Method and apparatus for reporting channel state information

Abstract

A method according to one embodiment of the present specification comprises the steps of: receiving a channel state information-reference signal (CSI-RS); and reporting CSI. The CSI includes i) first information related to channel prediction, and ii) second information related to interference prediction.

Description

Method and device for reporting channel status information

[0001] This specification relates to a method and device for reporting channel state information.

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

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

[0004] Meanwhile, the reporting behavior of channel state information is defined. For example, CSI may be reported based on a Channel State Information-Reference Signal (CSI-RS). The CSI may include a Channel Quality Indicator (CQI).

[0005] According to the channel state information prediction operation, CSI can be predicted based on one or more past CSI (historical CSI).

[0006] In conventional CSI-related prediction operations, a model (a single model) is used for channel prediction. If CSI reporting is performed based solely on channel prediction, the accuracy of CSI-based operations may be reduced.

[0007] For example, a CQI determined based only on predicted channel information without considering interference may not be the most appropriate CQI for channel conditions including interference.

[0008] For example, if the reported CQI is different from the previously reported CQI, it is difficult for the base station to clearly determine whether the change is due to a change in channel power or interference power.

[0009] The purpose of this specification is to propose a method to solve the above-mentioned problems.

[0010] The technical problems to be achieved in this specification are not limited to the technical problems mentioned above, and other technical problems not mentioned can be clearly understood by a person having ordinary skill in the technical field to which the present invention belongs from the description below.

[0011] A method according to one embodiment of the present disclosure includes receiving a channel state information-reference signal (CSI-RS) and reporting the channel state information (CSI).

[0012] The above CSI includes i) first information related to channel prediction and ii) second information related to interference prediction.

[0013] The above CSI may include a channel quality indicator (CQI). The CQI may be determined based on the first information and the second information.

[0014] The above second information can be predicted based on historical interference measurements.

[0015] The above past interference measurement may be based on one or more past interference measurements within a time interval.

[0016] The above second information can be predicted based on past CQI and / or past signal-to-interference noise ratio (SINR).

[0017] The method may further include the step of transmitting information related to interference and information related to a raw channel.

[0018] The method may further include the steps of transmitting i) a signal-to-noise ratio (SNR) or reference signal received power (RSRP) and ii) an interference-to-noise ratio (INR).

[0019] The method may further include a step of receiving configuration information related to the CSI. The configuration information may include information related to reporting of the first information and / or the second information.

[0020] Based on the above setting information, the CSI may include the first information and / or the second information.

[0021] The above CSI may include a channel quality indicator (CQI). The CQI may be determined based on the first information and / or the second information.

[0022] The above CSI may include a precoding matrix indicator (PMI). The PMI may be determined based on the first information and / or the second information.

[0023] The above PMI may be determined based on a channel condition. The channel condition may be predicted based on the first information and / or the second information.

[0024] The above CSI may include a channel quality indicator (CQI). The CQI may be determined based on the PMI and the second information.

[0025] The second information may be determined based on the most recently measured Interference-to-Noise Ratio (INR).

[0026] The second information may be determined based on an average of one or more interference measurements within a time interval.

[0027] The above first information and the above second information may be related to the first model and the second model.

[0028] The above past interference measurements may be related to the input of the second model. The second information may be related to the output of the second model.

[0029] The above first and second models may include a user equipment-sided model.

[0030] A terminal according to another embodiment of the present disclosure includes one or more transceivers, one or more processors, and one or more memories connected to the one or more processors and storing instructions.

[0031] The above instructions are characterized in that they cause the terminal to perform all steps of any one of the above methods based on being executed by the one or more processors.

[0032] A device according to another embodiment of the present disclosure comprises one or more memories and one or more processors connected to the one or more memories. The one or more memories are characterized in that they store instructions that, when executed by the one or more processors, cause the device to perform all steps of any one of the above methods.

[0033] A non-transitory computer-readable medium according to another embodiment of the present disclosure stores instructions, the instructions being executable by one or more processors, characterized in that they cause a terminal to perform all steps of any one of the above methods.

[0034] A method according to another embodiment of the present disclosure includes the steps of transmitting a channel state information-reference signal (CSI-RS) and receiving channel state information (CSI).

[0035] The above CSI includes i) first information related to channel prediction and ii) second information related to interference prediction.

[0036] The method may further include a step of transmitting configuration information related to the CSIk. The configuration information may include information related to reporting of the first information and / or the second information.

[0037] Based on the above setting information, the CSI may include the first information and / or the second information.

[0038] The above CSI may include a Channel Quality Indicator (CQI). The CQI may be determined based on the first information and / or the second information.

[0039] A base station according to another embodiment of the present disclosure includes one or more transceivers, one or more processors, and one or more memories coupled to the one or more processors and storing instructions.

[0040] The above instructions are characterized in that they cause the base station to perform all steps of the method based on being executed by the one or more processors.

[0041] According to embodiments of the present disclosure, the accuracy of channel state information (CSI)-based operations can be prevented from being degraded by predicting CSI based on interference-related information. Therefore, when a base station performs downlink scheduling based on reported CSI, interference information can be taken into account when scheduling the downlink.

[0042] According to an embodiment of the present specification, the CSI includes a channel quality indicator. The CQI is determined based on interference-related information. Accordingly, the base station can schedule downlink transmissions by considering the CQI that takes interference information into account.

[0043] As described above, according to the embodiment of the present specification, the base station can perform downlink scheduling most suitable for the current channel by considering interference information, so that the reliability of the downlink transmission and reception procedure can be improved.

[0044] Figure 1 is a flowchart showing an example of a CSI-related procedure.

[0045] Figure 2 illustrates the functional framework of the AI / ML model.

[0046] Figure 3 shows an example of CSI prediction based on an AI / ML model.

[0047] FIG. 4 is a flowchart illustrating an example of signaling based on a method according to at least one embodiment of the present specification.

[0048] FIG. 5 is a flowchart illustrating a method according to one embodiment of the present specification.

[0049] FIG. 6 is a flowchart illustrating a method according to another embodiment of the present specification.

[0050] FIG. 7 is a diagram showing the configuration of a first device and a second device according to one embodiment of the present specification.

[0051] Figure 8 illustrates an AI device according to an embodiment of the present specification.

[0052] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. The detailed description set forth below, together with the accompanying drawings, is intended to illustrate exemplary embodiments of the present invention and is not intended to represent the only embodiments in which the present invention may be practiced. The following detailed description includes specific details to provide a thorough understanding of the present invention.

[0053] In some cases, to avoid obscuring the concept of the present invention, well-known structures and devices may be omitted or illustrated in block diagram form focusing on the core functions of each structure and device.

[0054] Hereinafter, downlink (DL) refers to communication from a base station to a terminal, and uplink (UL) refers to communication from a terminal to a base station. In downlink, a transmitter may be part of a base station, and a receiver may be part of a terminal. In uplink, a transmitter may be part of a terminal, and a receiver may be part of a base station. A base station may be expressed as a first communication device, and a terminal may be expressed as a second communication device. A base station (BS) may be replaced by terms such as a fixed station, Node B, eNB (evolved-NodeB), gNB (Next Generation NodeB), BTS (base transceiver system), access point (AP: Access Point), network (5G network), AI 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, etc.

[0055] < CSI-related actions >

[0056] In NR (New Radio) systems, CSI-RS (channel state information-reference signal) is used for time / frequency tracking, CSI computation, L1 (layer 1)-RSRP (reference signal received power) computation, and mobility. Here, CSI computation is related to CSI acquisition, and L1-RSRP computation is related to beam management (BM).

[0057] CSI (channel state information) is a general term for information that can indicate the quality of the wireless channel (or link) formed between the terminal and the antenna port.

[0058] The base station can transmit CSI-RS to the terminal to determine the characteristics of the downlink channel, and receive feedback on channel measurement results based on the CSI-RS from the terminal.

[0059] A CSI-RS can 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 to multiple terminals. A CSI-RS can support up to 32 antenna ports. CSI-RSs corresponding to N (N is 1 or more) antenna ports can be mapped to N RE positions within a time-frequency unit corresponding to one slot and one RB. When N is 2 or more, the N-port CSI-RSs can be multiplexed by CDM, FDM, and / or TDM. One CDM group can include two antenna ports (CDM2) distinguished based on code resources on the same two adjacent subcarriers, four antenna ports (CDM4) distinguished based on code resources on the same two adjacent subcarriers and the same two adjacent OFDM slots, or eight antenna ports (CDM8) distinguished based on code resources on the same two adjacent subcarriers and the same four adjacent OFDM slots. When multiple CDM groups exist, the CDM groups may not be mapped to adjacent subcarriers and / or adjacent OFDM symbols. CSI-RS antenna ports may be indexed in the order of CDM group, frequency domain, and time domain. CSI-RS may be mapped to REs other than REs to which CORESET, DMRS, and SSB are mapped.

[0060] 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 can be transmitted on each RB within the configured bandwidth (i.e., density = 1), or on every second RB (e.g., 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 can also be mapped on three subcarriers in each resource block (i.e., density = 3).

[0061] One or more CSI-RS resource sets may be configured for a terminal in the time domain. Each CSI-RS resource set may include one or more CSI-RS configurations.

[0062] Each CSI-RS resource set can be configured as periodic, semi-persistent, or aperiodic. For a periodic CSI-RS resource set, the period can be configured as a number of slots greater than or equal to 4 and less than or equal to 640. In addition, a start offset value of the periodic CSI-RS resource set can be configured. For a semi-persistent CSI-RS resource set, an offset and a period for a CSI-RS resource set candidate 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 can be performed according to the configured offset and period until deactivated. When a CSI-RS resource set is deactivated, CSI-RS transmission may not be performed until it is explicitly reactivated. For aperiodic CSI-RS transmission, information about each CSI-RS resource set can be explicitly provided by DCI.

[0063] A CSI-IM resource may be configured for interference measurement (IM) of a terminal. A CSI-IM resource may include four resource elements (REs) within one slot and one resource block. The four REs may correspond to two consecutive OFDM symbols and two consecutive subcarriers, or one OFDM symbol and four consecutive subcarriers. In the frequency domain, the CSI-IM RE positions may be determined by the CSI-IM configuration. In the time domain, a CSI-IM resource set may be configured periodically, semi-persistently, or aperiodically, similar to a CSI resource set. Typically, in a CSI-IM resource, transmission may not be performed in the corresponding cell, but may be performed in a neighboring cell. In this way, the CSI-IM resource may be configured as a zero power (ZP)-CSI-RS for the terminal.

[0064] ZP-CSI-RS can be configured to be distinct from Non-Zero Power (NZP)-CSI-RS. When a PDSCH is scheduled on a resource including a CSI-RS RE, the first terminal can assume that rate matching considering the CSI-RS RE is applied to the corresponding PDSCH, and that the PDSCH is not mapped to the CSI-RS RE. Here, the CSI-RS may be configured for the first terminal or may be configured for the second terminal. In this case, the CSI-RS for the first terminal can be configured as an NZP-CSI-RS for the first terminal, and an NZP-CSI-RS resource set can be configured for the first terminal. Meanwhile, the CSI-RS for the second terminal can be configured as a ZP-CSI-RS for the first terminal, and a ZP-CSI-RS resource set can be configured for the first terminal. The NZP-CSI-RS resource set can be used for the CSI reporting configuration of the corresponding terminal. The NZP-CSI-RS resource set can also be associated with CSI-RS or SSB. Additionally, multiple periodic NZP-CSI-RS resource sets can be configured as TRS resource sets.

[0065] Figure 1 is a flowchart showing an example of a CSI-related procedure.

[0066] A terminal (e.g., user equipment, UE) receives configuration information related to CSI from a base station (e.g., general Node B, gNB) through RRC (radio resource control) signaling (S110).

[0067] The configuration information related to the above CSI may include at least one of CSI-IM (interference management) resource related information, CSI measurement configuration related information, CSI resource configuration related information, CSI-RS resource related information, or CSI report configuration related information.

[0068] i) CSI-IM resource-related information may include CSI-IM resource information, CSI-IM resource set information, etc. A CSI-IM resource set is identified by a CSI-IM resource set ID (identifier), and one resource set includes at least one CSI-IM resource. Each CSI-IM resource is identified by a CSI-IM resource ID.

[0069] ii) CSI resource configuration related information can be expressed as CSI-ResourceConfig IE. The CSI resource configuration related information defines a group including at least one of a non-zero power (NZP) CSI-RS resource set, a CSI-IM resource set, or a CSI-SSB resource set. That is, the CSI resource configuration related information includes a CSI-RS resource set list, and the CSI-RS resource set list can include at least one of an NZP CSI-RS resource set list, a CSI-IM resource set list, or a CSI-SSB resource set list. A CSI-RS resource set is identified by a CSI-RS resource set ID, and one resource set includes at least one CSI-RS resource. Each CSI-RS resource is identified by a CSI-RS resource ID.

[0070] Table 1 shows an example of an NZP CSI-RS resource set IE. As shown in Table 1, parameters indicating the purpose of CSI-RS (e.g., BM-related 'repetition' parameter, tracking-related 'trs-Info' parameter) can be set for each NZP CSI-RS resource set.

[0071]

[0072] And, the repetition parameter corresponding to the higher layer parameter corresponds to the 'CSI-RS-ResourceRep' of the L1 parameter.

[0073] iii) Information related to the CSI report configuration includes a report configuration type parameter (reportConfigType) indicating time domain behavior and a report quantity parameter (reportQuantity) indicating the CSI-related quantity to be reported. The time domain behavior may be periodic, aperiodic, or semi-persistent.

[0074] Information related to CSI report configuration can be expressed in CSI-ReportConfig IE, and Table 2 below shows an example of CSI-ReportConfig IE.

[0075]

[0076] - The terminal measures CSI based on configuration information related to the above CSI (S120).

[0077] The above CSI measurement may include (1) a process of receiving a CSI-RS of a terminal (S121) and (2) a process of calculating (computing) CSI using the received CSI-RS (S122), which will be described in detail later.

[0078] CSI-RS sets the RE (resource element) mapping of CSI-RS resources in the time and frequency domains by the higher layer parameter CSI-RS-ResourceMapping.

[0079] Table 3 shows an example of the CSI-RS-ResourceMapping IE.

[0080]

[0081] 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.

[0082] - The terminal reports the measured CSI to the base station (S130).

[0083] Here, if the quantity of CSI-ReportConfig in Table E is set to 'none (or No report)', the terminal may omit the report.

[0084] However, even if the above quantity is set to 'none (or No report)', the terminal may report to the base station.

[0085] When the above quantity is set to 'none', it triggers an aperiodic TRS or repetition is set.

[0086] Here, the report of the terminal can be omitted only when repetition is set to 'ON'.

[0087] CSI measurement

[0088] The NR system supports more flexible and dynamic CSI measurement and reporting. Here, the CSI measurement may include a procedure for receiving a CSI-RS and computing the received CSI-RS to acquire CSI.

[0089] As a time-domain behavior for CSI measurement and reporting, aperiodic / semi-persistent / periodic channel measurement (CM) and interference measurement (IM) are supported. A 4-port NZP CSI-RS RE pattern is used to configure CSI-IM.

[0090] NR's CSI-IM-based IMR has a similar design to LTE's CSI-IM and is configured independently of the ZP CSI-RS resources for PDSCH rate matching. Furthermore, in the NZP CSI-RS-based IMR, each port emulates an interference layer with (preferred channel and) precoded NZP CSI-RS. This is for intra-cell interference measurement in multi-user cases, primarily targeting MU interference.

[0091] The base station transmits precoded NZP CSI-RS to the terminal on each port of the configured NZP CSI-RS-based IMR.

[0092] The terminal assumes a channel / interference layer for each port in the resource set and measures interference.

[0093] For a channel, if there is no PMI and RI feedback, multiple resources are configured in a set, and the base station or network indicates a subset of NZP CSI-RS resources via DCI for channel / interference measurement.

[0094] Let's take a closer look at resource settings and resource setting configuration.

[0095] Resource setting

[0096] Each CSI resource setting 'CSI-ResourceConfig' contains a configuration for S≥1 CSI resource sets (given by the higher layer parameter csi-RS-ResourceSetList). A CSI resource setting corresponds to a CSI-RS-resourcesetlist, where S represents the number of configured CSI-RS resource sets. Wherein, the list of S≥1 CSI resource sets contains either or both of NZP CSI-RS resource set(s) and SS / PBCH block (SSB) set(s) used for L1-RSRP computation, or contains CSI-IM resource set(s).

[0097] Each CSI resource setting is located in a DL BWP (bandwidth part) identified by the higher layer parameter BWP-id. All CSI resource settings linked to a CSI reporting setting have the same DL BWP.

[0098] The time domain behavior of CSI-RS resources within a CSI resource setting included in the CSI-ResourceConfig IE is indicated by the higher layer parameter resourceType, and can be set to aperiodic, periodic, or semi-persistent. For periodic and semi-persistent CSI resource settings, the number of configured CSI-RS resource sets (S) is limited to '1'. For periodic and semi-persistent CSI resource settings, the configured periodicity and slot offset are given in the numerology of the associated DL BWP, as given by the BWP-id.

[0099] 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.

[0100] When a UE is configured with multiple CSI-ResourceConfigs containing the same CSI-IM resource ID, the same time domain behavior is configured for multiple CSI-ResourceConfigs.

[0101] One or more CSI resource settings for channel measurement (CM) and interference measurement (IM) are configured via higher layer signaling.

[0102] - CSI-IM resource for interference measurement.

[0103] - NZP CSI-RS resources for interference measurement.

[0104] - NZP CSI-RS resources for channel measurement.

[0105] That is, the CMR (channel measurement resource) can be NZP CSI-RS for CSI acquisition, and the IMR (Interference measurement resource) can be NZP CSI-RS for CSI-IM and IM.

[0106] Here, CSI-IM (or ZP CSI-RS for IM) is mainly used for inter-cell interference measurement.

[0107] And, NZP CSI-RS for IM is mainly used for intra-cell interference measurement from multi-user.

[0108] A UE may assume that the CSI-RS resource(s) configured for channel measurement for one CSI reporting and the CSI-IM / NZP CSI-RS resource(s) for interference measurement (when NZP CSI-RS resource(s) are used for interference measurement) are in a QCL relationship with respect to 'QCL-TypeD' per resource.

[0109] Resource setting configuration

[0110] As we have seen, resource setting can mean a resource set list.

[0111] 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 or semi-persistent or aperiodic resource setting.

[0112] One reporting setting can be linked to up to three resource settings.

[0113] - 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.

[0114] - 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.

[0115] - 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.

[0116] For semi-persistent or periodic CSI, each CSI-ReportConfig is linked to a periodic or semi-persistent resource setting(s).

[0117] - When one resource setting (given by resourcesForChannelMeasurement) is set, the resource setting is for channel measurement for L1-RSRP computation or channel and interference measurement for L1-SINR computation.

[0118] - When two resource settings are set, 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.

[0119] CSI computation

[0120] When interference measurements are performed on CSI-IM, each CSI-RS resource for channel measurements is associated with a CSI-IM resource in the order of the CSI-RS resources and CSI-IM resources within the corresponding resource set. The number of CSI-RS resources for channel measurements is equal to the number of CSI-IM resources.

[0121] And, if interference measurement is performed on NZP CSI-RS, the UE does not expect to be configured with more than one NZP CSI-RS resource in the associated resource set within the resource setting for channel measurement.

[0122] A terminal with the higher layer parameter nzp-CSI-RS-ResourcesForInterference set does not expect more than 18 NZP CSI-RS ports to be set within a single NZP CSI-RS resource set.

[0123] For CSI measurement(s) other than L1-SINR, the terminal assumes the following:

[0124] - Each NZP CSI-RS port configured for interference measurement corresponds to an interference transport layer.

[0125] - All interference transmission layers of the NZP CSI-RS port for interference measurement consider the associated EPRE (energy per resource element) ratio.

[0126] - Other interference signals on RE(s) of NZP CSI-RS resource for channel measurement, NZP CSI-RS resource for interference measurement or CSI-IM resource for interference measurement.

[0127] CSI report

[0128] A terminal can measure 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 can be provided for the terminal. Each CSI report configuration can include settings for feedback type, measurement resources, and report type.

[0129] Feedback types may include a Channel Quality Indicator (CQI), a Precoding Matrix Indicator (PMI), a CSI-RS Resource Indicator (CRI), an SSB Resource block Indicator (SSBRI), a Layer Indicator (LI), a Rank Indicator (RI), and a Layer 1-Reference Signal Received Strength (RSRP).

[0130] Measurement resources may include configurations for downlink signals and / or downlink resources on which a terminal will perform measurements to determine feedback information. Measurement resources may be configured as ZP and / or NZP CSI-RS resource sets associated with CSI reporting configurations. The NZP CSI-RS resource set may include a CSI-RS set or an SSB set. For example, L1-RSRP may be measured for a CSI-RS set or an SSB set.

[0131] The report type may include settings for the time at which the terminal performs the report and the uplink channel. The report time may be set to be periodic, semi-persistent, or aperiodic. Periodic CSI reports may be transmitted on the PUCCH. Semi-persistent CSI reports may be transmitted on the PUCCH or PUSCH based on a MAC CE indicating activation / deactivation. Aperiodic CSI reports may be indicated by DCI signaling. For example, the CSI request field of the uplink grant may indicate one of various report trigger sizes. Aperiodic CSI reports may be transmitted on the PUSCH.

[0132] CSI can be defined in two types. Type 1 CSI may be related to a case where a single user is scheduled, and Type 2 CSI may be related to a case 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 a single precoding matrix selected by the UE, Type 2 CSI may report information on the size and phase of up to four beams.

[0133] For CSI reporting, the time and frequency resources available to the UE are controlled by the base station.

[0134] CSI (channel state information) may include at least one of a channel quality indicator (CQI), a precoding matrix indicator (PMI), a CSI-RS resource indicator (CRI), an SS / PBCH block resource indicator (SSBRI), a layer indicator (LI), a rank indicator (RI), L1-RSRP, and / or L1-SINR.

[0135] For CQI, PMI, CRI, SSBRI, LI, RI, and L1-RSRP, the UE is configured by a higher layer with N≥1 CSI-ReportConfig reporting settings, M≥1 CSI-ResourceConfig resource settings, and a list of one or two trigger states (provided by CSI-AperiodicTriggerStateList and CSI-SemiPersistentOnPUSCH-TriggerStateList). Each trigger state in the CSI-AperiodicTriggerStateList includes an associated list of CSI-ReportConfigs indicating resource set IDs for channel and optionally interference. Each trigger state in the CSI-SemiPersistentOnPUSCH-TriggerStateList includes one associated CSI-ReportConfig.

[0136] Additionally, the time domain behavior of CSI reporting supports periodic, semi-persistent, and aperiodic.

[0137] i) Periodic CSI reporting is performed on short PUCCH and long PUCCH. The periodicity and slot offset of periodic CSI reporting can be configured via RRC, and refer to the CSI-ReportConfig IE.

[0138] ii) SP (semi-periodic) CSI reporting is performed on short PUCCH, long PUCCH, or PUSCH.

[0139] In case of SP CSI on short / long PUCCH, the period and slot offset are set by RRC, and CSI reporting is activated / deactivated with a separate MAC CE / DCI.

[0140] 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.

[0141] The initial CSI reporting timing follows the PUSCH time domain allocation value indicated in the DCI, and subsequent CSI reporting timing follows the cycle set by RRC.

[0142] DCI format 0_1 ​​contains a CSI request field and can activate / deactivate a specific configured SP-CSI trigger state. SP CSI reporting has the same or similar activation / deactivation mechanism as data transmission on the SPS PUSCH.

[0143] iii) Aperiodic CSI reporting is performed on PUSCH and is triggered by DCI. In this case, information related to the triggering of aperiodic CSI reporting can be transmitted / indicated / configured via MAC-CE.

[0144] For AP CSI with AP CSI-RS, AP CSI-RS timing is set by RRC, and timing for AP CSI reporting is dynamically controlled by DCI.

[0145] NR does not apply the method of dividing CSI into multiple reporting instances (e.g., transmitting in the order of RI, WB PMI / CQI, and SB PMI / CQI) used for PUCCH-based CSI reporting in LTE. Instead, NR restricts specific CSI reporting on short / long PUCCHs and defines CSI omission rules. Furthermore, with respect to AP CSI reporting timing, PUSCH symbol / slot locations are dynamically indicated by DCI. Candidate slot offsets are configured by RRC. For CSI reporting, the slot offset (Y) is configured for each reporting setting. For UL-SCH, the slot offset K2 is configured separately.

[0146] Two CSI latency classes (low latency class, high latency class) are defined from the perspective of CSI computation complexity. Low latency CSI is WB CSI including up to 4 ports Type-I codebook or up to 4-port non-PMI feedback CSI. High latency CSI refers to any CSI other than low latency CSI. For a normal terminal, (Z, Z') is defined in units of OFDM symbols. Here, Z represents the minimum CSI processing time from receiving an aperiodic CSI triggering DCI to performing a CSI report. In addition, Z' represents the minimum CSI processing time from receiving a CSI-RS for channel / interference to performing a CSI report.

[0147] Additionally, the terminal reports the number of CSIs it can calculate simultaneously.

[0148] < AIML related explanation >

[0149] Advances in AI / ML (Artificial intelligence / machine learning) technology are leading to the intelligence / advanced advancement of the nodes and terminals that make up wireless communication networks.

[0150] Below, a functional framework for AI operation is described with reference to FIG. 2.

[0151] Figure 2 illustrates the functional framework of the AI / ML model.

[0152] Below, to explain AI (or AI / ML) more specifically, the terms can be defined as follows.

[0153] - Data collection: Data collected from network nodes, management entities, or UEs as a basis for AI model training, data analysis, and inference.

[0154] - AI Model: A data-driven algorithm that applies AI technology to generate a set of outputs containing predictive information and / or decision parameters based on a set of inputs.

[0155] - AI / ML Training: An online or offline process of training an AI model by learning features and patterns that best represent the data and obtain a trained AI / ML model for inference.

[0156] - AI / ML Inference: The process of making predictions or inducing decisions based on collected data and the AI ​​model using a trained AI model.

[0157] Referring to FIG. 2, the data collection function (10) is a function that collects input data and provides processed input data to the model training function (20) and the model inference function (30).

[0158] Examples of input data may include measurements from UEs or other network entities, feedback from actors, and output from AI models.

[0159] The Data Collection function (10) performs data preparation based on input data and provides input data processed through data preparation. Here, the Data Collection function (10) does not perform data preparation specific to each AI algorithm (e.g., data pre-processing and cleaning, formatting, and transformation), but can perform data preparation common to AI algorithms.

[0160] After the data preparation process is performed, the Model Training function (10) provides training data (11) to the Model Training function (20) and provides inference data (Inference Data) (12) to the Model Inference function (30). Here, the Training Data (11) is data required as input for the AI ​​Model Training function (20). The Inference Data (12) is data required as input for the AI ​​Model Inference function (30).

[0161] The Data Collection function (10) may be performed by a single entity (e.g., UE, RAN node, network node, etc.) or may be performed by multiple entities. In this case, Training Data (11) and Inference Data (12) may be provided to the Model Training function (20) and Model Inference function (30), respectively, from multiple entities.

[0162] The Model Training function (20) is a function that performs AI model training, validation, and testing, which can generate model performance metrics as part of the AI ​​model testing process. If necessary, the Model Training function (20) also handles data preparation (e.g., data pre-processing and cleaning, forming, and transformation) based on the Training Data (11) provided by the Data Collection function (10).

[0163] Here, Model Deployment / Update (13) is used to initially deploy the trained, verified, and tested AI model to the Model Inference function (30) or to provide the updated model to the Model Inference function (30).

[0164] The Model Inference function (30) is a function that provides AI model inference output (16) (e.g., prediction or decision). If applicable, the Model Inference function (30) may provide model performance feedback (14) to the Model Training function (20). In addition, the Model Inference function (30) is also responsible for data preparation (e.g., data pre-processing and cleaning, forming, and transformation) based on the Inference Data (12) provided by the Data Collection function (10), if necessary.

[0165] Here, Output (16) refers to the inference output of the AI ​​model generated by the Model Inference function (30), and the details of the inference output may vary depending on the use case.

[0166] Model Performance Feedback (14) can be used to monitor the performance of the AI ​​model if available, and this feedback may be omitted.

[0167] The actor function (40) is a function that receives the output (16) from the model inference function (30) and triggers or performs a corresponding task / action. The actor function (40) can trigger tasks / actions for other entities (e.g., one or more UEs, one or more RAN nodes, one or more network nodes, etc.) or for itself.

[0168] Feedback (15) can be used to derive training data (11), inference data (12), or to monitor the performance of the AI ​​model, its impact on the network, etc.

[0169] Meanwhile, the definitions of training / validation / test in data sets used in AI / ML can be distinguished as follows.

[0170] - Training data: This refers to the data set for learning the model.

[0171] - Validation data: This refers to a data set used to validate a model that has already completed training. In other words, it refers to a data set typically used to prevent overfitting of the training data set.

[0172] It also refers to a data set for selecting the best model among the various models learned during the learning process. Therefore, it can be viewed as a type of learning.

[0173] - Test data: This refers to the data set for final evaluation. This data is unrelated to learning.

[0174] In the case of the above data set, if the training set is generally divided, the training data and validation data can be divided and used in a ratio of 8:2 or 7:3 within the entire training set, and if the test is included, it can be divided and used in a ratio of 6:2:2 (training: validation: test).

[0175] The functions exemplified in FIG. 2 above may be implemented in a RAN node (e.g., a base station, a TRP, a central unit (CU) of a base station, etc.), a network node, an operation administration maintenance (OAM) of a network operator, or a UE.

[0176] Alternatively, two or more entities, such as a RAN, a network node, a network operator's OAM, or a UE, may cooperate to implement the functions illustrated in FIG. 2. For example, one entity may perform some of the functions of FIG. 2, and another entity may perform the remaining functions. In this way, since some of the functions illustrated in FIG. 2 are performed by a single entity (e.g., a UE, a RAN node, a network node, etc.), the transmission / provision of data / information between each function may be omitted. For example, if the Model Training function (20) and the Model Inference function (30) are performed by the same entity, the transmission / provision of Model Deployment / Update (13) and Model Performance Feedback (14) may be omitted.

[0177] Alternatively, any one of the functions illustrated in FIG. 2 may be performed collaboratively by two or more entities, including a RAN, a network node, a network operator's OAM, or a UE. This may be referred to as a split AI operation.

[0178] < AI Model Training Function (Network Node) - AI Model Inference Function (RAN Node) >

[0179] Below, we examine the operation when the AI ​​Model Training function is performed by a network node (e.g., core network node, network operator's OAM, etc.) and the AI ​​Model Inference function is performed by a RAN node (e.g., base station, TRP, base station's CU, etc.).

[0180] Step 1: RAN node 1 and RAN node 2 transmit input data (i.e., training data) for AI model training to the network node. Here, RAN node 1 and RAN node 2 can also transmit data collected from the UE (e.g., UE measurements related to RSRP, RSRQ, SINR of the serving cell and neighboring cells, UE location, speed, etc.) to the network node.

[0181] Step 2: Network nodes train the AI ​​model using the received training data.

[0182] Step 3: The network node distributes / updates the AI ​​Model to RAN Node 1 and / or RAN Node 2. RAN Node 1 (and / or RAN Node 2) may continue model training based on the received AI Model.

[0183] For convenience of explanation, we assume that the AI ​​Model is deployed / updated only to RAN node 1.

[0184] Step 4: RAN node 1 receives input data for AI Model Inference (i.e., Inference data) from UE and RAN node 2.

[0185] Step 5: RAN node 1 performs AI Model Inference using the received Inference data to generate output data (e.g., prediction or decision).

[0186] Step 6: If applicable, RAN node 1 may transmit model performance feedback to the network nodes.

[0187] Step 7: RAN node 1, RAN node 2, and the UE (or 'RAN node 1 and the UE', or 'RAN node 1 and the RAN node 2') perform actions based on the output data. For example, in the case of a load balancing operation, the UE may move from RAN node 1 to RAN node 2.

[0188] Step 8: RAN node 1 and RAN node 2 transmit feedback information to the network nodes.

[0189] < AI Model Training / Inference Function: RAN node >

[0190] Below, we examine the operation when both the AI ​​Model Training function and the AI ​​Model Inference function are performed by RAN nodes (e.g., base stations, TRPs, base station CUs, etc.).

[0191] Step 1: UE and RAN node 2 transmit input data (i.e., training data) for AI model training to RAN node 1.

[0192] Step 2: RAN node 1 trains the AI ​​model using the received training data.

[0193] Step 3: RAN node 1 receives input data for AI Model Inference (i.e., Inference data) from the UE and RAN node 2.

[0194] Step 4: RAN node 1 performs AI Model Inference using the received Inference data to generate output data (e.g., prediction or decision).

[0195] Step 5: RAN node 1, RAN node 2, and the UE (or 'RAN node 1 and the UE', or 'RAN node 1 and the RAN node 2') perform actions based on the output data. For example, in the case of a load balancing operation, the UE may move from RAN node 1 to RAN node 2.

[0196] Step 6: RAN node 2 sends feedback information to RAN node 1.

[0197] < AI Model Training Function (RAN Node) - AI Model Inference Function (UE) >

[0198] Below, we examine the operation when the AI ​​Model Training function is performed by a RAN node (e.g., a base station, TRP, a CU of the base station, etc.) and the AI ​​Model Inference function is performed by a UE.

[0199] Step 1: The UE transmits input data (i.e., training data) for AI model training to the RAN node. Here, the RAN node can collect data (e.g., UE measurements related to RSRP, RSRQ, SINR of the serving cell and neighboring cells, UE location, speed, etc.) from various UEs and / or from other RAN nodes.

[0200] Step 2: The RAN node trains the AI ​​model using the received training data.

[0201] Step 3: The RAN node distributes / updates the AI ​​model to the UE. The UE may also continue model training based on the received AI model.

[0202] Step 4: Receive input data (i.e., Inference data) for AI Model Inference from the UE and RAN nodes (and / or from other UEs).

[0203] Step 5: The UE performs AI Model Inference using the received Inference data to generate output data (e.g., prediction or decision).

[0204] Step 6: If applicable, the UE may send model performance feedback to the RAN node.

[0205] Step 7: The UE and RAN nodes perform actions based on the output data.

[0206] Step 8: The UE transmits feedback information to the RAN node.

[0207] In this document, ' / ' means 'and', 'or', or 'and / or' depending on the context.

[0208] In Release 18, research is underway on CSI compression based on a two-sided model and CSI prediction based on a UE-sided model, both related to AI / ML. A use case for CSI prediction using AI / ML is described in detail with reference to Figure 3.

[0209] Figure 3 illustrates an example of CSI prediction based on an AI / ML model. Specifically, Figure 3 illustrates a use case for estimating / predicting / determining one or more future CSIs. The model performing model inference related to this use case may be an AI / ML (Artificial Intelligence / Machine Learning) model provided only on the terminal side. For example, the model input / model output type may be a raw channel matrix or a precoder type (e.g., eigenvector).

[0210] For example, CSI prediction may mean prediction of at least one of the parameters included in CSI (e.g., Channel Quality Indicator (CQI), Precoding Matrix Indicator (PMI), Rank Indicator (RI), Reference Signal Received Power (RSRP), CSI-RS Resource Indicator (CRI), SSB Resource Indicator (SSBRI), etc.). As a specific example, in CSI prediction, the input of the model may be RSRP (e.g., measured RSRP), and the output of the model may be predicted RSRP (e.g., predicted RSRP).

[0211] This specification proposes an improved reporting method to effectively support CQI (Channel Quality Indicator) calculation in AI / ML-based CSI prediction.

[0212] Below we discuss the improved method proposed by this specification.

[0213] Existing CSI prediction methods rely solely on CSI information, without interference information, resulting in incomplete CQI calculations. We propose methods to address this ambiguity regarding Channel Measurement Resource (CMR) / Interference Measurement Resource (IMR). Below, we explore methods for resolving this ambiguity in CQI calculations in AI / ML-based CSI prediction.

[0214] Proposal 1

[0215] In this specification, Model 1, Model 2, etc. may be defined as follows for convenience of explanation. However, 'Model 1' and 'Model 2' are only used to distinguish the objects (channel and interference) predicted by the models, and are not intended to limit the technical ideas of this specification to these terms.

[0216] - Model 1: Channel Prediction Model (CPM)

[0217] - Model 2: Interference Prediction Model (IPM)

[0218] A node (e.g., a terminal) performing training / interference can predict CSI (e.g., at least one parameter included in the above-described CSI, channel-related information, and / or interference-related information) based on Model 1 and Model 2.

[0219] The terminal can output and report the predicted channel based on Model 1 in the following way.

[0220] For example, the terminal may output and report a predicted channel based on a historical channel through Model 1. Specifically, the historical channel is based on the input of Model 1, and the predicted channel is based on the output of Model 1. The historical channel may be based on information indicating a past channel state. The predicted channel may be based on information indicating a predicted future channel state based on the historical channel. For example, the historical channel / predicted channel may be based on information including at least one of i) at least one parameter included in existing CSI (e.g., CQI, PMI, RI, etc.), ii) a signal-to-noise ratio (SNR), iii) a raw channel matrix, and / or iv) an eigenvector.

[0221] For example, the information reported by the terminal may differ depending on the input / output CSI type of Model 1.

[0222] For example, when a raw channel matrix is ​​used as input / output of CPM, the terminal can report CSI based on explicit feedback.

[0223] For example, when a precoder type (e.g., eigenvector, eigenvalue) is used as input / output of CPM, the terminal can report CSI based on the implicit feedback of the existing NR.

[0224] The terminal can report the predicted interference by outputting it in the following way based on Model 2.

[0225] For example, a terminal can output and report predicted interference based on historical interference measurements through Model 2. Specifically, historical interference measurements are based on the input of Model 2, and predicted interference is based on the output of Model 2. Historical interference measurements can be based on information related to past interference measurements. Predicted interference can be based on information related to predicted future interference based on historical interference measurements.

[0226] For example, based on Model 2 outputting predicted interference based on historical interference measurement, for the historical interference measurement, the measurement restriction for one-shot measurement can be set / limited to always be on / enabled.

[0227] For example, based on Model 2's prediction of interference based on historical interference measurements, the base station can set one or more specific measurement windows to ensure effective interference measurements. In this case, the filtered (e.g., averaged) interference measurements within the measurement window can be used as input to Model 2.

[0228] For example, the terminal can output and report predicted CQI / SINR (Signal Interference Noise Ratio) based on historical CQI / SINR through Model 2. Specifically, the historical CQI / SINR is based on the input of Model 2, and the predicted CQI / SINR is based on the output of Model 2. The historical CQI / SINR may be based on past CQI and / or SINR. The predicted CQI / SINR may be based on future CQI and / or SINR predicted based on past CQI and / or SINR.

[0229] When a model that infers the raw channel and a model that infers interference are used together, it can be easy to calculate the predicted CQI corresponding to a specific reporting occasion within the prediction window at the terminal side. The CQI can be calculated based on the SINR. For example, the SINR is It can be expressed as follows. When only raw channel information is inferred with a single model, the desired signal power is obtained through the raw channel information. can be calculated, but the interference power Therefore, if we can calculate the interference power by adding a model that infers interference, the accuracy of the CQI determined / calculated from SINR can be improved.

[0230] Unlike the method of predicting interference through IPM, a method of calculating CQI within the prediction window by applying the interference power measured by the terminal based on the reference resource to the prediction window can be considered.

[0231] For example, while the desired power is calculated based on the predicted raw channel matrix, the interference can be unified into a single, pre-calculated value. To compensate for interference fluctuations, a long-term measurement window can be set, or measurement restriction can be turned on (enabled) to allow calculations based on simple instantaneous interference.

[0232] For example, a method may be considered in which a base station signals correction values ​​for future interference or future CQI / SINR calculations. Specifically, a terminal may receive correction values ​​for future interference or future CQI / SINR calculations from the base station. Based on these correction values, the terminal may calculate / determine predicted CQI / SINR, etc.

[0233] For example, the base station can transmit information about the δ value to the terminal. At this time, the SINR is It can be.

[0234] According to Proposal 1 above, CQI accuracy can be improved. The base station can perform downlink scheduling based on the corresponding CQI. Since DL scheduling can be performed optimally for the current channel, including interference, reliability in DL transmission and reception procedures can be improved.

[0235] Proposal 2

[0236] In UE-sided AI / ML based CSI prediction, the terminal can calculate / report CSI in the following ways.

[0237] Method 1: The terminal can calculate / report CSI based on CPM. Specifically, only CPM is turned on, and interference can be measured without prediction as before (e.g., for a high SINR regime).

[0238] Method 2: The terminal can calculate / report CSI based on IPM. Specifically, only IPM can be turned on, and the channel can be measured without prediction as before (e.g., for a low SINR regime).

[0239] Method 3: The terminal can calculate / report CSI based on CPM and IPM. Specifically, the terminal can calculate / report CSI by turning on both CPM and IPM and predicting channels and interference.

[0240] For example, when calculating / reporting CSI, the terminal may report to the base station / NW which method among Methods 1 to 3 is used to report the CSI. As a specific example, the terminal may report to the base station information indicating a model related to the CSI (e.g., i) CPM, ii) IPM, or iii) CPM and IPM).

[0241] For example, the base station / NW may configure / instruct the terminal to perform reporting based on which method among method 1, method 2, and method 3 for a specific CSI report. As a specific example, the base station may configure the terminal with information indicating a model related to the CSI report (e.g., i) CPM, ii) IPM, or iii) CPM and IPM).

[0242] For example, a terminal can report CSI based on multiple methods among Method 1, Method 2, and Method 3, and the base station can determine which CSI is correct based on A / N (ACK / NACK) and other factors by scheduling based on each of Method 1, Method 2, and Method 3 reported by the terminal, thereby assisting in LCM (Life Cycle Management). As a more specific example, if the base station finds out that only Method 1 is working properly after scheduling based on each of the multiple methods reported by the terminal, the base station can terminate IPM LCM or instruct the terminal to retrain.

[0243] For example, separately from the UE calculating / reporting CSI, the UE capability for the model(s) supported for CSI reporting may be reported by the UE. Based on the UE capability, the model(s) supported for CSI reporting may be indicated as i) IPM, ii) CPM, or iii) IPM and CPM. Specifically, the UE may report the UE capability to the base station, indicating whether i) only IPM is available, ii) only CPM is available, or iii) both IPM and CPM are available, and the base station may configure / instruct / transmit information indicating a model related to CSI reporting to the UE based on the UE capability. For example, the information indicating the model related to CSI reporting may be transmitted to the UE as configuration information related to the CSI report.

[0244] Proposal 3

[0245] In UE-sided AI / ML based CSI prediction, the following method of reporting PMI to the base station / NW can be considered.

[0246] - Method 1: The terminal can recalculate PMI based on AI / ML model output CSI for each reporting instance and report it to NW.

[0247] - Method 2: For each reporting instance, the AI / ML model output is output in the form of PMI, and the terminal can report the PMI to the NW.

[0248] Method 1 above offers advantages in terms of inference accuracy, as the terminal infers CSI based on historical raw channels, which contain more information than the PMI. However, since the PMI is recalculated and reported to the base station through post-processing, loss values ​​resulting from post-processing or existing channel quality information (e.g., eigenvalues ​​of the raw channel matrix or eigenvalues ​​of covariance of the raw channel matrix) may be additionally reported.

[0249] In the case of the above method 2, since the PMI itself is a model output, there is an advantage in that the terminal can report the PMI directly without separate calculation.

[0250] The terminal can calculate and report CQI based on the PMI reported through the above method 1 / method 2. At this time, a method in which the terminal reports CQI based on interference information may be considered.

[0251] Proposal 3-1

[0252] The terminal can use the latest interference during the prediction window. Specifically, the terminal can measure the most recent IMR and calculate and report the CQI based on the same interference during the prediction window.

[0253] Proposal 3-2

[0254] The terminal can use the long-term average interference of the past during the prediction window. Specifically, the terminal can calculate the CQI based on the long-term average interference during the prediction window. According to Proposal 3-2, even if the interference measured based on the most recent IMR relative to the reference resource deviates significantly from the long-term trend, the terminal can calculate and report a CQI that reflects the long-term trend by using the long-term average interference during the prediction window.

[0255] Proposal 3-3

[0256] The terminal can utilize predicted interference predicted through the inference operation of another AI / ML model. Specifically, the terminal can utilize a separate model to predict interference. The predicted interference can be output from the model based on historical interference. The terminal can calculate and report the predicted CQI based on the predicted interference.

[0257] Proposal 4

[0258] In NW-sided AI / ML-based CSI prediction, it may be considered how the base station / NW utilizes / utilizes information on i) SNR (or RSRP) and INR, or ii) raw channel and interference. For example, the terminal may report information on i) SNR (or RSRP) and INR, or ii) raw channel and interference to the base station.

[0259] If a base station / NW only periodically receives information about a desired channel from a terminal / UE, it may be difficult for the base station / NW to understand the CQI. For example, when the CQI decreases (e.g., when the reported CQI is lower than the previously reported CQI), the base station / NW cannot tell whether the channel power has decreased or the interference power has increased. The present embodiment is intended to solve this problem. For example, a terminal / UE can report both raw channel information and interference information to the base station / NW. For example, a terminal / UE can report both SNR (or RSRP) information and INR information to the base station / NW. This allows the base station / NW to understand changes in the CQI in more detail. For example, when interference increases and the CQI decreases, the base station / NW can determine the reason for the decrease in the CQI based on the interference information reported by the terminal / UE, and the base station / NW can take action to adjust the beam of an adjacent cell to reduce the interference.

[0260] FIG. 4 is a flowchart illustrating an example of signaling based on a method according to at least one embodiment of the present disclosure. FIG. 4 illustrates an example of signaling between a terminal and a base station / NW based on Proposals 1 to 4 described above. The terminal / UE and the base station / NW are merely examples and may be replaced with various devices. FIG. 4 is merely for convenience of explanation and does not limit the scope of the present invention. Furthermore, some steps / processes illustrated in FIG. 4 may be omitted depending on circumstances and / or settings.

[0261] A terminal (UE) performs a reporting procedure (S410) related to terminal capability values ​​based on a proposed method (e.g., Proposals 1 to 4). In this procedure, information on the range of terminal capability values ​​may be included and reported to a base station / NW (e.g., gNB / TRP(s)).

[0262] Based on the proposed method (e.g., Proposal 1 to Proposal 4), the base station performs the setting of functions / parameters related to the terminal capability values ​​reported in S410 and / or the setting / instruction related to the terminal report in the subsequent S430 (S420).

[0263] The terminal reports the terminal capability value for the reporting time or a specific past / future time based on the report of S410 based on the proposed method (S430).

[0264] Based on the terminal's report information (S430), the base station can perform (re)configuration or instructions for functions / parameters related to terminal capability values ​​(S440).

[0265] In applying the above operation, all or part of the S420 process may be performed before / after the S410 process and / or simultaneously with the S410 process.

[0266] The above-described proposals 1, 2, 3, and 4 can be extended to not only AI / ML-based CSI prediction but also temporal beam prediction.

[0267] 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 to 4) can be processed by the devices of FIGS. 7 and 8 (e.g., the processor (110, 210) of FIG. 7).

[0268] 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 to 4) may be stored in a memory (e.g., 140, 240 of FIG. 7) in the form of commands / programs (e.g., instructions, executable codes) for driving at least one processor (e.g., 110, 210 of FIG. 7).

[0269] The embodiments described below are specifically described with reference to FIGS. 5 and 6 in terms of the operation of the terminal and base station. The methods described below are distinguished for convenience of explanation, and it is understood that some components of one method may be substituted for or combined with some components of another method.

[0270] FIG. 5 is a flowchart illustrating a method according to one embodiment of the present specification.

[0271] Referring to FIG. 5, a method according to one embodiment of the present specification includes a channel state information-reference signal (CSI-RS) receiving step (S510) and the CSI reporting step (S520).

[0272] In S510, the terminal receives CSI-RS from the base station.

[0273] The above CSI-RS can be received based on configuration information related to CSI.

[0274] For example, the CSI-RS can be used for CSI computation.

[0275] The above CSI may include at least one of i) a channel quality indicator (CQI), ii) a precoding matrix indicator (PMI), iii) a CSI-RS resource indicator (CRI), iv) an SS / PBCH block resource indicator (SSBRI), v) a layer indicator (LI), vi) a rank indicator (RI), vii) a L1-RSRP, and / or viii) a L1-SINR.

[0276] At this time, the CSI (e.g., at least one parameter reported as CSI, information included in the CSI) may be measured / determined / predicted / calculated based on the above-described CPM and / or IPM. As a specific example, the CSI (e.g., CQI) may be calculated / determined based on i) information related to channel prediction predicted through the CPM and / or ii) information related to interference prediction predicted through the IPM. For convenience of explanation in this specification, i) information related to channel prediction may be expressed as first information, and ii) information related to interference prediction may be expressed as second information.

[0277] For example, information related to the channel prediction can be predicted based on a historical channel.

[0278] In one embodiment, the CSI may include a channel quality indicator (CQI). The CQI may be determined based on information related to the channel prediction and information related to the interference prediction. This embodiment may be based on Proposal 1.

[0279] In one embodiment, information related to the interference prediction may be predicted based on historical interference measurements. This embodiment may be based on Proposal 1.

[0280] For example, the past interference measurement may be based on one or more past interference measurements within a time interval. As a specific example, the past interference measurement may be a filtered (e.g., averaged) result of one or more past interference measurements within a specific measurement window.

[0281] For example, the above past interference measurement may be based on a one-shot measurement, and the measurement restriction may be set to always be on / enabled for the one-shot measurement.

[0282] In one embodiment, the information related to the interference prediction may be predicted based on historical CQI (historical CQI) and / or historical Signal-to-Interference Noise Ratio (historical SINR). This embodiment may be based on Proposal 1.

[0283] In one embodiment, the method may further include the step of receiving configuration information related to the CSI.

[0284] For example, the above configuration information may include information related to a CSI report.

[0285] In one embodiment, the configuration information may include information related to reporting of i) information related to the channel prediction and / or ii) information related to the interference prediction.

[0286] For example, a terminal may calculate / report CSI in various ways depending on the environment (e.g., high SINR environment, low SINR environment). As a specific example, the terminal may report CSI based on i) information related to the channel prediction and / or ii) information related to the interference.

[0287] For example, the base station may configure / instruct the terminal on how to calculate / report CSI, depending on the environment. Specifically, the base station may transmit configuration information to the terminal, allowing the terminal to report CSI based on i) information related to channel prediction and / or ii) information related to interference, depending on the environment.

[0288] In one embodiment, based on the configuration information, the CSI may include i) information related to the channel prediction and / or ii) information related to the interference prediction.

[0289] For example, the terminal may report CSI based on information related to the channel prediction predicted through the CPM. Specifically, in an environment with high SINR, the terminal may only use the CPM. The terminal may report CSI based on information related to the channel prediction output by the CPM. Specifically, at least one parameter (e.g., CQI) included in the CSI may be determined based on information related to the channel prediction. Furthermore, the CSI may include information related to the channel prediction.

[0290] For example, the terminal may report CSI based on information related to the interference prediction predicted through the IPM. As a specific example, in an environment with low SINR, the terminal may only use the IPM. The terminal may report CSI based on information related to the interference prediction output by the IPM. As a specific example, at least one parameter (e.g., CQI) included in the CSI may be determined based on information related to the interference prediction. In addition, the CSI may include information related to the interference prediction.

[0291] For example, the terminal may report CSI based on the CPM and the IPM. As a specific example, the terminal may report CSI based on i) information related to the channel prediction output by the CPM and ii) information related to the interference prediction output by the IPM. As a specific example, at least one parameter (e.g., CQI) included in the CSI may be determined based on information related to the channel prediction and information related to the interference prediction. In addition, the CSI may include i) information related to the channel prediction and ii) information related to the interference prediction. The present embodiment may be based on Proposal 2.

[0292] In one embodiment, the CSI may include a channel quality indicator (CQI).

[0293] For example, the CQI may be determined based on information related to the channel prediction and / or information related to the interference prediction. This embodiment may be based on Proposals 1 and 2.

[0294] In one embodiment, the CSI may include a PMI.

[0295] In one embodiment, the PMI may be predicted based on information related to the channel prediction and / or information related to the interference prediction. As a specific example, the terminal may predict the PMI based on information related to the channel prediction and / or information related to the interference prediction at each reporting instance. In this case, since the output of the AI / ML model is the PMI itself, the terminal may directly report the PMI to the base station without additional calculation. This embodiment may be based on Method 1 of Proposal 3.

[0296] In one embodiment, the PMI may be determined based on a predicted channel state. In this case, the channel state may be calculated / predicted based on information related to the channel prediction and / or information related to the interference prediction. As a specific example, the terminal may recalculate the PMI based on the predicted channel state for each report. In this case, since the channel state is predicted based on a historical raw channel, there is an advantage of high prediction accuracy. Since additional processing is required to transform the channel into the PMI, the terminal may additionally report a loss value resulting from the additional processing or existing channel quality information (e.g., eigenvalue of raw channel matrix or eigenvalue of covariance of raw channel matrix). This embodiment may be based on Method 2 of Proposal 3.

[0297] In one embodiment, the CSI includes a CQI, and the CQI may be determined based on the PMI and information related to the interference prediction. Specifically, interference information may be required when the terminal determines the CQI based on the PMI. In this case, the terminal may utilize the information related to the interference prediction as interference information for determining the CQI. This embodiment may be based on Proposal 3.

[0298] In one embodiment, the information related to the interference prediction may be determined based on the most recently measured Interference-to-Noise Ratio (INR). Specifically, the terminal may measure the most recent IMR and determine the CQI by using the interference based on the most recent IMR throughout the prediction window. This embodiment may be based on Proposal 3-1.

[0299] In one embodiment, information related to the interference prediction may be determined based on an average of one or more interference measurements within a time interval. Specifically, the terminal may determine the CQI using the long-term average interference. In this case, even if the interference measured based on the most recent IMR relative to the reference resource deviates significantly from the long-term perspective, the terminal can determine and report a CQI that reflects the long-term trend by using the long-term average interference. This embodiment may be based on Proposal 3-2.

[0300] In step S520, the terminal reports the CSI to the base station.

[0301] For example, a terminal may perform measurements on channel characteristics based on CSI-RS and feed back a CSI report as a result to the base station.

[0302] For example, the feedback type may include i) Channel Quality Indicator (CQI), ii) Precoding Matrix Indicator (PMI), iii) CSI-RS Resource Indicator (CRI), iv) SSB Resource block Indicator (SSBRI), v) Layer Indicator (LI), vi) Rank Indicator (RI), and vii) L1-Reference Signal Received Strength (RSRP).

[0303] In one embodiment, the CSI may include interference-related information and raw channel-related information. Specifically, the terminal may report CSI, including interference-related information and raw channel-related information, to the base station / NW. This embodiment may be based on Proposal 4.

[0304] In another embodiment, the method may further include transmitting interference-related information and raw channel-related information. Specifically, the terminal may report interference-related information and raw channel-related information to the base station along with the CSI report. This embodiment may be based on Proposal 4.

[0305] In one embodiment, the CSI may include i) SINR or RSRP and ii) INR. Specifically, the terminal may report CSI including i) SINR or RSRP and ii) INR to the base station / NW. This embodiment may be based on Proposal 4.

[0306] In another embodiment, the method may further include the steps of transmitting i) SINR or RSRP and ii) INR. Specifically, the terminal may report i) SINR or RSRP and ii) INR to the base station along with the CSI report. This embodiment may be based on Proposal 4.

[0307] In one embodiment, the information related to the channel prediction and the information related to the interference prediction may be related to the CPM and the IPM. In this specification, the CPM may be represented by the first model, and the IPM may be represented by the second model.

[0308] In one embodiment, the past interference measurements may be associated with the input of the IPM. Information related to the interference prediction may be associated with the output of the IPM.

[0309] For example, the operations according to the embodiments described above can be performed when the model is implemented / equipped on the i) terminal, ii) base station (network) or iii) terminal and base station (network) sides.

[0310] The operations based on S510 to S520 described above can be implemented by the devices of FIGS. 7 and 8. For example, referring to FIG. 7, the terminal (200) can control one or more transceivers (230) and / or one or more memories (240) to perform the operations based on S510 to S520.

[0311] The embodiments described below are specifically described in terms of base station operation.

[0312] S610 to S620 described below correspond to S510 to S520 described in FIG. 5. Considering the above correspondence, redundant descriptions are omitted. The specific descriptions of base station operations described below may be replaced by the corresponding descriptions / exemplifications of FIG. 5.

[0313] FIG. 6 is a flowchart illustrating a method according to another embodiment of the present specification.

[0314] Referring to FIG. 6, a method according to another embodiment of the present specification includes a step of transmitting a channel state information-reference signal (CSI-RS) (S610) and a step of receiving channel state information (CSI) (S620).

[0315] In step S610, the base station transmits CSI-RS to the terminal.

[0316] At step S620, the base station receives CSI from the terminal. At this time, the CSI includes i) information related to channel prediction and ii) information related to interference prediction.

[0317] For example, the CSI may include i) information related to channel prediction and ii) information related to interference prediction. For convenience of explanation in this specification, i) information related to channel prediction may be expressed as first information, and ii) information related to interference prediction may be expressed as second information.

[0318] In one embodiment, the method may further include a step of transmitting configuration information related to the CSI.

[0319] For example, the configuration information may include information related to reporting of i) information related to the channel prediction and / or ii) information related to the interference prediction. This embodiment may be based on Proposal 2.

[0320] According to one embodiment, based on the configuration information, the CSI may include i) information related to channel prediction and / or ii) information related to interference prediction. This embodiment may be based on Proposal 2.

[0321] According to one embodiment, the CSI may include a Channel Quality Indicator (CQI).

[0322] For example, the CQI may be determined based on i) information related to the channel prediction and / or ii) information related to the interference prediction. This embodiment may be based on Proposals 1 and 2.

[0323] The base station can schedule downlink channels based on the CSI reported from the terminal. According to embodiments of the present disclosure, downlink channel scheduling can be performed to minimize the impact of interference.

[0324] The operations based on S610 to S620 described above can be implemented by the devices of FIGS. 7 and 8. For example, referring to FIG. 7, the base station (100) can control one or more transceivers (130) and / or one or more memories (140) to perform the operations based on S610 to S620.

[0325] The operations / terms based on the embodiments described above have been described assuming a 5G system. However, this is for convenience of explanation and is not intended to limit the scope of application of the technical problems and problem-solving means to be solved by this specification to a specific system. The technical problems / technical issues / problems mentioned in this specification may equally exist in other systems (e.g., 6G systems). It is self-evident that the embodiments of this specification can be expanded and applied to solve problems equally existing in the other systems. Therefore, for the expanded application of the embodiments of this specification to other systems, the terms defined / described based on the 5G system may be replaced / changed with terms defined in the other systems (or generalized terms not specific to one system). For example, PRACH, PUSCH, PUCCH, or SRS may be replaced / changed with uplink signals (or uplink channels). For example, SSB, CSI-RS, PDSCH, and PDCCH may be replaced / changed with downlink signals (or downlink channels).

[0326] Hereinafter, a device to which an embodiment of the present specification can be applied (a device that implements a method / operation according to an embodiment of the present specification) will be described with reference to FIGS. 7 and 8.

[0327] FIG. 7 is a drawing showing the configuration of a first device and a second device according to an embodiment of the present specification.

[0328] The first device (100) may include a processor (110), an antenna unit (120), a transceiver (130), and a memory (140).

[0329] The processor (110) performs baseband-related signal processing and may include a higher layer processing unit (111) and a physical layer processing unit (115). The higher layer processing unit (111) may process operations of a MAC layer, an RRC layer, or higher layers. The physical layer processing unit (115) may process operations of a PHY layer. For example, when 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, when the first device (100) is a first terminal device in terminal-to-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).

[0330] The antenna unit (120) may include one or more physical antennas, and when 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, an operating system, applications, etc. related to the operation of the first device (100), and may also include components such as a buffer.

[0331] The processor (110) of the first device (100) may be configured to implement the operation of the base station in the base station-to-terminal communication (or the operation of the first terminal device in the terminal-to-terminal communication) in the embodiments described in the present disclosure.

[0332] The second device (200) may include a processor (210), an antenna unit (220), a transceiver (230), and a memory (240).

[0333] The processor (210) performs baseband-related signal processing and may include a higher layer processing unit (211) and a physical layer processing unit (215). The higher layer processing unit (211) may process operations of a MAC layer, an RRC layer, or higher layers. The physical layer processing unit (215) may process operations of a PHY layer. For example, when 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, when the second device (200) is a second terminal device in terminal-to-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).

[0334] The antenna unit (220) may include one or more physical antennas, and when it includes multiple antennas, it may support MIMO transmission and reception. The transceiver (230) may include an RF transmitter and an RF receiver. The memory (240) may store information processed by the processor (210), software, an operating system, applications, etc. related to the operation of the second device (200), and may also include components such as a buffer.

[0335] The processor (210) of the second device (200) may be configured to implement operations of the terminal in base station-to-terminal communication (or operations of the second terminal device in terminal-to-terminal communication) in the embodiments described in the present disclosure.

[0336] In the operation of the first device (100) and the second device (200), the same explanations given for the base station and the terminal (or the first terminal and the second terminal in the terminal-to-terminal communication) in the examples of the present disclosure may be applied, and redundant explanations are omitted.

[0337] Figure 8 illustrates an AI device according to an embodiment of the present specification. The AI ​​device may be implemented as a fixed or mobile device, such as a TV, projector, smartphone, PC, laptop, digital broadcasting terminal, tablet PC, wearable device, set-top box (STB), radio, washing machine, refrigerator, digital signage, robot, vehicle, etc.

[0338] Referring to FIG. 8, the AI ​​device (100) may include a communication unit (110), a control unit (120), a memory unit (130), an input / output unit (140a / 140b), a learning processor unit (140c), and a sensor unit (140d). Blocks 110 to 130 / 140a to 140d correspond to blocks 110 to 130 / 140 of FIG. X3, respectively.

[0339] The communication unit (110) can transmit and receive wired and wireless signals (e.g., sensor information, user input, learning models, control signals, etc.) with external devices such as other AI devices or AI servers using wired and wireless communication technology. To this end, the communication unit (110) can transmit information within the memory unit (130) to the external device or transmit signals received from the external device to the memory unit (130).

[0340] The control unit (120) may determine at least one executable operation of the AI ​​device (100) based on information determined or generated using a data analysis algorithm or a machine learning algorithm. In addition, the control unit (120) may control components of the AI ​​device (100) to perform the determined operation. For example, the control unit (120) may request, search, receive, or utilize data from the learning processor unit (140c) or the memory unit (130), and may control components of the AI ​​device (100) to perform a predicted operation or an operation determined to be desirable among at least one executable operation. In addition, the control unit (120) may collect history information including the operation contents of the AI ​​device (100) or user feedback on the operation, and store the collected history information in the memory unit (130) or the learning processor unit (140c), or transmit the collected history information to an external device such as an AI server. The collected history information may be used to update a learning model.

[0341] The memory unit (130) can store data that supports various functions of the AI ​​device (100). For example, the memory unit (130) can store data obtained from the input unit (140a), data obtained from the communication unit (110), output data of the learning processor unit (140c), and data obtained from the sensing unit (140). In addition, the memory unit (130) can store control information and / or software codes necessary for the operation / execution of the control unit (120).

[0342] The input unit (140a) can obtain various types of data from the outside of the AI ​​device (100). For example, the input unit (140a) can obtain learning data for model learning and input data to which the learning model will be applied. The input unit (140a) may include a camera, a microphone, and / or a user input unit. The output unit (140b) may generate output related to vision, hearing, or touch. The output unit (140b) may include a display unit, a speaker, and / or a haptic module. The sensing unit (140) can obtain at least one of internal information of the AI ​​device (100), information about the surrounding environment of the AI ​​device (100), and user information using various sensors. The sensing unit (140) may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone, and / or a radar.

[0343] The learning processor unit (140c) can train a model composed of an artificial neural network using learning data. The learning processor unit (140c) can perform AI processing together with the learning processor unit of the AI ​​server. The learning processor unit (140c) can process information received from an external device via the communication unit (110) and / or information stored in the memory unit (130). In addition, the output value of the learning processor unit (140c) can be transmitted to an external device via the communication unit (110) and / or stored in the memory unit (130).

[0344] 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 LPWAN (Low Power Wide Area Network) technology and may be implemented in standards such as LTE Cat NB1 and / or LTE Cat NB2, and is not limited to the above-described names.

[0345] 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 called by various names such as eMTC (enhanced Machine Type Communication). For example, LTE-M technology may be implemented by at least one of various standards such as 1) LTE CAT 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-BL (non-Bandwidth Limited), 5) LTE-MTC, 6) LTE Machine Type Communication, and / or 7) LTE M, and is not limited to the above-described names.

[0346] Additionally or alternatively, the wireless communication technology implemented in the device of the present disclosure may include at least one of ZigBee, Bluetooth, and Low Power Wide Area Network (LPWAN), which take low-power communication into account, and is not limited to the above-described names. 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 called by various names.

Claims

1. In the method, A step of receiving a Channel State Information-Reference Signal (CSI-RS); and a step of reporting channel state information (CSI); A method wherein the CSI comprises i) first information related to channel prediction and ii) second information related to interference prediction.

2. In paragraph 1, The above CSI includes a Channel Quality Indicator (CQI), A method, characterized in that the CQI is determined based on the first information and the second information.

3. In paragraph 1, A method characterized in that the second information is predicted based on historical interference measurements.

4. In paragraph 3, A method, characterized in that the past interference measurement is based on one or more past interference measurements within a time interval.

5. In paragraph 1, A method characterized in that the second information is predicted based on past CQI and / or past signal-to-interference noise ratio (SINR).

6. In paragraph 1, A method, wherein the method further comprises the step of transmitting information related to interference and information related to a raw channel.

7. In paragraph 1, A method, wherein the method further comprises the steps of transmitting i) a signal-to-noise ratio (SNR) or reference signal received power (RSRP) and ii) an interference-to-noise ratio (INR).

8. In paragraph 1, The method further includes a step of receiving setting information related to the CSI; A method, characterized in that the above setting information includes information related to reporting of the first information and / or the second information.

9. In paragraph 8, A method, characterized in that the CSI includes the first information and / or the second information based on the above setting information.

10. In paragraph 9, The above CSI includes a channel quality indicator (CQI), A method, characterized in that the CQI is determined based on the first information and / or the second information.

11. In paragraph 1, The above CSI includes a Precoding Matrix Indicator (PMI), A method, characterized in that the PMI is determined based on the first information and / or the second information.

12. In paragraph 11, The above PMI is determined based on the channel status, A method, characterized in that the channel state is predicted based on the first information and / or the second information.

13. In paragraph 11, The above CSI includes a channel quality indicator (CQI), A method, characterized in that the CQI is determined based on the PMI and the second information.

14. In paragraph 13, A method wherein the second information is determined based on the most recently measured Interference-to-Noise Ratio (INR).

15. In paragraph 13, A method wherein the second information is determined based on an average of one or more interference measurements within a time interval.

16. In paragraph 3, A method, characterized in that the first information and the second information are related to a first model and a second model.

17. In paragraph 16, The above past interference measurements are related to the input of the second model, A method, characterized in that the second information is related to the output of the second model.

18. In paragraph 16, A method, characterized in that the above model includes a user equipment-sided model.

19. At the terminal, One or more transmitters and receivers; one or more processors; and One or more memories connected to said one or more processors and storing instructions, A terminal, characterized in that the instructions, based on being executed by the one or more processors, cause the terminal to perform all steps of the method according to any one of claims 1 to 18.

20. In a device comprising one or more memories and one or more processors connected to the one or more memories, A device characterized in that said one or more memories store instructions that cause said device to perform all steps of a method according to any one of claims 1 to 18, based on being executed by said one or more processors.

21. In a non-transitory computer-readable medium storing instructions, A non-transitory computer-readable medium, characterized in that the instructions executable by one or more processors cause a terminal to perform all steps of a method according to any one of claims 1 to 18.

22. In the method, A step of transmitting a Channel State Information-Reference Signal (CSI-RS); and comprising a step of receiving channel state information (CSI); A method wherein the CSI comprises i) first information related to channel prediction and ii) second information related to interference prediction.

23. In paragraph 22, The method further includes a step of transmitting setting information related to the CSI; A method, characterized in that the above setting information includes information related to reporting of the first information and / or the second information.

24. In paragraph 23, A method, characterized in that the CSI includes the first information and / or the second information based on the above setting information.

25. In paragraph 24, The above CSI includes a Channel Quality Indicator (CQI), A method, characterized in that the CQI is determined based on the first information and / or the second information.

26. At the base station, One or more transmitters and receivers; one or more processors; and One or more memories connected to said one or more processors and storing instructions, A base station, characterized in that the instructions, based on being executed by the one or more processors, cause the base station to perform all steps of the method according to claims 22 to 25.

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