measurement consider the associated EPRE (energy per resource element) ratio.

[0122] - 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.

[0123] CSI Report

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The report 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 reports may be transmitted over the PUCCH. Semi-persistent CSI reports may be transmitted over the PUCCH or PUSCH based on MAC CEs indicating activation / deactivation. Non-periodic CSI reports 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 reports may be transmitted over the PUSCH.

[0128] 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.

[0129] For CSI reporting, the time and frequency resources available to the UE are controlled by the base station.

[0130] 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 L1-SINR.

[0131] In the case of CSI prediction, the CSI related to the prediction may include at least one of the predicted CQI (predicted CQI, P-CQI), predicted PMI (predicted PMI, P-PMI), predicted CRI (predicted CRI, P-CRI), predicted SSBRI (predicted SSBRI, P-SSBRI), predicted LI (predicted LI, P-LI), predicted RI (predicted RI, P-RI), predicted L1-RSRP (predicted L1-RSRP, P-L1-RSRP) and / or predicted L1-SINR (predicted L1-SINR, P-L1-SINR).

[0132] When monitoring the performance / accuracy of the above CSI prediction, the CSI related to prediction accuracy may include a Prediction Accuracy Indicator (PAI). Since the PAI may indicate the accuracy of predicted downlink reference signal(s) (e.g., predicted CRI(s) and / or predicted SSBRI(s)), the PAI may be interpreted / replaced with a Reference Signal-Prediction Accuracy Indicator (RS-PAI).

[0133] 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.

[0134] In addition, the time domain behavior of CSI reporting supports periodic, semi-persistent, and aperiodic.

[0135] 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.

[0136] ii) SP (semi-periodic) CSI reporting is performed on short PUCCH, long PUCCH, or PUSCH.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Additionally, the terminal reports the number of CSIs that can be calculated simultaneously.

[0146] AI / ML for Wireless Communication

[0147] With the advancement of computing technology, artificial intelligence (AI) and machine learning (ML) are being adopted across various industries and technological fields. In the field of wireless communication, various discussions are underway regarding the application of AI models trained on ML; notably, the 3GPP standardization process refers to this as AI / ML. In this specification, we use the term "AI / ML" following the terminology currently in use during the 3GPP standardization discussions; however, "AI / ML" may be referred to by various other terms depending on the progress of standardization and implementation in the future. For example, it may be referred to as "transmission / reception mode" or "signal / channel / operation / transmission / reception configuration" configured for AI / ML, but is not limited thereto. The meanings of the terms currently used in the 3GPP standardization process are briefly summarized as follows.

[0148] - AI / ML Model: Refers to a data-driven algorithm that applies AI / ML technology to generate a set of outputs containing predictive information and / or decision parameters based on a set of inputs.

[0149] - Data collection: This is the process of collecting data necessary for AI / ML model training, data analysis, and inference from network nodes, management entities, or terminals.

[0150] - 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.

[0151] - Offline training: A process of training a model based on previously collected data sets, where the trained model is used or provided for future inference.

[0152] - Online Training: A method in which the model is trained in real-time upon the acquisition of new training sample data and used for inference.

[0153] - AI / ML Inference: This is the process of making predictions or deriving decisions based on collected data and AI models using trained AI models. Meanwhile, depending on whether the AI / ML model is configured on both the transmitting and receiving devices or on only one, it can be classified into (i) two-sided models and (ii) one-sided models. In the case of (i) two-sided models, cooperative inference is performed through paired AI / ML models. Cooperative inference refers to cooperation between the network and the UE, where one side performs part of the inference and the other performs the remainder. (ii) One-sided models are divided into UE-side models and network-side models. In the case of one-sided models, inference is performed entirely by the UE / network-side models.

[0154] 1. Life Cycle Management (LCM) for AI / ML models

[0155] For AI / ML models, LCM is a concept that encompasses all overall procedures for the model, such as data collection, model training, model deployment, model inference, model monitoring, and model updates.

[0156] LCMs for AI / ML models can be broadly classified into functionality-based LCMs and model-ID-based LCMs. In functionality-based LCMs, the network can direct activation, deactivation, fallback, or switching for specific functions; in this case, the target AI / ML model may not be identified by the network. In model-ID (identifier)-based LCMs, the network can direct activation, deactivation, selection, or switching for AI / ML models identified based on their AI / ML model IDs.

[0157] Figure 2 is a diagram illustrating the overall functions from the perspective of an AI / ML model.

[0158] Referring to FIG. 2, a general AI / ML functional framework can 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).

[0159] 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 and can provide input data processed through data preparation.

[0160] 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).

[0161] The Model Training function (20) is a function that performs AI / ML model training, validation, and testing, and 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).

[0162] 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).

[0163] The Management function (30) is a function that monitors 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)).

[0164] 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).

[0165] A Model Transfer / Delivery Request (33) can be used to request model(s) from Model Storage (50).

[0166] 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).

[0167] 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).

[0168] 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.

[0169] 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. 2 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.

[0170] Model Transfer / Delivery (51) is used to transfer an AI / ML model to an inference function.

[0171] 2. General AI / ML related procedures between the network and the terminal

[0172] Figure 3 illustrates the general form of AI / ML-related procedures performed between a network and a terminal. While Figure 2 examined the LCM from the perspective of an AI / ML model, Figure 3 describes the general form of procedures performed between a terminal and a network from the perspective of signaling / protocols.

[0173] (1) AI / ML related setup procedure

[0174] Referring to FIG. 3, an AI / ML-related configuration procedure may be performed between the network and the terminal (S310). The AI / ML-related configuration procedure may include information exchange through at least one upper-layer signaling between the terminal and the network, and / or prior preparation / subsequent operations at the terminal / network respectively before / after the upper-layer signaling.

[0175] Specifically, the configuration procedure related to AI / ML may include, but is not limited to, at least one of the following: (i) reporting the capability of the AI / ML-related terminal, (ii) data collection, (iii) model training, (iv) model delivery / transmission, (v) selection of AI / ML functions / models, and (vi) configuration of various operations performed based on the AI / ML model (e.g., AI / ML-based CSI / Positioning / Beam Management).

[0176] (i) The terminal can inform the network of its capabilities, such as models and functions related to AI / ML, that it supports through UE Capability reporting. The network can provide AI / ML-related settings to the terminal based on the terminal's capabilities related to AI / ML reported by the terminal.

[0177] (ii) AI / ML-related configuration procedures may include data collection related to the training / inference of AI / ML models and / or the provision of configuration information regarding data collection. The configuration information regarding data collection may relate to how to configure the method / operation of data collection.

[0178] (iii) AI / ML-related configuration procedures may include training AI / ML models online or offline and / or providing configuration information for AI / ML model training. The configuration information for AI / ML model training may relate to how to configure the method / behavior, etc., of training the AI / ML model.

[0179] (iv) AI / ML-related configuration procedures may include transmitting / transmitting configuration information for a model. The configuration information for a model may include parameters that constitute the AI / ML model and / or an identifier (ID) for the AI / ML model.

[0180] The provided AI / ML model may be a model trained by the network or a model that requires self-training at the terminal. Even when a model trained by the network is provided, the terminal may perform fine-tuning or retraining as necessary. Meanwhile, if a model trained by the network is provided, the terminal may provide data for training to the network.

[0181] Meanwhile, AI / ML models can be classified into Type A models, which can be identified without OTA (over-the-air) signaling, and Type B models, which are identified through OTA signaling. A model ID may be assigned during the model identification process, and this process can be subdivided into methods initiated by the terminal and methods identified by the network.

[0182] (v) The configuration procedure related to AI / ML may include the configuration of how to select AI / ML Functionality / models and / or the selection process for AI / ML Functionality / models. In UE-side AI / ML models or two-sided AI / ML models, the selection of the UE part may be performed through instructions / signaling from the network or the terminal may select it itself. The selection of AI / ML Functionality / models may be performed when multiple AI / ML Functionality / models are configured / provided.

[0183] (vi) The configuration procedure related to AI / ML may include configuration information for various inference operations performed based on AI / ML models, e.g., AI / ML-based CSI measurement / reporting, AI / ML-based positioning, and / or AI / ML-based beam management.

[0184] (2) Operation based on inference by AI / ML models

[0185] Referring again to FIG. 3, the network and / or terminal can perform inference of the AI / ML model through the trained AI / ML model and perform various operations based on the inference of the AI / ML model (S320). If the AI / ML model is a one-sided model, the inference of the AI / ML model can be performed at either the network or the terminal where the AI / ML model is configured. If the AI / ML model is a two-sided model, each part of the inference of the AI / ML model can be performed at the network and the terminal, and depending on the implementation, such inference can be performed cooperatively between the network and the terminal.

[0186] (i) Actions performed based on the inference of an AI / ML model may include AI / ML-based CSI measurement / reporting. AI / ML-based CSI measurement / reporting is intended to improve CSI feedback and may be related to overhead reduction / CSI compression, accuracy improvement, and / or CSI prediction.

[0187] (ii) Actions performed based on the inference of an AI / ML model may include AI / ML-based beam management. AI / ML-based beam management may be related to beam prediction in the time domain, reduction of overhead / latency in the spatial domain, and / or improvement of beam selection accuracy.

[0188] (iii) Actions performed based on the inference of an AI / ML model may include AI / ML-based positioning. AI / ML-based positioning may be relevant to improving positioning accuracy in various scenarios, for example, in non-line-of-sight environments.

[0189] (3) Procedures for AI / ML management

[0190] The network and / or terminal can perform a procedure for managing AI / ML Functionality / model or settings therefor (S330).

[0191] The network and / or terminal may perform monitoring of AI / ML Functionality / model during the process of AI / ML model inference or operation based thereon (S320) for the management procedure (S330).

[0192] The management procedure may include, for example, at least one of activation / deactivation, switching, model update, and / or fallback operation for AI / ML Functionality / model. For the signaling of the management procedure, various 3GPP signaling schemes, such as RRC, MAC-CE, DCI, etc., may be used.

[0193] As an example of model switching, multiple model groups are configured, and switching between them can be performed based on models having a common model structure or partially common substructures, and models within the same group may be associated with different input / output formats or processing.

[0194] Model updating involves modifying the parameters used by the model to suit channel conditions that change over time, and fine-tuning is an example of model updating.

[0195] Fallback: In a wireless communication system using an AI / ML model, this may refer to the operation of not using the AI / ML model or operating in a pre-configured / defined default mode when the reliability of the AI / ML model decreases due to internal or external environmental factors.

[0196] For example, the decision on whether to perform a management procedure can be made by the network. For instance, the network may decide to perform the management procedure upon network initiation, or the network may decide to perform the management procedure upon terminal initiation and request.

[0197] As another example, the decision on whether to perform a management procedure can be made by the terminal. For instance, the terminal's decision on the management procedure may be triggered when an event condition set by the network is satisfied, performed by reporting the terminal's decision to the network, or performed autonomously by the terminal.

[0198] 3. Specific operation examples based on AI / ML model inference

[0199] CSI prediction and / or compression

[0200] Figure 4 illustrates an example of an AI / ML-based CSI measurement / reporting operation.

[0201] Referring to FIG. 4, the network / terminal can perform a configuration procedure related to AI / ML-based CSI (S410). The network / terminal can perform a configuration procedure for an AI / ML model to be used for AI / ML-based CSI, and for the exchange of configuration information for upper-layer signaling for AI / ML-based CSI measurement / reporting. For example, at least one of information related to model inference, settings for RS / resources to be used for CSI measurement, monitoring performance, data collection, and conditions / resources for CSI reporting may be signaled.

[0202] The terminal can perform CSI measurements based on AI / ML model inference (S420). The AI / ML model used by the terminal for CSI measurements may be a UE-side AI / ML model corresponding to a one-side AI / ML model, or an AI / ML model corresponding to the terminal part of a two-side AI / ML model.

[0203] The terminal may report CSI to the network based on the CSI measurement results (S430). CSI reporting may be performed periodically or non-periodically depending on the configuration, and in the case of non-period CSI reporting, network instructions (not shown), such as DCI, that trigger it may be additionally signaled. CSI reporting may include AI / ML-based CSI content and may additionally include legacy CSI content (e.g., non-AI / ML-based RI, PMI, CQI, etc.) (depending on the configuration / scheduling). AI / ML-based CSI content may be related to at least one of 1) CSI compression to reduce the overhead of CSI reporting and 2) CSI prediction for future time points in the time domain.

[0204] The network can acquire CSI based on the terminal's CSI report.

[0205] If a two-sided AI / ML model is configured, the network can reconstruct the CSI by using the terminal's CSI report as input data to the AI / ML model configured in the network (S440). The inference (output) of the AI / ML model configured in the network may be the reconstructed CSI. In such a two-sided AI / ML model, the terminal-side AI / ML model part is a CSI encoder, and the network-side AI / ML model part can be understood as a concept similar to a CSI decoder.

[0206] CSI compression is CSI compression in the spatial-frequency domain and can primarily be based on two-sided AI / ML models. CSI prediction can primarily be based on one-sided, specifically UE-side AI / ML models.

[0207] In CSI compression based on a two-sided AI / ML model, AI / ML model training may include at least one of the following: (i) Type 1, in which the two-sided AI / ML model is jointly trained at either the terminal or the network; (ii) Type 2, in which the terminal and the network each jointly train the corresponding parts of the two-sided AI / ML model; and (iii) Type 3, in which the terminal and the network each separately train the corresponding parts of the two-sided AI / ML model, wherein the training of the terminal is mainly related to CSI generation and the training of the network is mainly related to CSI reconstruction. Joint training means that the CSI generation / reconstruction models are trained in the same loop for forward / backward delays, and separate training may mean a sequential method in which one of the terminals or the network starts training first, and then the other performs training.

[0208] The symbols / abbreviations / terms used in this specification are as follows.

[0209] - BM: beam management

[0210] - CQI: channel quality indicator

[0211] - CRI: CSI-RS (channel state information - reference signal) resource indicator

[0212] - CSI: channel state information

[0213] - CSI-IM: channel state information - interference measurement

[0214] - CSI-RS: channel state information - reference signal

[0215] - DMRS: demodulation reference signal

[0216] - FDM: frequency division multiplexing

[0217] - FFT: fast Fourier transform

[0218] - IFDMA: interleaved frequency division multiple access

[0219] - IFFT: inverse fast Fourier transform

[0220] - L1-RSRP: Layer 1 reference signal received power

[0221] - L1-RSRQ: Layer 1 reference signal received quality

[0222] - MAC: medium access control

[0223] - NZP: non-zero power

[0224] - OFDM: orthogonal frequency division multiplexing

[0225] - PDCCH: physical downlink control channel

[0226] - PDSCH: physical downlink shared channel

[0227] - PMI: precoding matrix indicator

[0228] - RE: resource element

[0229] - RI: Rank indicator

[0230] - RRC: radio resource control

[0231] - RSSI: received signal strength indicator

[0232] - Rx: Reception

[0233] - QCL: quasi co-location

[0234] - SINR: signal to interference and noise ratio

[0235] - SSB (or SS / PBCH block): synchronization signal block (including primary synchronization signal, secondary synchronization signal and physical broadcast channel)

[0236] - TDM: time division multiplexing

[0237] - TRP: transmission and reception point

[0238] - TRS: tracking reference signal

[0239] - Tx: transmission

[0240] - UE: user equipment

[0241] - ZP: zero power

[0242] 본 문서에서 ' / '는 문맥에 따라 'and', 'or', 혹은 'and / or'를 의미한다.

[0243] Figure 5 is a diagram showing an example of a CSI report based on an AI / ML model.

[0244] AI / ML-based CSI reporting may be considered in this specification. Referring to FIG. 5, the terminal may be equipped with an AI encoder and the base station with an AI decoder to perform AI / ML model inference. A model in which AI / ML model inference is performed at each of the two nodes in this manner is called a two-sided model. The terminal-side model (UE-side model) and the network (or base station)-side model (NW-side model) of FIG. 5 exemplify CSI compression aimed at overhead reduction.

[0245] For example, in a two-sided AI / ML model, AI / ML models are deployed / configured at the terminal and the base station (or network), respectively, so that each model can perform inference.

[0246] In the terminal-side model (the CSI encoder side of FIG. 5), an AI / ML model inference output can be calculated by using i) channel information (e.g., channel matrix / channel covariance matrix / channel eigenvector) as input, or ii) information that has undergone pre-processing of the channel information as input. The terminal can feed back the output information to the base station. At this time, the output information may be information that has undergone a specific post-processing process or information that has not undergone a specific post-processing process.

[0247] The base station side model (the CSI decoder side of FIG. 5) may or may not preprocess the feedback information. The base station may calculate an inference output using the preprocessed or unpreprocessed feedback information as input. The base station may or may not perform a post-processing process on the inference output, and may decode the final CSI based on the output.

[0248] As another use case, the Rel-18 AI / ML study conducted a study on CSI prediction based on a UE-sided model. A use case for CSI prediction is exemplified in Figure 6.

[0249] Figure 6 is a diagram showing an example of CSI prediction based on an AI / ML model.

[0250] Referring to FIG. 6, an AI / ML model is provided on the base station (or network) side, and an example use case of CSI prediction based on a NW-sided model that performs model inference is shown. In this case, past time instances (time instances, t in FIG. 6) -x , t -x+1 Multiple historical measurements for , ..., t0) can be used as AI / ML input, and single or multiple future CSIs (or predicted CSIs) estimated based on this can be output as AI / ML model output. t in Fig. 6 N , t N+1 , ..., t N+K represents future time instances related to the above-mentioned predicted CSIs.

[0251] Figure 7 is a diagram showing an example of CSI prediction for multiple time instances.

[0252] In Release 18 CSI, a codebook was introduced that can predict N4 future time instances by compressing N4 basis vectors into the Doppler domain. Similarly, in AI / ML-based CSI prediction, model inference for N4 time instances can be performed.

[0253] Referring to Fig. 7, prediction is performed on N4 time instances in the prediction window, and inference of an AI / ML model for performing CSI prediction is performed on those instances.

[0254] When inference is performed in an AI / ML model, the performance can be affected by factors such as how well the model was trained or how similar the inference environment is to the training environment. Since it is meaningless to continue performing inference when the AI / ML model's performance has deteriorated, it is important to monitor the model's performance.

[0255]

[0256] To this end, Rel19 discusses performance monitoring by dividing it into three types, as shown in the agreement in Table 4 above. In particular, for Type 2 performance monitoring, the terminal calculates a predicted CSI at a certain point in time using an AI / ML-based model and transmits it to the base station, and separately reports a ground-truth CSI to the base station calculated by measuring the actual channel after a certain period. Based on these two CSI reports, the base station calculates a performance metric and determines whether to continue using the AI / ML model or fallback based on the results.

[0257] In this specification, ground-truth CSI refers to a CSI generated / calculated in the same manner as conventional methods. Alternatively, ground-truth CSI may refer to a CSI not based on prediction.

[0258] The existing legacy codebook-based CSI reporting method involves the terminal estimating the channel matrix (H) using the CSI-RS (Channel State Information Reference Signal) transmitted from the base station, calculating eigenvectors and eigenvalues ​​based on this, and then constructing and reporting appropriate rank indicators (RI) and precoding matrix indicators (PMI). Generally, the terminal obtains eigenvectors and eigenvalues ​​from the channel matrix (H) through preprocessing steps such as Singular Value Decomposition (SVD), determines the PMI and RI based on these, and reports them to the base station. Through this process, the terminal reports predicted CSI and ground-truth CSI separately; however, there is a possibility that the terminal may select different configurations during these separate reporting processes. Consequently, the configurations of the predicted CSI and ground-truth CSI (e.g., rank, PMI, SD / FD basis, etc.) may differ, making accurate performance evaluation difficult.

[0259] For example, under a specific channel condition, the ground-truth CSI (H_true) is calculated as RI=3, consisting of [v1, v2, v3] which includes both v2 and v3 layers with similar eigenvalues; however, the predicted CSI (H_pred) is composed of [v1_pred, v3_pred] due to minute differences in eigenvalues, leading to the conclusion that RI=2 is optimal. In this case, even if the ground-truth CSI is limited to [v1, v2], accurate performance monitoring may be difficult because the values ​​of performance metrics such as SGCS or NMSE become abnormally low due to the discrepancy between v3_pred in the predicted CSI's [v1_pred, v3_pred] and v2 in the ground-truth CSI during performance metric calculation. This problem can occur more frequently when the quality of a layer is determined by the magnitude of its eigenvalues ​​and when layers with similar eigenvalues ​​exist. This results in an environment where the predicted CSI layer and the ground-truth CSI layer can be configured completely differently, and if the base station uses this as is for performance metric calculation, there is a problem in that the base station cannot make accurate performance comparisons and judgments.

[0260] Additionally, Predicted CSI and ground-truth CSI can be reported using different SD / FD bases. SD / FD bases used in standards are generally designed to be nearly orthogonal to each other. That is, the inner product between the basis vectors is very small or close to zero, making them independent of each other. This leads to a problem where the correlation between the two CSIs drops significantly.

[0261] All of the above problems stem from the structural characteristics of Type 2 monitoring, in which the terminal calculates two CSIs independently and the base station calculates the performance metric. Therefore, accurate and reliable performance monitoring cannot be achieved unless the configuration alignment between the predicted CSI and the ground-truth CSI is clearly guaranteed, and consequently, there is a risk of making incorrect fallback decisions. To solve this problem, the present invention proposes a method for aligning the configuration between the predicted CSI and the ground-truth CSI based on the same criteria to accurately perform performance monitoring operations at the base station.

[0262] Proposal 1. Method for a terminal to construct ground-truth CSI based on the most recent inference report in Type 2 monitoring

[0263] The above proposal 1 is intended to solve the problem where it is difficult to calculate the monitoring metric at the base station when the CSI configuration is different when the terminal reports the predicted CSI and ground-truth CSI.

[0264] Generally, the terminal calculates and reports the predicted CSI, which is an inference result based on AI / ML. The base station transmits a monitoring RS for the purpose of performance monitoring to verify whether the AI / ML model is operating properly after a specific period of time has elapsed, and the terminal receives the monitoring RS and calculates / generates and reports the ground-truth CSI based on the measured channel information. In this case, the timing associated with the ground-truth information must generally coincide with the timing represented by the predicted CSI or exist within a certain time interval (or within a certain time gap).

[0265] At this time, since the predicted CSI and ground-truth CSI utilize different resources and / or CSI reporting (configuration) methods, as described above, it may be difficult for the base station to calculate performance metrics based on the reported inference results and ground-truth CSI. To address this, Proposal 1 proposes a method for generating ground-truth CSI (GT CSI) based on parameters / CSI reporting values ​​used in the most recent inference results report. For example, when constructing the GT CSI, the same number of layers (i.e., same rank) and mapping method assumed in the most recent inference report can be considered. Additionally, specific codebook parameter information (SD / FD basis), etc., can be assumed as described in Proposal 1-1 / 1-2 below. In addition, even if codebook subset restriction / RI restriction, etc. are not set in the CSI report config configured to report ground-truth CSI, if the corresponding CBSR / Rank restriction is set in the report config of the most recent inference report, the terminal can calculate the ground-truth CSI by assuming the corresponding CBSR / Rank restriction.

[0266] How to match layer mapping

[0267] For example, the terminal can define UE behavior to report an inference result (e.g., predicted CSI) to the base station, store layer information used by the precoder calculated at that point (e.g., using Rel-18 CSI) (e.g., which layer (or layer direction) and / or mapping order was used to construct the PMI), and subsequently, when reporting a ground-truth CSI, prioritize selecting the layer closest to the layer used in the past predicted CSI (e.g., evaluating the layer closest to the past predicted CSI based on the magnitude of the inner product between layer vectors, cosine similarity, etc.) to construct the PMI and report it to the base station. In other words, the terminal can internally align the layers of the ground-truth CSI based on the layer information of the past predicted CSI without a separate base station instruction signal.

[0268] As a specific example, if a terminal reports a CSI for [Layer1, Layer3] to the base station at time n when reporting a predicted CSI, then at the time n+k when reporting the ground-truth CSI, the terminal internally compares the similarity with the layer of the predicted CSI from the past time n without separate signaling, selects the layer most similar to [Layer1, Layer3] (e.g., the layer with the maximum magnitude of the inner product between layer vectors, cosine similarity, etc.), constructs the ground-truth CSI, and reports it to the base station.

[0269] Proposal 1-1. Method for determining the rank of ground-truth CSI based on RI of the most recent inference report

[0270] The above proposal 1-1 is a method for performing stable performance monitoring while reducing the possibility that the rank and layer of the predicted CSI and ground-truth CSI may differ due to minute changes in the channel environment. In other words, to perform the reporting operation of the terminal's ground-truth CSI without separate NW signaling, the terminal can determine the RI of the GT CSI based on the RI reported in the most recent inference result. For example, if the RI of the GT CSI is determined based on the RI reported in the most recent inference result, the RI reporting for the GT CSI may be omitted.

[0271] As a specific example of Proposal 1-1 above, a case may be considered where the predicted CSI is reported as RI=4, and the quality of Layer 3 and Layer 4 is similar, so the RI of the ground-truth CSI is calculated as 3. In this case, if the terminal reports the CSI for [Layer 1, Layer 2] to the base station excluding layers with similar quality when reporting the predicted CSI at time n, then at the time of reporting the ground-truth CSI (n+k), the terminal may, without separate signaling, internally compare the similarity with the layer of the predicted CSI from the past time n, select the layer most similar to [Layer 1, Layer 3] (e.g., the maximum magnitude of the inner product between layer vectors, cosine similarity, etc.), construct the ground-truth CSI, and report it to the base station. If the predicted CSI is reported as RI=2, and all layers must be excluded because the quality of Layer 1 and Layer 2 is similar, the layer corresponding to LI must be included in the report to construct the CSI. Subsequently, the terminal can configure the ground-truth CSI in the same manner as above and report it to the base station.

[0272] As another example, terminal operation can be restricted so that the terminal must include an LI in both the Inference Report and the GT CSI Report, ensuring that the LI indicates the same layer. In this case, the base station can perform performance monitoring based on the layer corresponding to the indicated LI.

[0273] As another example, the terminal may report the maximum rank that can be set for the GT CSI (e.g., rank restriction) and may separately indicate and transmit information about layers similar in direction to the layers reported in the most recent inference result. For example, if the GT CSI is transmitted at rank 4 (e.g., layers 1, 2, 3, 4) and the inference result (e.g., predicted CSI) is transmitted at rank 2, the relationships between the layers corresponding to rank 2 of the inference result and the layers of the GT CSI can be indicated and transmitted as a 4-bit bitmap, etc. For example, if the above 4-bit bitmap is [1 0 1 0], GT CSI layer 1 / 3 can be considered to correspond to the inference result. Alternatively, if this information is not sent, when calculating the GT CSI, layer 1 / 2 can be implicitly considered to be mapped to the layers of the predicted CSI reported as the inference result. Alternatively, the base station can perform performance monitoring by finding the layer with the maximum match using methods such as exhaustive search.

[0274] Proposal 1-2. Method for a terminal to align the SD / FD basis of ground-truth CSI based on the most recent inference report

[0275] Proposal 1-2 is a method to solve the problem of reduced performance evaluation accuracy caused by reporting predicted CSI and ground-truth CSI on different SD / FD basis.

[0276] For example, the terminal can determine the precoding matrix of the ground-truth CSI based on the precoding matrix of the most recent inference report (e.g., the most recent predicted CSI). Specifically, the terminal can maintain and manage SD / FD basis set information used when reporting the predicted CSI to the base station. Subsequently, when reporting the ground-truth CSI, the terminal can construct the ground-truth CSI by selecting the basis set closest to the SD / FD basis of the previously reported predicted CSI (evaluated based on criteria such as the magnitude of the inner product between basis vectors or cosine similarity) and report it to the base station. In other words, the terminal can maintain consistency in CSI configuration by performing basis alignment internally without a separate base station instruction signal.

[0277] As a specific embodiment of the above method, when a terminal reports a CSI for a basis set consisting of [1st SD, 3rd SD, 2nd FD, 3rd FD] at time n when reporting a predicted CSI, at a time (n+k) when a ground-truth CSI report is requested, the terminal internally calculates the ground-truth CSI by selecting the basis that has the greatest similarity to the basis set of the predicted CSI reported in the past (e.g., the magnitude of the inner product between basis vectors, cosine similarity, etc., is maximized) without separate signaling, and reports it to the base station.

[0278] In the above proposals (e.g., proposals 1-1 to 1-2), where an inference result and a GT CSI are reported simultaneously, the most recent inference result may be the inference result reported simultaneously with the GT CSI.

[0279] According to the above proposal 1-1 / 1-2, there is an advantage in that the accuracy of performance monitoring of AI / ML models can be improved by aligning the predicted CSI and ground-truth CSI without separate signaling and feedback overhead.

[0280] The above proposal 1-1 / 1-2 may be applied alone or in combination.

[0281] Below, we examine the operation procedure of a base station and a terminal to which the method according to at least one embodiment of Proposal 1-1 to Proposal 1-2 described above can be applied.

[0282] 1) Receiver (Entity A, e.g., terminal operation):

[0283] Step 1: A step of reporting capabilities including the maximum number of CSI-RS resources supported by the base station, the number of CSI-RS ports, the total number of CSI-RS ports that can be supported simultaneously, the number of Rx antenna groups, etc.

[0284] Step 2: Receiving CSI-RS transmission, CSI reports, and related configuration information from the base station

[0285] Step 3: A step of receiving CSI-RS from the base station and measuring / predicting / calculating CSI based on it.

[0286] Step 3-1: A step of performing CSI omission based on configuration information and CSI priority from the base station.

[0287] Step 4: Reporting the above measured / predicted / calculated CSI to the base station

[0288] Step 5: The step of receiving scheduling for downlink channels (e.g., PDCCH, PDSCH) from the base station.

[0289] Step 6: Receiving the downlink channel / signal transmitted by the base station

[0290] 2) Transmitter (Entity B, e.g., base station operation):

[0291] Step 1: A step of receiving a capability report from the terminal including the maximum number of supported CSI-RS resources, the number of CSI-RS ports, the total number of simultaneously supported CSI-RS ports, the number of Rx antenna groups, etc.

[0292] Step 2: Transmitting CSI-RS to the terminal and sending CSI reports and related configuration information.

[0293] Step 3: Step of transmitting CSI-RS to the terminal

[0294] Step 4: The step where the terminal receives the measured / predicted / calculated CSI from the terminal.

[0295] Step 5: A step of predicting CSI using an AI / ML model based on CSI reported from the terminal, scheduling downlink channels (e.g., PDCCH, PDSCH), and transmitting this to the terminal.

[0296] In the above terminal / base station operation, (some) specific steps may be omitted.

[0297] In terms of implementation, the operations of the base station / terminal according to the embodiments described above (e.g., operations based on at least one of proposals 1-1 to 1-2) can be processed by the device of FIG. 10 (e.g., the processor (110, 210) of FIG. 10).

[0298] In addition, the operations of the base station / terminal according to the above-described embodiment (e.g., operations based on at least one of proposals 1-1 to 1-2) may be stored in memory (e.g., 140, 240 of FIG. 10) in the form of instructions / programs (e.g., instruction, executable code) for driving at least one processor (e.g., 110, 210 of FIG. 10).

[0299] The embodiments described above will be explained in detail below with reference to FIGS. 8 and FIG. 9 regarding the operation of the terminal and base station. The methods described below are distinguished only for convenience of explanation, and it is understood that a part of one method may be substituted with a part of another method or combined with one another and applied.

[0300] FIG. 8 is a flowchart illustrating a method according to one embodiment of the present specification.

[0301] Referring to FIG. 8, a method according to one embodiment of the present specification includes i) a setting information receiving step (S810) and a CSI reporting step (S830).

[0302] In step S810, the terminal receives configuration information related to channel state information (CSI) from the base station.

[0303] For example, the above configuration information may include information based on at least one of the above-described CSI-related operations and proposals 1-1 to 1-2. As a specific example, the above configuration information may include at least one of one or more resource configurations (e.g., M≥1 CSI-ResourceConfig resource setting) and / or one or more reporting configurations (e.g., N≥1 CSI-ReportConfig reporting setting). Each of the one or more reporting configurations may be associated with up to three resource configurations. In other words, each reporting configuration may include the IDs (e.g., CSI-ResourceConfigId) of up to three resource configurations.

[0304] For example, the above one or more reporting settings may include i) a first reporting setting related to performance monitoring and ii) a second reporting setting related to CSI prediction. As a specific example, the setting information may include i) a first reporting setting related to performance monitoring and ii) a second reporting setting related to CSI prediction.

[0305] In step S830, the terminal reports the CSI to the base station.

[0306] The above CSI includes a first CSI related to performance monitoring. The above first CSI may be configured / set / reported based on at least one of the aforementioned proposals 1-1 to 1-2. This will be explained in detail below.

[0307] For example, the first CSI above may be related to the first reporting setting above.

[0308] The rank of the first CSI is determined based on the rank indicator (RI) of the most recent second CSI among one or more CSIs related to the prediction.

[0309] For example, the above CSI may include one or more CSIs related to prediction. In this case, the second CSI may be related to the second reporting setting.

[0310] For example, the above prediction may be related to a terminal-side model (UE-side model).

[0311] For example, the result of the above performance monitoring may be based on a performance metric. In this case, the performance metric may be calculated by the base station. The performance monitoring and the performance metric may be performed / calculated based on the Type 2 performance monitoring described above.

[0312] According to one embodiment, the RI of the first CSI may be the same as the RI of the second CSI. This embodiment may be based on Proposal 1 described above.

[0313] According to one embodiment, one or more layers of the first CSI may be determined based on one or more layers of the second CSI.

[0314] For example, one or more layers of the first CSI may include a layer corresponding to a layer indicator (LI) of the second CSI.

[0315] For example, the above performance monitoring can be performed based on a layer corresponding to the LI.

[0316] This embodiment may be based on the above-described proposal 1-1.

[0317] According to one embodiment, the precoding matrix of the first CSI can be determined based on the spatial-domain vector and / or frequency-domain coefficient of the precoding matrix of the second CSI.

[0318] For example, the first CSI may be constructed based on the basis set closest to the basis set used in the report of the second CSI. As a specific example, the first CSI may be constructed based on the spatial-domain basis and / or frequency-domain basis of the second CSI that has the largest cosine similarity and i) the magnitude of the inner product between the basis vectors and / or ii) the cosine similarity.

[0319] This embodiment may be based on the above-described proposal 1-2.

[0320] Operations based on S810 to S830 described above can be implemented by the device of FIG. 10. For example, referring to FIG. 10, a terminal (200) can control one or more transceivers (230) and / or one or more memories (240) to perform operations based on S810 to S830.

[0321] The embodiments described above will be explained in detail below in terms of base station operation.

[0322] S910 to S930 described below correspond to S810 to S830 described in FIG. 8. Considering the above correspondence, redundant descriptions are omitted. That is, the specific description of the base station operation described below can be replaced by the description / embodiment of FIG. 8 corresponding to the operation.

[0323] FIG. 9 is a flowchart illustrating a method according to another embodiment of the present specification.

[0324] Referring to FIG. 9, a method according to another embodiment of the present specification includes i) a setting information transmission step (S910) and ii) a CSI reception step (S930).

[0325] In S910, the base station transmits configuration information related to channel state information (CSI) to the terminal (user equipment, UE).

[0326] In S930, the base station receives the CSI from the terminal.

[0327] The above CSI includes a first CSI related to performance monitoring.

[0328] The rank of the first CSI is determined based on the rank indicator (RI) of the most recent second CSI among one or more CSIs related to the prediction.

[0329] Operations based on the above-described S910 to S930 can be implemented by the device of FIG. 10. For example, referring to FIG. 10, a base station (100) can control one or more transceivers (130) and / or one or more memories (140) to perform operations based on S910 to S930.

[0330] The operations / terms based on the embodiments described above are described under the assumption of an existing system (e.g., a 5G system). However, this is for the convenience of explanation and is not intended to limit the scope of application of the technical problems and means for solving problems that are to be solved by this specification to a specific system. That is, the technical problems / technical issues / problems mentioned in this specification may exist in other systems (e.g., a 6G system). It is evident that the embodiments of this specification can be extended to solve problems that exist in other systems as well. Therefore, for the extended application of the embodiments of this specification to other systems, terms defined / described based on a 5G system may be replaced / changed with terms defined in other systems (or generalized terms not specific to one system). For example, PRACH, PUSCH, PUCCH, or SRS may be replaced / changed to uplink signals (or uplink channels). For example, SSB, CSI-RS, PDSCH, and PDCCH may be replaced / changed to downlink signals (or downlink channels).

[0331] Hereinafter, an apparatus to which the embodiments of the present specification can be applied (an apparatus implementing the method / operation according to the embodiments of the present specification) will be described with reference to FIG. 10.

[0332] FIG. 10 is a drawing showing the configuration of a first device and a second device according to an embodiment of the present specification.

[0333] The first device (100) may include a processor (110), an antenna unit (120), a transceiver (130), and a memory (140).

[0334] The processor (110) performs baseband-related signal processing and may include an upper layer processing unit (111) and a physical layer processing unit (115). The upper layer processing unit (111) may process operations of the MAC layer, RRC layer, or higher upper layers. The physical layer processing unit (115) may process operations of the PHY layer. For example, if the first device (100) is a base station device in base station-terminal communication, the physical layer processing unit (115) may perform uplink reception signal processing, downlink transmission signal processing, etc. For example, if the first device (100) is a first terminal device in terminal-terminal communication, the physical layer processing unit (115) may perform downlink reception signal processing, uplink transmission signal processing, sidelink transmission signal processing, etc. In addition to performing baseband-related signal processing, the processor (110) may also control the overall operation of the first device (100).

[0335] The antenna section (120) may include one or more physical antennas, and if it includes multiple antennas, it may support MIMO transmission and reception. The transceiver (130) may include an RF (Radio Frequency) transmitter and an RF receiver. The memory (140) may store information processed by the processor (110) and software, operating systems, applications, etc. related to the operation of the first device (100), and may include components such as a buffer.

[0336] The processor (110) of the first device (100) may be configured to implement the operation of the base station in base station-terminal communication (or the operation of the first terminal device in terminal-terminal communication) in the embodiments described in this disclosure.

[0337] The second device (200) may include a processor (210), an antenna unit (220), a transceiver (230), and a memory (240).

[0338] The processor (210) performs baseband-related signal processing and may include an upper layer processing unit (211) and a physical layer processing unit (215). The upper layer processing unit (211) may process operations of the MAC layer, RRC layer, or higher upper layers. The physical layer processing unit (215) may process operations of the PHY layer. For example, if the second device (200) is a terminal device in base station-terminal communication, the physical layer processing unit (215) may perform downlink reception signal processing, uplink transmission signal processing, etc. For example, if the second device (200) is a second terminal device in terminal-terminal communication, the physical layer processing unit (215) may perform downlink reception signal processing, uplink transmission signal processing, sidelink reception signal processing, etc. In addition to performing baseband-related signal processing, the processor (210) may also control the overall operation of the second device (210).

[0339] The antenna section (220) may include one or more physical antennas, and may support MIMO transmission and reception if it includes multiple antennas. The transceiver (230) may include an RF transmitter and an RF receiver. The memory (240) may store information processed by the processor (210) and software, operating systems, applications, etc. related to the operation of the second device (200), and may include components such as a buffer.

[0340] The processor (210) of the second device (200) may be configured to implement the operation of the terminal in base station-terminal communication (or the operation of the second terminal device in terminal-terminal communication) in the embodiments described in this disclosure.

[0341] In the operation of the first device (100) and the second device (200), the details described in the examples of the present disclosure regarding the base station and terminal (or the first terminal and the second terminal in terminal-to-terminal communication) in base station-to-terminal communication may be applied in the same way, and redundant descriptions are omitted.

[0342] Here, the wireless communication technology implemented in the device of the present disclosure may include LTE, NR, and 6G, as well as Narrowband Internet of Things (NB-IoT) for low-power communication. For example, NB-IoT technology may be an example of Low Power Wide Area Network (LPWAN) 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.

[0343] Additionally or alternatively, the wireless communication technology implemented in the device of the present disclosure 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.

[0344] Additionally or generally, the wireless communication technology implemented in the device of the present disclosure may include at least one of ZigBee, Bluetooth, and a Low Power Wide Area Network (LPWAN) for low-power communication, but is not limited to the names mentioned above. For 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.

Claims

1. A method performed by a terminal (user equipment, UE), A step of receiving configuration information related to channel state information (CSI) from a base station; and A step of reporting CSI to the above base station; including, The above CSI includes a first CSI related to performance monitoring, and A method characterized in that the rank of the first CSI is determined based on the rank indicator (RI) of the most recent second CSI among one or more CSIs related to the prediction.

2. In Paragraph 1, A method characterized in that the above prediction is related to a terminal-side model (UE-side model).

3. In Paragraph 1, The results of the above performance monitoring are based on performance metrics, and A method characterized by the above performance metric being calculated by the base station.

4. In Paragraph 1, A method characterized in that the RI of the first CSI is the same as the RI of the second CSI.

5. In Paragraph 1, A method characterized in that one or more layers of the first CSI are determined based on one or more layers of the second CSI.

6. In Paragraph 1, A method characterized in that one or more layers of the first CSI include a layer corresponding to a layer indicator (LI) of the second CSI.

7. In Paragraph 6, A method characterized in that the above performance monitoring is performed based on a layer corresponding to the LI.

8. In Paragraph 1, A method characterized in that the precoding matrix of the first CSI is determined based on the spatial-domain vector and / or frequency-domain coefficient of the precoding matrix of the second CSI.

9. In a terminal (user equipment, UE), One or more transmitters / receivers; One or more processors; and It includes one or more memories connected to the above one or more processors and storing instructions, A terminal characterized by the above instructions, based on execution by the one or more processors, such that the terminal performs all steps of the method according to any one of claims 1 to 8.

10. A device comprising one or more memories and one or more processors connected to the one or more memories, A device characterized in that the one or more of the above memories store instructions that cause the device to perform all steps of the method according to any one of claims 1 to 8, based on execution by the one or more processors.

11. In a non-transitory computer-readable storage medium for storing instructions, A non-transient computer-readable storage medium characterized in that the instructions executable by one or more processors cause the terminal to perform all steps of the method according to any one of claims 1 to 8.

12. In a method performed by a base station, A step of transmitting configuration information related to channel state information (CSI) to a terminal (user equipment, UE); and The step of receiving CSI from the above terminal; comprising, The above CSI includes a first CSI related to performance monitoring, and A method characterized in that the rank of the first CSI is determined based on the rank indicator (RI) of the most recent second CSI among one or more CSIs related to the prediction.

13. Regarding base stations, One or more transmitters / receivers; One or more processors; and It includes one or more memories connected to the above one or more processors and storing instructions, A method characterized by the above instructions, based on execution by the one or more processors, having the base station perform all steps of the method according to claim 12.

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