Method and apparatus for reporting channel state information

Abstract

Methods according to embodiments of the present disclosure include receiving channel state information-reference signals (CSI-RS) and reporting channel state information (CSI). The CSI includes i) first information related to channel prediction and ii) second information related to interference prediction.

Description

Technical Field

[0001] This disclosure relates to a method and apparatus for reporting channel state information. Background Technology

[0002] Mobile communication systems have been developed to provide voice services while ensuring user activity. However, the scope of mobile communication systems extends beyond voice to include data services. Due to the current explosive growth in traffic and insufficient resources, users require faster services. Therefore, a more advanced mobile communication system is needed.

[0003] The requirements for next-generation mobile communication systems necessitate adaptability to explosive growth in data traffic, a dramatic increase in data rates per user, a significant increase in the number of connected devices, very low end-to-end latency, and high energy efficiency. To this end, various technologies have been investigated, such as dual connectivity, massive MIMO, in-band full-duplex, non-orthogonal multiple access (NOMA), ultra-wideband support, and device networking.

[0004] The operation for reporting Channel State Information (CSI) is defined. For example, CSI can be reported based on the Channel State Information-Reference Signal (CSI-RS). This CSI may include a Channel Quality Indicator (CQI). Summary of the Invention

[0005] [Technical Issues]

[0006] Based on the prediction operation of Channel State Information (CSI), CSI can be predicted based on one or more historical CSIs.

[0007] In existing CSI-related prediction operations, a model related to channel prediction (a single model) is used. If only channel prediction is considered when performing CSI reporting, the accuracy of CSI-based operations may be reduced.

[0008] For example, a channel quality indicator (CQI) determined solely based on predicted channel information without considering interference may not be the most suitable CQI for channel conditions that include interference.

[0009] For example, if the reported CQI differs from the previously reported CQI, the base station may have difficulty determining whether the channel power or the interference power has changed.

[0010] The purpose of this invention is to provide a method for solving the above-mentioned problems.

[0011] The technical objectives to be achieved by this disclosure are not limited to those described above by way of example only, and other technical objectives not mentioned will be clearly understood by those skilled in the art from the following description.

[0012] [Technical Solution]

[0013] The method according to embodiments of this disclosure includes: receiving a channel state information-reference signal (CSI-RS) and reporting channel state information (CSI).

[0014] CSI includes: i) first information related to channel prediction, and ii) second information related to interference prediction.

[0015] CSI may include a Channel Quality Indicator (CQI). The CQI can be determined based on first information and second information.

[0016] The second piece of information can be predicted based on historical disturbance measurements.

[0017] Historical interference measurements can be based on one or more historical interference measurements within a time period.

[0018] The second piece of information can be predicted based on historical CQI and / or historical signal-to-interference-noise ratio (SINR).

[0019] The method may also include transmitting information related to interference and information related to the original channel.

[0020] The method may also include transmitting i) signal-to-noise ratio (SNR) or reference signal received power (RSRP), and ii) interference-to-noise ratio (INR).

[0021] The method may also include receiving configuration information associated with CSI. The configuration information may include information related to the reporting of the first information and / or the reporting of the second information.

[0022] CSI may include first information and / or second information based on configuration information.

[0023] CSI may include a Channel Quality Indicator (CQI). The CQI may be determined based on first information and / or second information.

[0024] CSI may include a precoded matrix indicator (PMI). The PMI may be determined based on first information and / or second information.

[0025] PMI can be determined based on channel state. Channel state can be predicted based on first information and / or second information.

[0026] CSI can include a Channel Quality Indicator (CQI). The CQI can be determined based on the PMI and second information.

[0027] The second piece of information can be determined based on the most recently measured interference to noise ratio (INR).

[0028] The second piece of information can be determined based on the average of one or more interference measurements over a period of time.

[0029] The first and second information can be related to the first and second models.

[0030] Historical disturbance measurements can be correlated with the input of the second model. Secondary information can be correlated with the output of the second model.

[0031] The model may include a user equipment (UE) side model.

[0032] A user equipment 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.

[0033] The instructions are based on execution by the one or more processors, causing the user equipment to perform all the steps according to any one of the methods.

[0034] An apparatus according to another embodiment of this disclosure includes: one or more memories, and one or more processors connected to the one or more memories. The one or more memories store instructions, which, based on execution by the one or more processors, allow the apparatus to perform all steps according to any of the methods.

[0035] A non-transitory computer-readable medium storage instruction according to another embodiment of this disclosure. The instruction may be executed by one or more processors, causing a user device to perform all the steps of any of the methods.

[0036] A method according to another embodiment of this disclosure includes: transmitting channel state information-reference signal (CSI-RS) and receiving channel state information (CSI).

[0037] CSI includes: i) first information related to channel prediction, and ii) second information related to interference prediction.

[0038] The method may also include: transmitting configuration information associated with CSI. The configuration information may include information related to the reporting of the first information and / or the reporting of the second information.

[0039] CSI may include first information and / or second information based on configuration information.

[0040] CSI may include a Channel Quality Indicator (CQI). The CQI may be determined based on first information and / or second information.

[0041] A base station according to another embodiment of this disclosure includes: one or more transceivers, one or more processors, and one or more memories, the one or more memories being connected to the one or more processors and storing instructions.

[0042] The instructions are based on execution by one or more processors, causing the user device to perform all the steps according to any one of the methods.

[0043] [Beneficial Effects]

[0044] According to embodiments of this disclosure, the accuracy of operations based on Channel State Information (CSI) can be prevented from being reduced by predicting CSI based on interference-related information. Therefore, when performing downlink scheduling based on the reported CSI, the base station can take interference information into account during downlink scheduling.

[0045] According to embodiments of this disclosure, the CSI includes a Channel Quality Indicator (CQI). The CQI is determined based on interference-related information. Therefore, the base station can perform downlink scheduling by taking into account the CQI, which incorporates interference information.

[0046] As described above, according to embodiments of this disclosure, a base station can improve the reliability of downlink transmission and reception processes by taking interference information into account to perform downlink scheduling best suited to the current channel. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating an example of a process associated with CSI.

[0048] Figure 2 The functional framework of the AI / ML model is illustrated.

[0049] Figure 3 An example of CSI prediction based on an AI / ML model is illustrated.

[0050] Figure 4 This is a flowchart illustrating an example of signaling based on a method according to at least one embodiment of the present disclosure.

[0051] Figure 5 This is a flowchart illustrating a method according to an embodiment of the present disclosure.

[0052] Figure 6 This is a flowchart illustrating a method according to another embodiment of the present disclosure.

[0053] Figure 7 The illustration shows the configuration of a first device and a second device according to an embodiment of the present disclosure.

[0054] Figure 8 An AI device according to an embodiment of the present disclosure is illustrated. Detailed Implementation

[0055] The following will be related to the appendix Figure 1 The detailed description disclosed herein is intended to describe exemplary embodiments of the present disclosure and not to describe unique embodiments for carrying out the present disclosure. The following detailed description includes details to provide a complete understanding of the present disclosure. However, those skilled in the art will recognize that the present disclosure can be carried out without these details.

[0056] In some cases, to prevent the concepts of this disclosure from being implicitly disclosed, known structures and devices may be omitted or illustrated in block diagram format based on the core functions of each structure and device.

[0057] In the following text, 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 the downlink, the transmitter can be part of the base station, and the receiver can be part of the terminal. In the uplink, the transmitter can be part of the terminal, and the receiver can be part of the terminal. A base station can be referred to as a first communication device, and a terminal can be referred to as a second communication device. A base station (BS) can be replaced by terms including: fixed station, Node B, evolved Node B (eNB), next-generation Node B (gNB), base transceiver system (BTS), access point (AP), network (5G network), AI system, roadside unit (RSU), vehicle, robot, unmanned aerial vehicle (UAV), AR (augmented reality) device, VR (virtual reality) device, etc. Furthermore, the terminal can be fixed or mobile, and can be replaced by terms including User Equipment (UE), Mobile Station (MS), User Terminal (UT), Mobile Subscriber Station (MSS), Subscriber Station (SS), Advanced Mobile Station (AMS), Wireless Terminal (WT), Machine Type Communication (MTC) equipment, Machine-to-Machine (M2M) equipment and Device-to-Device (D2D) equipment, vehicle, robot, AI module, Unmanned Aerial Vehicle (UAV), AR (Augmented Reality) equipment, VR (Virtual Reality) equipment, etc.

[0058] <Operations related to CSI>

[0059] In NR (New Radio) systems, CSI-RS (Channel State Information - Reference Signal) is used for time and / or frequency tracking, CSI calculation, L1 (Layer 1)-RSRP (Reference Signal Received Power) calculation, and mobility processing. In this case, CSI calculation is associated with CSI acquisition, and L1-RSRP calculation is associated with BM (Beam Management).

[0060] CSI (Channel State Information) generally refers to information that can show the quality of the radio channel (or link) formed between the terminal and the antenna port.

[0061] The base station can transmit CSI-RS to the UE (User Equipment) to determine the channel characteristics of the downlink, and can receive feedback from the UE on the CSI-RS-based channel measurement results.

[0062] CSI-RS can be configured for one or more UEs. A different CSI-RS configuration can be provided for each UE, and the same CSI-RS configuration can be provided to multiple UEs. CSI-RS can support up to 32 antenna ports. A CSI-RS corresponding to N antenna ports (where N is 1 or greater) can be mapped to N RE locations within a time-frequency cell corresponding to one timeslot and one RB. When N is 2 or greater, N-port CSI-RS can be multiplexed using CDM, FDM, and / or TDM schemes. A 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 timeslots; or eight antenna ports (CDM8), distinguished based on code resources on the same two adjacent subcarriers and the same four adjacent OFDM timeslots. If multiple CDM groups exist, these CDM groups may not be mapped to adjacent subcarriers and / or adjacent OFDM symbols. CSI-RS antenna ports can be indexed in the order of CDM group, frequency domain, and time domain. CSI-RS can be mapped to the remaining REs except for those mapped to CORESET, DMRS, and SSB.

[0063] 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 in every RB within the configured bandwidth (i.e., density = 1), or CSI-RS can be transmitted every other RB (e.g., even-numbered or odd-numbered RBs) (i.e., density = 1 / 2). When CSI-RS is used as a tracking reference signal (TRS), a single-port CSI-RS can be mapped to three subcarriers in each resource block (i.e., density = 3).

[0064] One or more CSI-RS resource sets can be configured for a UE in the time domain. Each CSI-RS resource set can include one or more CSI-RS configurations.

[0065] Each CSI-RS resource set can be configured as periodic, semi-persistent, or aperiodic. In the case of a periodic CSI-RS resource set, the period can be configured to 4 or more time slots and 640 or fewer time slots. Furthermore, the starting offset value for the periodic CSI-RS resource set can be configured. In the case of a semi-persistent CSI-RS resource set, the offset and period of the CSI-RS resource set candidates can be configured. Here, actual CSI-RS transmissions can be activated / deactivated based on the MAC control unit (CE). When a CSI-RS resource set is activated, CSI-RS transmissions can be performed according to the configured offset and period until it is deactivated. When a CSI-RS resource set is deactivated, CSI-RS transmissions can be suspended until it is explicitly reactivated. In the case of aperiodic CSI-RS transmissions, information about each CSI-RS resource set can be explicitly provided via the DCI.

[0066] CSI-IM resources can be configured for UE interference measurement (IM). A CSI-IM resource can include four REs within a time slot and a resource block. These four REs can correspond to two consecutive OFDM symbols and two consecutive subcarriers, or they can correspond to one OFDM symbol and four consecutive subcarriers. The position of the CSI-IM REs in the frequency domain can be determined by the CSI-IM configuration. In the time domain, the CSI-IM resource set can 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 transmission may be performed in neighboring cells. In this way, the CSI-IM resource can be configured for UE as Zero Power (ZP)-CSI-RS.

[0067] ZP-CSI-RS can be configured to be distinct from Non-Zero Power (NZP)-CSI-RS. When scheduling PDSCH on resources including CSI-RS REs, the first UE can assume that the PDSCH is not mapped to that CSI-RS RE because rate matching considering that CSI-RS RE is applied to the corresponding PDSCH. Here, CSI-RS can be configured for the first UE or for the second UE. In this case, the CSI-RS for the first UE can be configured as NZP-CSI-RS for the first UE, and the NZP-CSI-RS resource set can be configured for the first UE. Simultaneously, the CSI-RS for the second UE can be configured as ZP-CSI-RS for the first UE, and the ZP-CSI-RS resource set can be configured for the first UE. The NZP-CSI-RS resource set can be used for the corresponding UE's CSI report configuration. The NZP-CSI-RS resource set can be associated with either CSI-RS or SSB. In addition, multiple periodic NZP-CSI-RS resource sets can be configured as TRS resource sets.

[0068] Figure 1 This is a flowchart illustrating an example of a process associated with CSI.

[0069] The terminal (e.g., UE (User Equipment)) receives configuration information associated with CSI from the base station (e.g., gNB (Next Generation Node B)) via RRC (Radio Resource Control) signaling (S110).

[0070] Configuration information associated with CSI may include at least one of the following: information associated with CSI-IM (Interference Management) resources, information associated with CSI measurement configuration, information associated with resource configuration, information associated with CSI-RS resources, or information associated with CSI reporting configuration.

[0071] i) Information associated with CSI-IM resources may include CSI-IM resource information and CSI-IM resource set information. A CSI-IM resource set is identified by a CSI-IM resource set ID (identifier), and a resource set may include at least one CSI-IM resource. Each CSI-IM resource is identified by a CSI-IM resource ID.

[0072] ii) Information associated with CSI resource configuration can be represented as a CSI-ResourceConfig IE. The information associated with CSI resource configuration defines a group that includes at least one of the NZP (Non-Zero Power) CSI-RS resource sets, CSI-IM resource sets, or CSI-SSB resource sets. That is, the information associated with CSI resource configuration includes a list of CSI-RS resource sets, and this list may include at least one of the NZP CSI-RS resource set list, CSI-IM resource set list, or CSI-SSB resource set list. CSI-RS resource sets are identified by a CSI-RS resource set ID, and a resource set includes at least one CSI-RS resource. Each CSI-RS resource is identified by a CSI-RS resource ID.

[0073] Table 1 shows an example of an NZP CSI-RS resource set IE. As shown in Table 1, parameters indicating the purpose of the CSI-RS can be configured for each NZP CSI-RS resource set (e.g., the 'repetition' parameter associated with BM and the 'trs-Info' parameter associated with tracking).

[0074] Table 1

[0075] In addition, the repetition parameter corresponding to the higher-level parameter corresponds to the 'CSI-RS-ResourceRep' parameter of the L1 parameter.

[0076] iii) Information associated with CSI reporting configuration includes: the report configuration type parameter (reportConfigType) indicating the time-domain behavior, and the report quantity parameter (reportQuantity) indicating the number of reports associated with CSI. This time-domain behavior can be periodic, non-periodic, or semi-persistent.

[0077] Information associated with CSI report configuration can be represented as CSI-ReportConfig IE, and Table 2 below shows an example of CSI-ReportConfig IE.

[0078] Table 2

[0079] - The terminal measures CSI based on the configuration information associated with CSI (S120).

[0080] CSI measurement may include: (1) the terminal’s CSI-RS receiving process (S121), and (2) the calculation of CSI by the received CSI-RS (S122), which will be described in detail below.

[0081] CSI-RS is configured to map to CSI-RS resources on REs (resource units) in the time and frequency domains via the higher-level parameter CSI-RS-ResourceMapping.

[0082] Table 3 shows an example of CSI-RS-ResourceMapping IE.

[0083] Table 3

[0084] In Table 3, density (D) is the density of CSI-RS resources measured in RE / port / PRB (physical resource block), and nrofPorts is the number of antenna ports.

[0085] - The terminal reports the measured CSI to the base station (S130).

[0086] In this case, when the number of CSI-ReportConfig entries in Table 2 is configured as 'none' (or 'Noreport'), the terminal can omit reporting.

[0087] However, even if the number is configured as 'none' (or no report), the terminal can still report to the base station.

[0088] When this quantity is configured as 'none', it means that an aperiodic TRS is triggered or repetition is set.

[0089] In this case, terminal reports can only be omitted when repetition is configured as 'ON'.

[0090] CSI measurement

[0091] The NR system supports more flexible and dynamic CSI measurement and reporting. In this case, CSI measurement can include the process of receiving CSI-RS and obtaining CSI by calculating the received CSI-RS.

[0092] For the time-domain behavior of CSI measurements and reporting, aperiodic / semi-persistent / periodic CM (channel measurements) and IM (interference measurements) are supported. To configure CSI-IM, use the 4-port NZP CSI-RS RE pattern.

[0093] The NR CSI-IM-based IMR has a similar design to the LTE CSI-IM and is configured independently of the ZP CSI-RS resources used for PDSCH rate matching. Furthermore, on each port in the NZP CSI-RS-based IMR, an interference layer with (preferred channels and) precoded NZP CSI-RS is simulated. This is an intra-cell interference measurement for multi-user scenarios and is typically aimed at MU interference.

[0094] The base station transmits pre-coded NZP CSI-RS to the terminal on each port of the configured NZP CSI-RS-based IMR.

[0095] The terminal assumes that resources are centrally located at the channel / interference layer of each port and measures the interference.

[0096] For channels, when there is no PMI and RI feedback, several resources are configured in a set, and the base station or network indicates a subset of NZP CSI-RS resources for channel / interference measurement via DCI.

[0097] The following section provides a more detailed description of resource settings and configuration.

[0098] Resource settings

[0099] Each CSI resource setting 'CSI-ResourceConfig' includes the configuration of S ≥ 1 CSI resource sets (given by the higher-level parameter csi-RS-ResourceSetList). The CSI resource setting corresponds to CSI-RS-resourcesetlist. Here, S represents the number of configured CSI-RS resource sets. The list of S ≥ 1 CSI resource sets includes any one or both of the NZP CSI-RS resource sets and the SS / PBCH block (SSB) sets used for L1-RSRP computation, or includes the CSI-IM resource set.

[0100] Each CSI resource setting is located at the DL BWP (bandwidth portion), identified by the higher-level parameter bwp-id. Furthermore, all CSI resource settings linked to the CSI reporting setting have the same DL BWP.

[0101] The temporal behavior of CSI-RS resources included in the CSI resource settings within the CSI-ResourceConfig IE is indicated by the higher-level parameter resourceType and can be configured as aperiodic, periodic, or semi-persistent. For periodic and semi-persistent CSI resource settings, the number (S) of configured CSI-RS resource sets is limited to '1'. For periodic and semi-persistent CSI resource settings, the configured period and slot offset are given by the parameter set (numerology) of the associated DL BWP given by bwp-id.

[0102] When a UE is configured with several CSI-ResourceConfigs that include the same NZP CSI-RS resource ID, the same time-domain behavior is configured for that CSI-ResourceConfig.

[0103] When a UE is configured with several CSI-ResourceConfigs that include the same CSI-IM resource ID, the same time-domain behavior is configured for that CSI-ResourceConfig.

[0104] Next, configure one or more CSI resource settings for channel measurement (CM) and interference measurement (IM) via higher-level signaling.

[0105] - CSI-IM resources for interference measurement.

[0106] - NZP CSI-RS resources for interference measurement.

[0107] - NZP CSI-RS resources for channel measurements.

[0108] That is, CMR (Channel Measurement Resource) can be NZP CSI-RS for CSI acquisition, and IMR (Interference Measurement Resource) can be NZP CSI-RS for CSI-IM and IM.

[0109] In this context, CSI-IM (or ZP CSI-RS for IM) is typically used for inter-cell interference measurements.

[0110] In addition, the NZP CSI-RS for IM is typically used for intra-cell interference measurements from multiple users.

[0111] The UE may assume that the CSI-RS resource configured for channel measurements for a CSI report and the CSI-IM / NZP CSI-RS resource configured for interference measurements (when using NZP CSI-RS resources for interference measurements) have a QCL relationship relative to 'QCL-TypeD' for each resource.

[0112] Resource settings configuration

[0113] As mentioned above, resource settings can refer to a list of resource sets.

[0114] For aperiodic CSI, each trigger state configured using the higher-level parameter CSI-AperiodicTriggerState is associated with one or more CSI-ReportConfigs, and each CSI-ReportConfig is linked to a periodic, semi-persistent, or aperiodic resource setting.

[0115] A report setting can be linked to up to three resource settings.

[0116] - When a resource setting is configured, the corresponding resource setting (given by the higher-level parameter resourcesForChannelMeasurement) is used for channel measurements in L1-RSRP calculation, or for channel and interference measurements in L1-SINR calculation.

[0117] - When two resource settings are configured, the first resource setting (given by the higher-level parameter resourcesForChannelMeasurement) is used for channel measurements, and the second resource setting (given by csi-IM-ResourcesForInterference or nzp-CSI-RS-ResourcesForInterference) is used for interference measurements performed on CSI-IM or NZP CSI-RS.

[0118] - When three resource settings are configured, the first resource setting (given by resourcesForChannelMeasurement) is used for channel measurements, the second resource setting (given by csi-IM-ResourcesForInterference) is used for CSI-IM-based interference measurements, and the third resource setting (given by nzp-CSI-RS-ResourcesForInterference) is used for NZP CSI-RS-based interference measurements.

[0119] For semi-persistent or periodic CSI, each CSI-ReportConfig is linked to a periodic or semi-persistent resource setting.

[0120] - When a resource setting is configured (given by resourcesForChannelMeasurement), that resource setting is used for channel measurements in L1-RSRP calculation, or for channel and interference measurements in L1-SINR calculation.

[0121] - When two resource settings are configured, the first resource setting (given by resourcesForChannelMeasurement) is used for channel measurements, and the second resource setting (given by the higher-level parameter csi-IM-ResourcesForInterference or nzp-CSI-RS-ResourcesForInterference) is used for interference measurements performed on CSI-IM or NZP CSI-RS.

[0122] CSI Calculation

[0123] When interference measurements are performed on CSI-IM, each CSI-RS resource used for channel measurements is associated with a CSI-IM resource through a sequence of CSI-RS and CSI-IM resources in the corresponding resource set. The number of CSI-RS resources used for channel measurements is the same as the number of CSI-IM resources.

[0124] Furthermore, when performing interference measurements on NZP CSI-RS, the UE does not expect to set one or more NZP CSI-RS resources in the resource set used for channel measurements.

[0125] When the higher-level parameter nzp-CSI-RS-ResourcesForInterference is set, the terminal does not expect to configure more than 18 NZP CSI-RS ports in one NZP CSI-RS resource set.

[0126] For CSI measurements other than L1-SINR, the terminal assumptions are as follows.

[0127] - Each NZP CSI-RS port configured for interference measurement corresponds to one interference transmission layer.

[0128] - All interference transmission layers of the NZP CSI-RS port used for interference measurement take into account the relevant EPRE (Energy Per Resource Unit) ratio.

[0129] - Another interference signal, either the NZP CSI-RS resource used for interference measurement, or the CSI-IM resource used for interference measurement on the RE of the NZPCSI-RS resource used for channel measurement.

[0130] CSI Report

[0131] The UE can measure channel characteristics based on CSI-RS and therefore can send CSI reports back to the base station. For this purpose, CSI report configurations can be provided for the UE. Each CSI report configuration can include configurations for feedback type, measurement resources, report type, etc.

[0132] Feedback types may include Channel Quality Indicator (CQI), Precoding Matrix Indicator (PMI), CSI-RS Resource Indicator (CRI), SSB Resource Block Indicator (SSBRI), Layer Indicator (LI), Rank Indicator (RI), L1-Reference Signal Received Power (RSRP), etc.

[0133] Measurement resources may include the configuration of downlink signals and / or downlink resources used by the UE to perform measurements to determine feedback information. Measurement resources may be configured as ZP and / or NZP CSI-RS resource sets associated with the CSI reporting configuration. The NZP CSI-RS resource set may include a CSI-RS set or an SSB set. For example, L1-RSRP may be measured against a CSI-RS set, or measurements may be performed against an SSB set.

[0134] Report types can include the timing of the UE reporting and configurations such as the uplink channel. Reporting points can be configured as periodic, semi-permanent, or aperiodic. Periodic CSI reports can be transmitted on the PUCCH. Semi-permanent CSI reports can be transmitted on the PUCCH or PUSCH based on an indication of an activated / deactivated MAC CE. Aperiodic CSI reports can be indicated by DCI signaling. For example, the uplink-granted CSI request field can indicate one of various report trigger sizes. Aperiodic CSI reports can be transmitted on the PUSCH.

[0135] CSI can be defined in two types. Type 1 CSI can address the scenario where a single user is scheduled, while Type 2 CSI can address the scenario where multiple users are scheduled simultaneously on the same resource. Type 1 CSI can include single-panel CSI and multi-panel CSI, each of which can correspond to a different codebook. The precoding matrix on the codebook can be specified as a combination of w1 and w2. Long-term and wide-band characteristics can correspond to w1, and short-term and subband characteristics can correspond to w2. Type 1 CSI can report a precoding matrix selected by the UE, while Type 2 CSI can report information on the size and phase of up to four beams.

[0136] For CSI reports, the time and frequency resources available to the UE are controlled by the base station.

[0137] CSI (Channel State Information) may include at least one of the following: CQI (Channel Quality Indicator), PMI (Precoding Matrix Indicator), CRI (CSI-RS Resource Indicator), SSBRI (SS / PBCH Block Resource Indicator), LI (Layer Indicator), RI (Rank Indicator), L1-RSRP, and / or L1-SINR.

[0138] For CQI, PMI, CRI, SSBRI, LI, RI, and L1-RSRP, the terminal is configured via a higher layer to have N ≥ 1 CSI-ReportConfig settings, M ≥ 1 CSI-ResourceConfig settings, and a list of one or more trigger states (provided by CSI-aperiodicTriggerStateList and CSI-semiPersistentOnPUSCH-TriggerStateList). Each trigger state includes an associated CSI-ReportConfig list, which selectively indicates the resource set IDs of the channel and interference in the CSI-aperiodicTriggerStateList. Each trigger state includes an associated CSI-ReportConfig in the CSI-semiPersistentOnPUSCH-TriggerStateList.

[0139] Furthermore, the time-domain behavior of the CSI report supports periodicity, semi-persistence, and aperiodicity.

[0140] i) Periodic CSI reporting is performed on both short and long PUCCH. The period and slot offset of periodic CSI reporting can be configured via RRC and involve the CSI-ReportConfig IE.

[0141] ii) SP (semi-periodic) CSI reports are executed on short PUCCH, long PUCCH, or PUSCH.

[0142] When SP CSI is on short / long PUCCH, the period and slot offset are set via RRC, and CSI reporting for separate MACCE / DCI is activated / deactivated.

[0143] When SP CSI is on the PUSCH, the SP CSI reporting period is configured via RRC, but the slot offset is not configured via RRC, and the SP CSI reporting is activated / deactivated by DCI (format 0_1). For SP CSI reporting on the PUSCH, separate RNTIs (SP-CSI C-RNTI) are used.

[0144] The initial CSI report timing follows the PUSCH time domain allocation value indicated in the DCI, and subsequent CSI report timing follows the period configured as RRC.

[0145] DCI format 0_1 ​​includes a CSI request field and can activate / deactivate SP-CSI trigger states for specific configurations. SP CSI reports have the same or similar activation / deactivation mechanism as those used for data transmission on the SPS PUSCH.

[0146] iii) Non-periodic CSI reporting is performed on the PUSCH and triggered by the DCI. In this case, information related to the triggering of non-periodic CSI reporting can be transmitted / indicated / configured via MAC-CE.

[0147] For AP CSI with AP CSI-RS, the timing of AP CSI-RS is configured by RRC, and the timing of AP CSI reports is dynamically controlled by DCI.

[0148] NR does not employ the method already used for PUCCH-based CSI reporting, which involves separately reporting CSI across several report instances (e.g., sequential transmission by RI, WB PMI / CQI, and SB PMI / CQI). Instead, NR restricts specific CSI reports from being configured on short / long PUCCHs and defines CSI omission rules. Furthermore, regarding AP CSI report timing, the PUSCH symbol / slot position is dynamically indicated by DCI. Additionally, candidate slot offsets are configured by RRC. For CSI reports, a slot offset (Y) is configured for each report. For UL-SCH, slot offset K2 is configured separately.

[0149] Regarding CSI computational complexity, two CSI latency classes (low-latency and high-latency) are defined. Low-latency CSI is WB CSI with a maximum of 4-port Type-I codebook, or 4-port non-PMI feedback CSI. High-latency CSI refers to another CSI besides low-latency CSI. For a normal terminal, (Z, Z') is defined in OFDM symbols. In this case, Z is the minimum CSI processing time from receiving an aperiodic CSI trigger DCI until executing a CSI report. Furthermore, Z' is the minimum CSI processing time from receiving a CSI-RS for channel / interference until executing a CSI report.

[0150] In addition, the terminal report can calculate the number of CSIs simultaneously.

[0151] <Description related to AIML>

[0152] Advances in artificial intelligence (AI) and machine learning (ML) technologies are driving the nodes and terminals that make up wireless communication networks toward intelligence and sophistication.

[0153] The following is for reference. Figure 2 Describe the functional framework for AI operations.

[0154] Figure 2 The functional framework of the AI / ML model is illustrated.

[0155] To explain AI (or AI / ML) more specifically, the following terms can be defined.

[0156] - Data collection: Data collected from network nodes, management entities, or user units (UEs) serves as the basis for AI model training, data analysis, and inference.

[0157] - AI Model: A data-driven algorithm that uses AI technology to generate an output set that includes predictive information and / or decision parameters based on an input set.

[0158] - AI / ML Training: An online or offline process for training an AI model by learning the features and patterns that best represent data, and obtaining a trained AI / ML model for inference.

[0159] - AI / ML inference: The process of making predictions or inferring decisions based on data and AI models collected using trained AI models.

[0160] refer to Figure 2 The data collection function 10 is a function that collects input data and provides the processed input data to the model training function 20 and the model inference function 30.

[0161] Examples of input data may include measurements from the UE or other network entities, feedback from actors, and outputs from AI models.

[0162] Data collection function 10 performs data preparation based on input data and provides processed input data through data preparation. Data collection function 10 does not perform specific data preparation (e.g., data preprocessing and cleaning, shaping and transformation) for each AI algorithm, but can perform general data preparation for AI algorithms.

[0163] After performing the data preparation process, the data collection function 10 provides training data 11 to the model training function 20 and inference data 12 to the model inference function 30. Training data 11 is the data required as input to the AI ​​model training function 20. Inference data 12 is the data required as input to the AI ​​model inference function 30.

[0164] The data collection function 10 can be performed by a single entity (e.g., UE, RAN node, network node, etc.) or by multiple entities. In this case, training data 11 and inference data 12 can be provided to the model training function 20 and the model inference function 30 from multiple entities, respectively.

[0165] The model training function 20 is responsible for performing AI model training, validation, and testing, and can generate model performance metrics as part of the AI ​​model testing process. If necessary, the model training function 20 is also responsible for data preparation (e.g., data preprocessing and cleaning, shaping, and transformation) based on the training data 11 provided by the data collection function 10.

[0166] Model Deployment / Update 13 is used to initially deploy a trained, validated, and tested AI model to the model inference function 30, or to provide an updated model to the model inference function 30.

[0167] 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 can provide model performance feedback 14 to the model training function 20. In addition, if necessary, the model inference function 30 is also responsible for data preparation (e.g., data preprocessing and cleaning, shaping and transformation) based on the inference data 12 provided by the data collection function 10.

[0168] 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.

[0169] If available, model performance feedback 14 can be used to monitor the performance of the AI ​​model, and this feedback can be omitted.

[0170] The actor function 40 is a function that receives the output 16 from the model inference function 30 and triggers or executes the corresponding task / action. The actor function 40 can trigger the task / action for other entities (e.g., one or more UEs, one or more RAN nodes, one or more network nodes, etc.) or for itself.

[0171] Feedback 15 can be used to derive training data 11 and inference data 12, or to monitor the performance of the AI ​​model and its impact on the network.

[0172] The definitions of training, validation, and testing datasets used in AI / ML can be distinguished as follows: - Training data: This refers to the dataset used to learn the model.

[0173] - Validation data: This refers to the dataset used to validate the learned model. In other words, it is the dataset typically used to prevent overfitting of the training dataset.

[0174] Validation data also refers to a dataset used to select the optimal model among various models during the learning process. Therefore, validation data can be regarded as a type of learning.

[0175] - Test data: it refers to a dataset used for final evaluation. This data is irrelevant to learning.

[0176] In a dataset, if the training set is generally divided, within the entire training set, the training data and validation data can be divided and used in a ratio of 8:2 or 7:3, and if test data is included, the dataset can be divided and used in a ratio of 6:2:2 (training: validation: test).

[0177] Figure 2 The above functions illustrated in the drawings can be implemented in a RAN node (e.g., base station, TRP, central unit (CU) of a base station, etc.), a network node, the Operations, Administration and Maintenance (OAM) of a network operator, or a UE.

[0178] Alternatively, Figure 2 the functions illustrated can be implemented through cooperation between two or more entities among the RAN, network nodes, OAM of a network operator, or UE. For example, one entity may execute Figure 2 a part of the functions, and another entity may execute the remaining functions. Thus, when Figure 2 a part of the illustrated functions are executed by a single entity (e.g., UE, RAN node, network node, etc.), the delivery / provision of data / information between corresponding functions is omitted. For example, if the model training function 20 and the model inference function 30 are executed by the same entity, the delivery / provision of model deployment / update 13 and model performance feedback 14 can be omitted.

[0179] Alternatively, Figure 2 any of the illustrated functions can be executed through cooperation between two or more entities among the RAN, network nodes, OAM of a network operator, or UE. This may be referred to as segmented AI operation.

[0180] <AI model training function (network node) - AI model inference function (RAN node)>

[0181] The following describes the operation when the AI model training function is executed by a network node (e.g., core network node, OAM of a network operator, etc.) and the AI model inference function is executed by a RAN node (e.g., base station, TRP, CU of a base station, etc.).

[0182] Step 1: RAN node 1 and RAN node 2 transmit input data for AI model training (i.e., training data) to a network node. RAN node 1 and RAN node 2 may transmit data collected from UEs (e.g., UE measurements related to RSRP, RSRQ and SINR of the serving cell and neighboring cells, UE position, speed, etc.) together to the network node.

[0183] Step 2: The network node trains the AI model using the received training data.

[0184] Step 3: The network node deploys / updates the AI model to RAN node 1 and / or RAN node 2. RAN node 1 (and / or RAN node 2) may continue to perform model training based on the received AI model.

[0185] For ease of explanation, it is assumed that the AI model is only deployed / updated to RAN node 1.

[0186] Step 4: RAN node 1 receives input data for AI model inference (i.e., inference data) from the UE and RAN node 2.

[0187] Step 5: RAN node 1 performs AI model inference using the received inference data to generate output data (e.g., prediction or decision).

[0188] Step 6: If applicable, RAN node 1 may transmit model performance feedback to the network node.

[0189] Step 7: RAN node 1, RAN node 2 and the UE (or "RAN node 1 and the UE" or "RAN node 1 and RAN node 2") perform an action based on the output data. For example, in a load balancing operation, the UE may be moved from RAN node 1 to RAN node 2.

[0190] Step 8: RAN node 1 and RAN node 2 transmit feedback information to the network node.

[0191] <AI model training / inference function: RAN node>

[0192] The following describes the operations when the AI model training function and the AI model inference function are performed by a RAN node (e.g., base station, TRP, CU of a base station, etc.).

[0193] Step 1: The UE and RAN node 2 transmit input data for AI model training (i.e., training data) to RAN node 1.

[0194] Step 2: RAN node 1 uses the received training data to train the AI model.

[0195] Step 3: The RAN node 1 receives input data for AI model inference (i.e., inference data) from the UE and the RAN node 2.

[0196] Step 4: The RAN node 1 performs AI model inference using the received inference data to generate output data (e.g., a prediction or a decision).

[0197] Step 5: The RAN node 1, the RAN node 2 and the UE (or "the RAN node 1 and the UE" or "the RAN node 1 and the RAN node 2") perform an action based on the output data. For example, in a load balancing operation, the UE may be moved from the RAN node 1 to the RAN node 2.

[0198] Step 6: The RAN node 2 transmits feedback information to the RAN node 1.

[0199] <AI model training function (RAN node) - AI model inference function (UE)>

[0200] The following describes operations when the AI model training function is performed by a RAN node (e.g., a base station, a TRP, a CU of a base station, etc.) and the AI model inference function is performed by a UE.

[0201] Step 1: The UE transmits input data for AI model training (i.e., training data) to the RAN node. The RAN node may collect data from various UEs and / or other RAN nodes (e.g., UE measurements related to RSRP, RSRQ and SINR of a serving cell and neighboring cells, UE positions, speeds, etc.).

[0202] Step 2: The RAN node trains an AI model using the received training data.

[0203] Step 3: The RAN node deploys / updates the AI model to the UE. The UE may continue to perform model training based on the received AI model.

[0204] Step 4: The UE receives input data for AI model inference (i.e., inference data) from the RAN node (and / or from another UE).

[0205] Step 5: The UE performs AI model inference using the received inference data to generate output data (e.g., a prediction or a decision).

[0206] Step 6: If applicable, the UE may transmit model performance feedback to the RAN node.

[0207] Step 7: The UE and the RAN node perform an action based on the output data.

[0208] Step 8: The UE transmits feedback information to the RAN node.

[0209] In this disclosure, ' / ' means 'and', 'or', or 'and / or' depending on the context.

[0210] In Release 18, research is underway on CSI compression based on a two-sided model and CSI prediction based on a UE-side model for AI / ML. See below for reference. Figure 3 Describe in detail the use cases of using AI / ML for CSI prediction.

[0211] Figure 3 The illustration shows an example of CSI prediction based on an AI / ML model. Specifically, Figure 3 The illustration depicts a use case for estimating / predicting / determining single or multiple future CSIs. The model performing the model inference associated with this use case can be an artificial intelligence / machine learning (AI / ML) model included only on the UE side. For example, the type of model input / output can be a raw channel matrix or a precoder type (e.g., feature vector).

[0212] For example, CSI prediction can refer to the 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 model input can be the RSRP (e.g., the measured RSRP), and the model output can be the predicted RSRP (e.g., the predicted RSRP).

[0213] This disclosure proposes an enhanced reporting method to efficiently support the computation of Channel Quality Indicator (CQI) in AI / ML-based CSI prediction.

[0214] The enhanced reporting method proposed in this disclosure is described below.

[0215] In existing CSI prediction, incomplete CQI calculations are inevitable because only information about CSI is used in the prediction instance, without information about interference. This disclosure proposes a method to improve the ambiguity of Channel Measurement Resources (CMR) / Interference Measurement Resources (IMR). The method for resolving the ambiguity problem in CQI calculation in AI / ML-based CSI prediction is described below.

[0216] Proposal 1

[0217] In this disclosure, for ease of explanation, Model 1, Model 2, etc., may be defined as follows. However, 'Model 1' and 'Model 2' are used only to distinguish the objects (channel and interference) predicted by the models, and are not intended to limit the technical concepts of this disclosure to these terms.

[0218] - Model 1: Channel Prediction Model (CPM)

[0219] Model 2: Interference Prediction Model (IPM)

[0220] The node performing training / interference (e.g., UE) can predict CSI based on Model 1 and Model 2 (e.g., at least one parameter included in the CSI above, channel-related information and / or interference-related information).

[0221] The UE can output a predicted channel based on Model 1 for reporting using the following method.

[0222] For example, the UE can report a predicted channel based on historical channels using Model 1 output. 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 can be based on information representing the historical channel state. The predicted channel can be based on information representing the future channel state predicted based on the historical channel. For example, the historical / predicted channel can be based on information including at least one of the following: i) at least one parameter included in the existing CSI (e.g., CQI, PMI, RI, etc.), ii) signal-to-noise ratio (SNR), iii) the original channel matrix, and / or iv) eigenvectors.

[0223] For example, the information reported by the UE can vary depending on the input / output CSI type of Model 1.

[0224] For example, when the original channel matrix is ​​used as the input / output of CPM, the UE can report CSI based on explicit feedback.

[0225] For example, when a precoder type (e.g., feature vector, eigenvalue) is used as input / output of CPM, the UE can report CSI based on the implicit feedback of the existing NR.

[0226] The UE can output model 2-based predicted interference for reporting using the following methods.

[0227] For example, a UE can report predicted interference based on historical interference measurements using Model 2 output. 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 historical interference measurements. Predicted interference can be based on information related to future interference predicted based on historical interference measurements.

[0228] For example, based on the predicted interference output of Model 2 based on historical interference measurements, for the purpose of historical interference measurement, the measurement limit for a single measurement can be configured / restricted to always be on / enabled.

[0229] For example, based on the predicted interference output of Model 2 based on historical interference measurements, one or more specific measurement windows can be configured by the base station to perform effective interference measurements. In this case, the value obtained by filtering (e.g., averaging) the interference measurements within the measurement window can be used as the input to Model 2.

[0230] For example, a UE can report a predicted CQI / SINR based on historical CQI / Signal-to-Interference-Noise Ratio (SINR) using Model 2 output. Specifically, historical CQI / SINR is based on the input of Model 2, and predicted CQI / SINR is based on the output of Model 2. Historical CQI / SINR can be based on historical CQI and / or historical SINR. Predicted CQI / SINR can be based on future CQI and / or future SINR predicted based on historical CQI and / or historical SINR.

[0231] If both the model used for inferring the original channel and the model used for inferring interference are used together, calculating the predicted CQI corresponding to a specific reporting time within the prediction window becomes straightforward on the UE side. The CQI can be calculated based on SINR. For example, SINR can be expressed as... If only a single model is used to infer the raw channel information, the desired signal power |H|² can be calculated from the raw channel information, but the interference power |H|² cannot be obtained. I Therefore, the accuracy of the CQI determined / calculated from SINR can be improved by adding a model for inferring interference to calculate the interference power.

[0232] Unlike methods that predict interference via IPM, one approach could be to have the UE apply all interference power measured based on reference resources equally to the prediction window to calculate the CQI within the prediction window.

[0233] For example, the desired power is calculated based on the predicted original channel matrix, but the interference can be calculated by unifying it into a single pre-calculated value. In this case, to offset some of the effects caused by fluctuations in interference, a long-term measurement window can be configured, or the measurement limit can be "on" to calculate based on simple transient interference.

[0234] For example, one approach could be to have the base station signal correction values ​​used for calculating future interference or future CQI / SINR. Specifically, the UE could receive these correction values ​​from the base station. The UE could then use these correction values ​​to calculate / determine predicted CQI / SINR, etc.

[0235] For example, the base station can transmit information about the value δ to the UE. In this case, SINR can be... .

[0236] According to Proposal 1, the accuracy of CQI can be improved. Base stations can perform downlink scheduling by taking this CQI into account. Since DL scheduling can be performed most appropriately for the current channel, including interference, the reliability of DL transmission and reception processes can be improved.

[0237] Proposal 2

[0238] In UE-based AI / ML-based CSI prediction, the UE can calculate / report CSI using the following methods.

[0239] - Method 1: The UE can calculate / report CSI based on CPM. Specifically, CPM can be enabled only, and interference can be measured without prediction as before (e.g., for high SINR scenarios).

[0240] - Method 2: The UE can calculate / report CSI based on IPM. Specifically, IPM can be enabled only, and the channel can be measured without prediction as before (e.g., for low SINR scenarios).

[0241] - Method 3: The UE can calculate / report CSI based on CPM and IPM. Specifically, the UE can calculate / report CSI by enabling both CPM and IPM and predicting channel and interference.

[0242] For example, when the UE calculates / reports CSI, the UE can work with the base station / NW to determine which of methods 1 through 3 is used to perform the CSI reporting. For example, the UE can report to the base station information representing the model associated with the CSI (e.g., i) CPM, ii) IPM, or iii) both CPM and IPM).

[0243] For example, the base station / NW can configure / instruct the UE to perform reporting for a specific CSI report based on which method from method 1 to method 3. For example, the base station can configure the UE to represent information about the model associated with the CSI report (e.g., i) CPM, ii) IPM, or iii) both CPM and IPM).

[0244] For example, a UE can report CSI based on multiple methods, including Method 1, Method 2, and Method 3. The base station can perform scheduling based on each of these methods reported by the UE, and assist in Lifecycle Management (LCM) by determining which method yields the correct CSI based on A / N (ACK / NACK), etc. As a specific example, if the result of scheduling based on each of the multiple methods reported by the UE is that only Method 1 performs well, the base station can instruct the UE to terminate or retrain the IPM LCM.

[0245] For example, in addition to the UE calculating / reporting CSI, the UE can report its capabilities for the models supported by the CSI report. Based on these UE capabilities, the models supported by the CSI report can be indicated as i) IPM, ii) CPM, or iii) both IPM and CPM. Specifically, the UE can report its capabilities to the base station regarding: i) whether only IPM is available, ii) whether only CPM is available, or iii) whether both IPM and CPM are available, and the base station can configure / indicate / transmit information indicating the models associated with the CSI report to the UE based on these capabilities. For example, information indicating the models associated with the CSI report can be transmitted to the UE as configuration information associated with the CSI report.

[0246] Proposal 3

[0247] In UE-side AI / ML-based CSI prediction, the following methods can be considered for reporting PMI to the base station / NW.

[0248] - Method 1: The UE can recalculate the PMI based on the CSI output by the AI / ML model in each reporting instance and report it to the NW.

[0249] - Method 2: The AI / ML model output is output as a PMI in each report instance, which the UE can report to the NW.

[0250] In Method 1, the UE has an advantage in inference accuracy because it infers CSI based on historical raw channels that include more information than PMI. However, since the UE recalculates PMI through post-processing and reports it to the base station, loss values ​​resulting from post-processing or existing channel quality information (e.g., eigenvalues ​​of the original channel matrix or eigenvalues ​​of the covariance of the original channel matrix) may be additionally reported.

[0251] In Method 2, since PMI is itself a model output, UE has the advantage of being able to report PMI immediately without any separate calculations.

[0252] The UE can calculate and report the CQI based on the PMI reported via Method 1 / Method 2. In this instance, a method for the UE to report the CQI based on interference information can be considered.

[0253] Proposal 3-1

[0254] The UE can use the latest interference during the prediction window. Specifically, the UE can measure the latest IMR and calculate and report the CQI based on the same interference during the prediction window.

[0255] Proposal 3-2

[0256] The UE can use historical long-term average interference during the prediction window. Specifically, the UE 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 by the latest IMR based on the reference resource deviates significantly from the long-term perspective, the UE can still calculate and report a CQI that reflects the long-term trend by using the long-term average interference during the prediction window.

[0257] Proposal 3-3

[0258] The UE can use predicted interference predicted through inference operations of another AI / ML model. Specifically, the UE can use separate models to predict interference. The predicted interference can be based on historical interference output from the corresponding model. The UE can then calculate and report the predicted CQI based on this predicted interference.

[0259] Proposal 4

[0260] In AI / ML-based CSI prediction on the NW side, methods for the base station / NW to use / utilize information regarding: i) SNR (or RSRP) and INR, or ii) the original channel and interference can be considered. For example, the UE can report information about i) SNR (or RSRP) and INR, or ii) the original channel and interference to the base station.

[0261] If the base station / NW only periodically receives information about the desired channel from the terminal / UE, it may have difficulty understanding the Channel Quality Index (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 determine whether the channel power has decreased or the interference power has increased. This embodiment aims to address this problem. For example, the terminal / UE can report both the original channel information and interference information to the base station / NW. For example, the terminal / UE can report both SNR (or RSRP) information and INR information to the base station / NW. In this way, the base station / NW can understand the changes in CQI in more detail. For example, if the CQI decreases due to increased interference, the base station / NW can determine the cause of the CQI decrease based on the interference information reported by the terminal / UE, and the base station / NW can take measures to adjust the beams of neighboring cells to reduce interference.

[0262] Figure 4 This is a flowchart illustrating an example of signaling based on a method according to at least one embodiment of the present disclosure. Figure 4 The illustration shows an example of signaling between the UE and the base station / NW based on Proposals 1 to 4 above. The terminal / UE and the base station / NW are merely examples and can be replaced with various devices. Figure 4 This is for illustrative purposes only and does not limit the scope of this disclosure. Additionally, depending on the circumstances and / or configuration, etc. Figure 4 Some steps / processes shown in the diagram can be omitted.

[0263] In S410, the user equipment (UE) performs a reporting process related to the UE capability value based on the proposed method (e.g., Proposal 1 to Proposal 4). This reporting process may include range information of the UE capability value and report it to the base station / NW (e.g., gNB / TRP).

[0264] In S420, the base station performs configuration for the functions / parameters related to the UE capability values ​​reported in S410 based on the proposed method (e.g., Proposal 1 to Proposal 4), and / or performs configuration / instructions related to the UE report in a subsequent step S430.

[0265] In S430, based on the report in S410, the UE reports the UE capability value for the reporting time or a specific past / future time based on the proposed method.

[0266] In S440, based on the UE's report information in S430, the base station can perform (re)configuration or instruction for functions / parameters related to the UE's capability values.

[0267] In the application of the above operations, all or part of the operations of S420 can be executed before / after S410, and / or simultaneously with S410.

[0268] Proposals 1, 2, 3 and 4 above can be extended and applied to temporal beam prediction and AI / ML-based CSI prediction.

[0269] From the perspective of implementation, the operation of the UE / base station according to the above embodiments (e.g., operation based on at least one of proposals 1 to 4) can be performed by Figure 7 and Figure 8 The devices in (e.g.) Figure 7 Processed by processors 110 and 210 in the system.

[0270] Furthermore, the operations of the UE / base station according to the above embodiments (e.g., operations based on at least one of proposals 1 to 4) can be stored in memory (e.g., in the form of commands / programs, instructions, executable code) in the form of commands / programs. Figure 7 In the memories 140 and 240, for use by at least one processor (e.g. Figure 7 The processors 110 and 210 in the system are running.

[0271] Below, for reference Figure 5 and Figure 6 The above embodiments are described in detail from the perspective of UE / base station operation. The methods described below are distinguished only for ease of explanation. Therefore, it is obvious that partial configurations of any method can be replaced or combined with partial configurations of another method.

[0272] Figure 5 This is a flowchart illustrating a method according to an embodiment of the present disclosure.

[0273] refer to Figure 5 The method according to embodiments of the present disclosure includes a channel state information-reference signal (CSI-RS) receiving step S510 and a CSI reporting step S520.

[0274] In step S510, the UE receives CSI-RS from the base station.

[0275] CSI-RS can be received based on CSI-related configuration information.

[0276] For example, CSI-RS can be used for CSI calculations.

[0277] CSI may include at least one of the following: i) Channel Quality Indicator (CQI), ii) Precoding Matrix Indicator (PMI), iii) CSI-RS Resource Indicator (CRI), iv) SS / PBCH Block Resource Indicator (SSBRI), v) Layer Indicator (LI), vi) Rank Indicator (RI), vii) L1-RSRP, and / or viiii) L1-SINR.

[0278] In such instances, CSI (e.g., information included in the CSI as at least one parameter of the CSI report) can be measured / determined / predicted / calculated based on the aforementioned CPM and / or IPM. As a specific example, CSI (e.g., CQI) can be calculated / determined based on: i) information related to channel predictions predicted via CPM, and / or ii) information related to interference predictions predicted via IPM. In this disclosure, for ease of illustration, i) information related to channel predictions can be represented as first information, and ii) information related to interference predictions can be represented as second information.

[0279] For example, information related to channel prediction can be used to make predictions based on historical channels.

[0280] According to one embodiment, CSI may include CQI. CQI may be determined based on information related to channel prediction and information related to interference prediction. This embodiment may be based on Proposal 1.

[0281] According to one embodiment, information related to interference prediction can be predicted based on historical interference measurements. This embodiment can be based on Proposal 1.

[0282] For example, historical interference measurements can be based on one or more historical interference measurements over a time period. Specifically, historical interference measurements can be the result of filtering (e.g., averaging) one or more historical interference measurements within a specific measurement window.

[0283] For example, historical interference measurements can be based on a single measurement, and the measurement restrictions for that single measurement can be configured to always be on / enabled.

[0284] According to one embodiment, information related to interference prediction can be predicted based on historical CQI and / or historical signal-to-interference-to-noise ratio (SINR). This embodiment can be based on Proposal 1.

[0285] According to one embodiment, the method may further include receiving configuration information related to CSI.

[0286] For example, configuration information may include information related to CSI reports.

[0287] According to one embodiment, the configuration information may include information related to reporting the following: i) information related to channel prediction, and / or ii) information related to interference prediction.

[0288] For example, the UE may calculate / report CSI in various ways depending on the environment (e.g., high SINR environment, low SINR environment). As a specific example, the UE may report CSI based on: i) information related to channel prediction, and / or ii) information related to interference prediction.

[0289] For example, a base station can configure / instruct the UE to calculate / report CSI based on the environment. As a specific example, the base station can transmit configuration information to the UE such that the UE reports CSI based on i) information related to channel prediction and / or ii) information related to interference prediction, depending on the environment.

[0290] According to one embodiment, CSI based on configuration information may include i) information related to channel prediction and / or ii) information related to interference prediction.

[0291] For example, a UE can report CSI based on channel prediction-related information predicted by CPM. As a specific example, in a high SINR environment, the UE can use CPM alone. The UE can report CSI based on channel prediction-related information output by CPM. As a specific example, at least one parameter included in the CSI (e.g., CQI) can be determined based on channel prediction-related information. Additionally, the CSI can include channel prediction-related information.

[0292] For example, a UE can report CSI based on interference prediction-related information predicted by an IPM. As a specific example, in a low SINR environment, the UE can use only an IPM. The UE can report CSI based on interference prediction-related information output by the IPM. As a specific example, at least one parameter included in the CSI (e.g., CQI) can be determined based on interference prediction-related information. Additionally, the CSI can include interference prediction-related information.

[0293] For example, the UE can report CSI based on CPM and IPM. As a specific example, the UE can report CSI based on i) channel prediction-related information output by the CPM, and ii) interference prediction-related information output by the IPM. As a specific example, at least one parameter included in the CSI (e.g., CQI) can be determined based on the channel prediction-related information and the interference prediction-related information. Additionally, the CSI can include i) channel prediction-related information and ii) interference prediction-related information. This embodiment can be based on Proposal 2.

[0294] According to one embodiment, CSI may include a Channel Quality Indicator (CQI).

[0295] For example, CQI can be determined based on information related to channel prediction and / or information related to interference prediction. This embodiment can be based on Proposal 1 and Proposal 2.

[0296] According to one embodiment, CSI may include a precoded matrix indicator (PMI).

[0297] According to one embodiment, the PMI can be predicted based on information related to channel prediction and / or information related to interference prediction. As a specific example, the UE can predict the PMI in each reporting instance based on information related to channel prediction and / or information related to interference prediction. In this case, since the output of the AI / ML model is itself the PMI, the UE can directly report the PMI to the base station without additional computation. This embodiment can be based on method 1 of Proposal 3.

[0298] According to one embodiment, the PMI can be determined based on the predicted channel state. In this case, the channel state can be calculated / predicted based on information related to channel prediction and / or information related to interference prediction. As a specific example, the UE can recalculate the PMI based on the predicted channel state in each reporting instance. In this case, since the channel state is predicted based on the historical original channel, there is an advantage in high prediction accuracy. Since additional processing is required to convert the channel into the PMI, the UE can additionally report the loss value resulting from the additional processing or existing channel quality information (e.g., eigenvalues ​​of the original channel matrix or eigenvalues ​​of the original channel matrix covariance). This embodiment can be based on method 2 of Proposal 3.

[0299] According to one embodiment, CSI may include CQI, and CQI may be determined based on PMI and information related to interference prediction. Specifically, interference information may be required during the UE's process of determining CQI based on PMI. In this case, the UE may utilize information related to interference prediction as interference information for determining CQI. This embodiment may be based on Proposal 3.

[0300] According to one embodiment, information related to interference prediction can be determined based on the most recently measured interference-to-noise ratio (INR). As a specific example, the UE can measure the most recent IMR and use the interference based on the most recent IMR equally during the prediction window to determine the CQI. This embodiment can be based on Proposal 3-1.

[0301] According to one embodiment, information related to interference prediction can be determined based on the average of one or more interference measurements over a time period. As a specific example, the UE can use long-term average interference to determine the CQI. In this case, even if the interference measured by the latest IMR based on the reference resource deviates significantly from a long-term perspective, the UE can still determine and report a CQI that reflects the long-term trend by using long-term average interference. This embodiment can be based on Proposal 3-2.

[0302] In step S520, the UE reports CSI to the base station.

[0303] For example, the UE can perform channel characteristic measurements based on CSI-RS and feed the CSI report back to the base station as a result.

[0304] For example, the types of feedback may include i) Channel Quality Indicator (CQI), ii) Precoding Matrix Indicator (PMI), iii) CRI (CSI-RS Resource Indicator), iv) SSBRI (SSB Resource Block Indicator), v) Layer Indicator (LI), vi) Rank Indicator (RI), vii) L1-Reference Signal Received Power (RSRP).

[0305] According to one embodiment, the CSI may include interference-related information and original channel-related information. Specifically, the UE may report the CSI, which includes interference-related information and original channel-related information, to the base station / NW. This embodiment may be based on Proposal 4.

[0306] According to another embodiment, the method may further include transmitting interference-related information and original channel-related information. Specifically, the UE may transmit the interference-related information and original channel-related information together with a CSI report to the base station. This embodiment may be based on Proposal 4.

[0307] According to one embodiment, CSI may include i) SINR or RSRP, and ii) INR. Specifically, the UE may report CSI including i) SINR or RSRP and ii) INR to the base station / NW. This embodiment may be based on Proposal 4.

[0308] According to another embodiment, the method may further include transmitting i) SINR or RSRP, and ii) INR. Specifically, the UE may transmit i) SINR or RSRP, and ii) INR together with a CSI report to the base station. This embodiment may be based on Proposal 4.

[0309] According to one embodiment, information related to channel prediction and information related to interference prediction can be associated with CPM and IPM. In this disclosure, CPM can be represented as a first model, and IPM can be represented as a second model.

[0310] According to one embodiment, historical interference measurements can be correlated with the input of the IPM, and information related to interference prediction can be correlated with the output of the IPM.

[0311] For example, when the model is implemented / included in i) the UE, ii) the base station (network), or iii) the UE and the base station (network), the operations according to the above embodiments can be performed.

[0312] Based on the operations of steps S510 and S520 above, it can be performed by... Figures 7 to 8 This is achieved using the devices described. For example, refer to... Figure 7 The UE 200 can control one or more transceivers 230 and / or one or more memories 240 to perform operations based on steps S510 and S520.

[0313] The above embodiments will now be described in detail from the perspective of base station operation.

[0314] Steps S610 and S620 described below correspond to reference Figure 5 Steps S510 and S520 are described. Redundant descriptions are omitted to account for the above correspondence. The detailed description of the base station operation described below can be replaced with... Figure 5 The description / examples corresponding to base station operation are as follows.

[0315] Figure 6 This is a flowchart illustrating a method according to another embodiment of the present disclosure.

[0316] refer to Figure 6 The method according to another embodiment of the present disclosure includes a channel state information-reference signal (CSI-RS) transmission step S610 and a channel state information (CSI) reception step S620.

[0317] In step S610, the base station transmits CSI-RS to the UE.

[0318] In step S620, the base station receives CSI from the UE. In this example, CSI may include i) information related to channel prediction and / or ii) information related to interference prediction.

[0319] For example, CSI may include i) information related to channel prediction and / or ii) information related to interference prediction. In this disclosure, for ease of illustration, i) information related to channel prediction may be represented as first information, and ii) information related to interference prediction may be represented as second information.

[0320] According to one embodiment, the method may further include transmitting configuration information related to CSI.

[0321] For example, the configuration information may include information related to reporting the following: i) information related to channel prediction, and / or ii) information related to interference prediction. This embodiment may be based on Proposal 2.

[0322] According to one embodiment, CSI based on configuration information may include i) information related to channel prediction, and / or ii) information related to interference prediction. This embodiment may be based on Proposal 2.

[0323] According to one embodiment, CSI may include a Channel Quality Indicator (CQI).

[0324] For example, CQI can be determined based on i) information related to channel prediction and / or ii) information related to interference prediction. This embodiment can be based on Proposal 1 and Proposal 2.

[0325] The base station can schedule downlink channels based on CSI reported from the UE. According to embodiments of this disclosure, downlink channels can be scheduled to minimize the impact of interference.

[0326] Based on the operations of steps S610 and S620 above, it can be performed by... Figure 7 and Figure 8 This is achieved using equipment. For example, refer to... Figure 7 The base station 100 can control one or more transceivers 130 and / or one or more memories 140 to perform operations based on steps S610 and S620.

[0327] The operations / terminology based on the above embodiments have been described under the assumption of a 5G system. However, this is merely for illustrative purposes and is not intended to limit the application scope of the technical problems and solutions to be solved by this disclosure to a specific system. That is, the technical challenges / problems / issues mentioned in this disclosure may also exist in other systems (e.g., 6G systems). It is obvious that the embodiments of this disclosure can be extended and applied to solve the same problems in other systems. Therefore, in order to extend the embodiments of this disclosure to other systems, the terms defined / described based on the 5G system can be replaced / changed to terms defined in other systems (or general terms not specific to a system). For example, PRACH, PUSCH, PUCCH, or SRS can be replaced / changed to uplink signals (or uplink channels). For example, SSB, CSI-RS, PDSCH, or PDCCH can be replaced / changed to downlink signals (or downlink channels).

[0328] The following is for reference. Figures 7 to 8 The embodiments of this disclosure are described to be applied to devices (devices that implement the methods / operations according to the embodiments of this disclosure).

[0329] Figure 7 The illustration shows the configuration of a first device and a second device according to an embodiment of the present disclosure.

[0330] The first device 100 may include a processor 110, an antenna unit 120, a transceiver 130, and a memory 140.

[0331] Processor 110 can perform baseband-related signal processing and includes a higher-layer processing unit 111 and a physical layer processing unit 115. Higher-layer processing unit 111 can handle operations at the MAC layer, RRC layer, or higher layers. Physical layer processing unit 115 can handle PHY layer operations. For example, if the first device 100 is a base station (BS) device in base station-UE communication, physical layer processing unit 115 can perform uplink receive signal processing, downlink transmit signal processing, etc. For example, if the first device 100 is a first UE device in inter-UE communication, physical layer processing unit 115 can perform downlink receive signal processing, uplink transmit signal processing, sidelink transmit signal processing, etc. In addition to performing baseband-related signal processing, processor 110 can also control the overall operation of the first device 100.

[0332] Antenna unit 120 may include one or more physical antennas, and if antenna unit 120 includes multiple antennas, MIMO transmission / reception is supported. Transceiver 130 may include a radio frequency (RF) transmitter and an RF receiver. Memory 140 may store information processed by processor 110, as well as software, operating system, and applications related to the operation of first device 100. Memory 140 may also include components such as buffers.

[0333] The processor 110 of the first device 100 may be configured to implement the operation of the BS in BS-UE communication (or the operation of the first UE device in inter-UE communication) in the embodiments described in this disclosure.

[0334] The second device 200 may include a processor 210, an antenna unit 220, a transceiver 230, and a memory 240.

[0335] Processor 210 performs baseband-related signal processing and includes a higher-layer processing unit 211 and a physical layer processing unit 215. Higher-layer processing unit 211 can handle MAC layer, RRC layer, or higher-layer operations. Physical layer processing unit 215 can handle PHY layer operations. For example, if the second device 200 is a UE device in BS-UE communication, physical layer processing unit 215 can perform downlink receive signal processing, uplink transmit signal processing, etc. For example, if the second device 200 is a second UE device in inter-UE communication, physical layer processing unit 215 can perform downlink receive signal processing, uplink transmit signal processing, sidelink receive signal processing, etc. In addition to performing baseband-related signal processing, processor 210 can also control the overall operation of the second device 210.

[0336] Antenna unit 220 may include one or more physical antennas, and if antenna unit 220 includes multiple antennas, MIMO transmission / reception is supported. Transceiver 230 may include an RF transmitter and an RF receiver. Memory 240 may store information processed by processor 210, as well as software, operating system, and applications related to the operation of second device 200. Memory 240 may also include components such as buffers.

[0337] The processor 210 of the second device 200 can be configured to implement the operation of the UE in BS-UE communication (or the operation of the second UE device in UE-to-UE communication) in the embodiments described in this disclosure.

[0338] The descriptions of the BS and UE (or the first UE device and the second UE device in inter-UE communication) in the examples of this disclosure are equally applicable to the operation of the first device 100 and the second device 200, and redundant descriptions are omitted.

[0339] Figure 8 The illustration shows an AI device according to an embodiment of the present disclosure. This AI device can be implemented by a fixed or mobile device, such as a TV, projector, smartphone, PC, laptop, digital broadcast terminal, tablet PC, wearable device, set-top box (STB), radio, washing machine, refrigerator, digital signage, robot, vehicle, etc.

[0340] refer to Figure 8 The AI ​​device 100 may include a communication unit 110, a control unit 120, a storage unit 130, I / O units 140a / 140b, a learning processor unit 140c, and a sensor unit 140d. Blocks 110 to 130 / 140a to 140d respectively correspond to... Figure 7 Blocks 110 to 130 / 140.

[0341] The communication unit 110 can utilize wired / wireless communication technologies to transmit and receive wired / wireless signals (e.g., sensor information, user input, learning models, or control signals) to and from external devices such as other AI devices or AI servers. To this end, the communication unit 110 can transmit information from the storage unit 130 to external devices and transmit signals received from external devices to the storage unit 130.

[0342] The control unit 120 can determine at least one feasible operation of the AI ​​device 100 based on information determined or generated using data analysis algorithms or machine learning algorithms. The control unit 120 can execute the determined operation by controlling the components of the AI ​​device 100. For example, the control unit 120 can request, search, receive, or use data from the learning processor unit 140c or the storage unit 130, and control the components of the AI ​​device 100 to execute a predicted operation or an operation determined as preferred among at least one feasible operation. The control unit 120 can collect historical information including the operation content of the AI ​​device 100 and user operation feedback, and store the collected information in the storage unit 130 or the learning processor unit 140c, or transmit the collected information to an external device such as an AI server. The collected historical information can be used to update the learning model.

[0343] Storage unit 130 can store data used to support various functions of AI device 100. For example, storage unit 130 can store data acquired from input unit 140a, data acquired from communication unit 110, output data from learning processor unit 140c, and data acquired from sensor unit 140. Storage unit 130 can store control information and / or software code required to operate / drive control unit 120.

[0344] Input unit 140a can acquire various types of data from outside the AI ​​device 100. For example, input unit 140a can acquire learning data for model learning, as well as input data for applying the learning model. Input unit 140a may include a camera, microphone, and / or user input unit. Output unit 140b can generate outputs related to vision, hearing, or touch. Output unit 140b may include a display unit, speaker, and / or haptic module. Sensing unit 140 can utilize various sensors to acquire at least one of the following: internal information of the AI ​​device 100, environmental information of the AI ​​device 100, and user information. Sensor unit 140 may include a proximity sensor, illumination sensor, accelerometer, magnetometer, gyroscope, inertial sensor, RGB sensor, IR sensor, fingerprint sensor, ultrasonic sensor, light sensor, microphone, and / or radar.

[0345] The learning processor unit 140c can use training data to learn a model composed of artificial neural networks. 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 external devices via the communication unit 110 and / or information stored in the storage unit 130. Furthermore, the output values ​​of the learning processor unit 140c can be transmitted to external devices via the communication unit 110 and can be stored in the storage unit 130.

[0346] In addition to LTE, NR, and 6G, the wireless communication technologies implemented in the devices according to this disclosure may also include narrowband Internet of Things (NB-IoT) for low-power communication. For example, NB-IoT technology may be an example of low-power wide-area network (LPWAN) technology and may be implemented in standards such as LTE Cat NB1 and / or LTE Cat NB2. NB-IoT technology is not limited to the above names.

[0347] Alternatively, the wireless communication technology implemented in the device according to this disclosure may perform communication based on LTE-M technology. For example, LTE-M technology may be an example of LPWAN technology and may be referred to by various names such as Enhanced Machine Type Communication (eMTC). For example, LTE-M technology may be implemented in at least one of various standards, such as 1) LTE CAT 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-BL (non-bandwidth limited), 5) LTE-MTC, 6) LTE Machine Type Communication, and / or 7) LTE M, and is not limited to the names mentioned above.

[0348] Alternatively, considering low power, the wireless communication technology implemented in the device according to this disclosure may include at least one of ZigBee, Bluetooth, and Low Power Wide Area Network (LPWAN), and is not limited to the names mentioned above. For example, ZigBee technology can generate PANs (Personal Area Networks) associated with small / low power digital communications based on various standards such as IEEE 802.15.4, and may be referred to by various names.

Claims

1. A method comprising: Receive Channel State Information-Reference Signal (CSI-RS); as well as Report Channel State Information (CSI). The CSI includes i) first information related to channel prediction and ii) second information related to interference prediction.

2. The method of claim 1, wherein, The CSI includes a Channel Quality Indicator (CQI), and The CQI is determined based on the first information and the second information.

3. The method of claim 1, wherein, The second piece of information is predicted based on historical interference measurements.

4. The method of claim 3, wherein, The historical interference measurement is based on one or more historical interference measurements within a time period.

5. The method of claim 1, wherein, The second piece of information is predicted based on historical CQI and / or historical signal-to-interference-noise ratio (SINR).

6. The method of claim 1, further comprising: Transmit information related to interference and information related to the original channel.

7. The method of claim 1, further comprising: Transmission i) Signal-to-noise ratio (SNR) or reference signal received power (RSRP), and ii) Interference-to-noise ratio (INR).

8. The method according to claim 1, further comprising: Receive configuration information related to the CSI. The configuration information includes information related to the reporting of the first information and / or the reporting of the second information.

9. The method according to claim 8, wherein, The CSI includes the first information and / or the second information based on the configuration information.

10. The method according to claim 9, wherein, The CSI includes a Channel Quality Indicator (CQI), and The CQI is determined based on the first information and / or the second information.

11. The method according to claim 1, wherein, The CSI includes a precoded matrix indicator (PMI). The PMI is determined based on the first information and / or the second information.

12. The method according to claim 11, wherein, The PMI is determined based on the channel state, and The channel state is predicted based on the first information and / or the second information.

13. The method according to claim 11, wherein, The CSI includes a Channel Quality Indicator (CQI), and The CQI is determined based on the PMI and the second information.

14. The method according to claim 13, wherein, The second piece of information was determined based on the most recently measured interference to noise ratio (INR).

15. The method according to claim 13, wherein, The second information is determined based on the average value of one or more interference measurements over a time period.

16. The method according to claim 3, wherein, The first information and the second information are related to the first model and the second model.

17. The method according to claim 16, wherein, The historical interference measurements are related to the input of the second model, and The second information is related to the output of the second model.

18. The method according to claim 16, wherein, The model includes a user equipment (UE) side model.

19. A user equipment, comprising: One or more transceivers; One or more processors; as well as One or more memories, connected to the one or more processors and storing instructions, The instructions are based on execution by the one or more processors, allowing the user equipment to perform all steps of the method according to any one of claims 1 to 18.

20. An apparatus comprising: One or more memory units; as well as One or more processors, the one or more processors being connected to the one or more memories, The one or more memory stores instructions, which are based on instructions executed by the one or more processors, allowing the device to perform all steps of the method according to any one of claims 1 to 18.

21. A non-transitory computer-readable medium for storing instructions, in, The instructions are executable by one or more processors, allowing a user device to perform all steps of the method according to any one of claims 1 to 18.

22. A method comprising: Transmit Channel State Information-Reference Signal (CSI-RS); as well as Receive Channel State Information (CSI). The CSI includes i) first information related to channel prediction and ii) second information related to interference prediction.

23. The method of claim 22, further comprising: Transmit configuration information related to the CSI. The configuration information includes information related to the reporting of the first information and / or the reporting of the second information.

24. The method according to claim 23, wherein, The CSI includes the first information and / or the second information based on the configuration information.

25. The method according to claim 24, wherein, The CSI includes a Channel Quality Indicator (CQI), and The CQI is determined based on the first information and / or the second information.

26. A base station, comprising: One or more transceivers; One or more processors; as well as One or more memories, connected to the one or more processors and storing instructions, The instructions are based on execution by the one or more processors, allowing the base station to perform all steps of the method according to any one of claims 22 to 25.

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