WO2026031168A1 - Réduction du surdébit dans des signaux de référence d'informations d'état de canal (csi) pour une rétroaction de csi basée sur une prédiction - Google Patents

Réduction du surdébit dans des signaux de référence d'informations d'état de canal (csi) pour une rétroaction de csi basée sur une prédiction

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Publication number
WO2026031168A1
WO2026031168A1 PCT/CN2024/111036 CN2024111036W WO2026031168A1 WO 2026031168 A1 WO2026031168 A1 WO 2026031168A1 CN 2024111036 W CN2024111036 W CN 2024111036W WO 2026031168 A1 WO2026031168 A1 WO 2026031168A1
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WO
WIPO (PCT)
Prior art keywords
csi
prediction
report
network entity
ports
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/CN2024/111036
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English (en)
Inventor
Yushu Zhang
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Google LLC
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Google LLC
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Publication date
Application filed by Google LLC filed Critical Google LLC
Priority to PCT/CN2024/111036 priority Critical patent/WO2026031168A1/fr
Publication of WO2026031168A1 publication Critical patent/WO2026031168A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0686—Hybrid systems, i.e. switching and simultaneous transmission
    • H04B7/0695—Hybrid systems, i.e. switching and simultaneous transmission using beam selection
    • H04B7/06952—Selecting one or more beams from a plurality of beams, e.g. beam training, management or sweeping
    • H04B7/06958—Multistage beam selection, e.g. beam refinement
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0621—Feedback content
    • H04B7/0626—Channel coefficients, e.g. channel state information [CSI]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0686—Hybrid systems, i.e. switching and simultaneous transmission
    • H04B7/0695—Hybrid systems, i.e. switching and simultaneous transmission using beam selection
    • H04B7/06952—Selecting one or more beams from a plurality of beams, e.g. beam training, management or sweeping
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0686—Hybrid systems, i.e. switching and simultaneous transmission
    • H04B7/0695—Hybrid systems, i.e. switching and simultaneous transmission using beam selection
    • H04B7/06952—Selecting one or more beams from a plurality of beams, e.g. beam training, management or sweeping
    • H04B7/06956—Selecting one or more beams from a plurality of beams, e.g. beam training, management or sweeping using a selection of antenna panels

Definitions

  • This disclosure relates generally to wireless communications and, more particularly, to machine learning based channel state information (CSI) .
  • CSI channel state information
  • channel state information enables a network entity (network entity) to select the digital precoder for a user equipment (UE) .
  • the network entity 104 configures the UE to provide a CSI report using RRC signaling, e.g., CSI-ReportConfig.
  • the network entity configures the UE to use channel state information reference signal (CSI-RS) as channel measurement resource (CMR) for the UE to measure the downlink channel.
  • CMR channel measurement resource
  • IMR interference measurement resource
  • the UE is able to identify the CSI, which may include at least one of rank indicator or rank index (RI) , precoder matrix indicator (PMI) , channel quality indicator (CQI) and layer indicator (LI) .
  • rank indicator and “rank index, ” both referred to by RI, are interchangeable herein.
  • RI and PMI are used to indicate the digital precoder
  • CQI is used to indicate the signal-to-interference plus noise (SINR) status in order to assist the network entity 104 to determine the modulation and coding scheme (MCS)
  • MCS modulation and coding scheme
  • LI is used to identify the strongest layer for the reported precoder indicated by RI and PMI.
  • the network entity may transmit the downlink signals from a large number of antenna ports, e.g., 128 ports, 256 ports, or more.
  • the network entity may configure the UE to report CSI based on CSI-RS from such large number of antenna ports.
  • the overhead of CSI-RS of the numerous antenna ports increases correspondingly. Reducing or minimizing the overhead is desired.
  • the present disclosure provides methods, systems, and techniques for reducing overhead in channel state information reference signals (CSI-RS) for prediction based CSI feedback.
  • Example methods include overhead reduction applicable to predictions in frequency domain (FD) , spatial domain (SD) , time domain (TD) , or any combination thereof.
  • the overhead reduction is beneficial for improving downlink performance when a network entity transmits downlink signals from a large number of antenna ports (e.g., 128 ports, 256 ports, or more) , which conventionally result in a high overhead.
  • This disclosure provides methods for reducing CSI-RS overhead using machine-learning enabled CSI prediction, such as using FD prediction, SD prediction, or a joint prediction in FD, SD, and TD.
  • a method for wireless communications by a user equipment includes receiving, from a network entity, a channel state information (CSI) report configuration configuring: a CSI reference signal (CSI-RS) resource set for channel measurement, one or more subbands for reporting CSI, and one or more antenna ports for reporting the CSI.
  • the method further includes receiving, from the network entity, a CSI-RS according to the CSI-RS resource set.
  • the method includes transmitting, to the network entity, a CSI report based on prediction or measurement of the CSI according to at least a subset of the one or more subbands or to at least a subset of the one or more antenna ports.
  • the CSI report configuration further includes at least one of: a report quantity; a codebook for the CSI report, one or more interference measurement resources, a configuration for spatial domain (SD) prediction associated with the one or more antenna ports; a configuration for frequency domain (FD) prediction; or a configuration for time domain (TD) prediction, wherein the SD prediction, the FD prediction, and the TD prediction use machine-learning models for predicting non-measured values based on measured values.
  • SD spatial domain
  • FD frequency domain
  • TD time domain
  • the method further includes transmitting, to the network entity, UE capability information for supporting CSI report based on at least one of an SD prediction, an FD prediction, or a TD prediction, wherein the UE capability information includes at least one off channel delay information for the FD prediction; or supported CSI-RS port information for the SD prediction.
  • an CSI-RS density for each subband is determined based on the channel delay information; and wherein the channel delay information includes at least one of: a channel delay profile indicating a delay for a number of paths; a channel power delay profile indicating delays and amplitudes of signal samples or paths; a channel impulse response; a channel delay spread; a maximum channel delay; an average channel delay; or a subband size recommended by the UE.
  • the method further includes transmitting, to the network entity, a supported CSI-RS pattern information associated with the FD prediction, wherein the supported CSI-RS pattern information includes at least one of: an FD density for CSI-RS from each antenna port for a set of subbands; one or more subband indexes associated with the set of subbands; a subband size; an FD density for CSI-RS from each antenna port for an entire bandwidth; a dataset identifier (ID) associated with a prediction scenario, the dataset ID corresponding to at least one of:a type of model input or a type of model output; a machine learning (ML) model ID associated with the prediction scenario, the model ID corresponding to at least one of: a type of model input or a type of model output; or an ID associated with antenna structure of the network entity.
  • ID dataset identifier
  • ML machine learning
  • the CSI report configuration further includes: one or multiple subband index (es) for the UE to perform the CSI measurement and report based on a reference bandwidth; and an indication to include, in the CSI report, at least one of: a rank index (RI) for each subband, a precoder matrix indicator (PMI) for each subband, a channel quality indicator (CQI) for each subband, an RI for a wideband, a PMI for a wideband, or a CQI for a wideband.
  • RI rank index
  • PMI precoder matrix indicator
  • CQI channel quality indicator
  • the supported CSI-RS port information includes at least one of: a number of horizontal ports, a number of vertical ports, a number of antenna panels, one or more supported sets of CSI-RS port indexes, one or more measured CSI-RS ports for each CSI-RS port structure, an association ID, a dataset ID, or a machine learning (ML) model ID.
  • ML machine learning
  • the configuration further includes at least one of: a non-zero-power (NZP) or zero-power (ZP) state for each CSI-RS port or port group; a number of CSI-RS ports; or a port association between a CSI-RS port and a codebook.
  • NZP non-zero-power
  • ZP zero-power
  • the method further includes transmitting, in the CSI report from the UE, a PMI based on: a channel eigenvector according to NZP CSI-RS ports indicated in the configuration; or a codebook indicated in the configuration, wherein the codebook is polarization-specific.
  • the UE determines a beam corresponding to a PMI based on antenna ports from a codebook mapped to the number of CSI-RS ports.
  • the configuration further includes time domain (TD) prediction information for the UE to calculate CSI for future slots based on received transmission occasions of CSI-RS resources associated with the CSI-RS resource set.
  • the TD prediction information includes at least one of: a measurement window configuration indicating the transmission occasions of the CSI-RS resources; one or more predicted slots; a codebook for predicted CSI quantization; a duration, a periodicity, and a starting point within each period of an activation window; or a duration, a periodicity, and a starting point within each period of a deactivation window.
  • the method further includes performing, by the UE, the FD prediction, the SD prediction, or the TD prediction using a single machine learning (ML) model, wherein the single machine learning model provides for CSI compression.
  • the UE may perform, with the network entity, joint FD prediction, SD prediction, or the TD prediction using respective machine learning (ML) models of the UE and of the network entity.
  • a method for wireless communications by a network entity includes transmitting, to a UE, a CSI report configuration configuring: a CSI-RS resource set for channel measurement, one or more subbands for reporting CSI, and one or more antenna ports for reporting the CSI.
  • the method includes transmitting, to the UE, a CSI-RS according to the CSI-RS resource set.
  • the method further includes receiving, from the UE, a CSI report based on prediction or measurement of the CSI according to at least a subset of the one or more subbands or to at least a subset of the one or more antenna ports.
  • the CSI report configuration further includes at least one of: a report quantity; a codebook for the CSI report, one or more interference measurement resources, a configuration for SD prediction associated with the one or more antenna ports; a configuration for FD prediction; or a configuration for TD prediction, wherein the SD prediction, the FD prediction, and the TD prediction use machine-learning models for predicting non-measured values based on measured values.
  • an CSI-RS density for each subband is determined based on channel delay information, and wherein the channel delay information includes at least one of: a channel delay profile indicating a delay for a number of paths; a channel power delay profile indicating delays and amplitudes of signal samples or paths; a channel impulse response; a channel delay spread; a maximum channel delay; an average channel delay; or a subband size recommended by the UE.
  • the channel delay information includes at least one of: a channel delay profile indicating a delay for a number of paths; a channel power delay profile indicating delays and amplitudes of signal samples or paths; a channel impulse response; a channel delay spread; a maximum channel delay; an average channel delay; or a subband size recommended by the UE.
  • the network entity transmits the configuration based on: receiving, from the UE (102) , a supported CSI-RS pattern information associated with the FD prediction, wherein the supported CSI-RS pattern information includes at least one of: an FD density for CSI-RS from each antenna port for a set of subbands; one or more subband indexes associated with the set of subbands; a subband size; an FD density for CSI-RS from each antenna port for an entire bandwidth; a dataset ID associated with a prediction scenario, the dataset ID corresponding to at least one of: a type of model input or a type of model output; a machine learning (ML) model ID associated with the prediction scenario, the model ID corresponding to at least one of: a type of model input or a type of model output; or an ID associated with antenna structure of the network entity.
  • the supported CSI-RS pattern information includes at least one of: an FD density for CSI-RS from each antenna port for a set of subbands; one or more subband indexes associated with the set
  • the network entity receives, from the UE, UE capability information.
  • the UE capability information includes supported CSI-RS port information, which includes at least one of: a number of horizontal ports, a number of vertical ports, a number of antenna panels, one or more supported sets of CSI-RS port indexes, one or more measured CSI-RS ports for each CSI-RS port structure, an association ID, a dataset ID, or a machine learning (ML) model ID.
  • ML machine learning
  • an apparatus includes one or more radio frequency (RF) modems; a processor coupled to the one or more RF modems; and at least one memory storing executable instructions.
  • the executable instructions manipulate at least one of the processor or the one or more RF modems to perform the above methods, which are discussed in details herein.
  • Fig. 1 illustrates a diagram of a wireless communications system that includes multiple user equipments (UEs) and network entities in communication over one or more cells, according to aspects of this disclosure.
  • UEs user equipments
  • Fig. 2 illustrates an example channel state information (CSI) measurement and time domain prediction for reducing CSI reference signal (CSI-RS) overhead, in accordance with aspects of this disclosure.
  • CSI channel state information
  • Fig. 3 illustrates an example diagram of CSI report based on CSI-RS with overhead reduction, in accordance with aspects of this disclosure.
  • Fig. 4 illustrates an example diagram of UE behavior for the CSI report based on CSI-RS with overhead reduction, in accordance with aspects of this disclosure.
  • Fig. 5 illustrates an example diagram of network entity behavior for the CSI report based on CSI-RS with overhead reduction, in accordance with aspects of this disclosure.
  • Fig. 6A illustrates an example CSI measurement with frequency domain (FD) channel or CSI prediction with different FD density in different subbands, in accordance with aspects of this disclosure.
  • FD frequency domain
  • Fig. 6B illustrates an example CSI measurement with FD channel or CSI prediction with different CSI-RS ports subsets in different subbands, in accordance with aspects of this disclosure.
  • Fig. 6C illustrates an example CSI measurement with FD channel or CSI prediction with different FD density in different subbands and different transmission occasions, in accordance with aspects of this disclosure.
  • Fig. 6D illustrates an example CSI measurement with FD channel/CSI prediction with different CSI-RS ports subsets in different subband and different transmission occasions, in accordance with aspects of this disclosure.
  • Fig. 7A illustrates an example for the CSI-RS based on the non-zero-power (NZP) or zero-power (ZP) state for each CSI-RS port, in accordance with aspects of this disclosure.
  • Fig. 7B illustrates an example for the CSI-RS port to codebook port association, in accordance with aspects of this disclosure.
  • Fig. 8A illustrates an example for calculation of a rank index or rank indicator, precoder matrix indicator, and/or channel quality indicator based on the predicted channel for all the CSI-RS ports, in accordance with aspects of this disclosure.
  • Fig. 8B illustrates an example for calculation of a rank index or rank indicator, precoder matrix indicator, and/or channel quality indicator based on the predicted channel and measured channel for the CSI-RS ports, in accordance with aspects of this disclosure.
  • Fig. 9 illustrates an example for polarization specific configuration, in accordance with aspects of this disclosure.
  • Fig. 10 illustrates an example for activation/deactivation for CSI-RS transmission occasions, in accordance with aspects of this disclosure.
  • Fig. 14 is a diagram illustrating a hardware implementation for one or more example network entities.
  • the present disclosure provides techniques for reducing overhead in channel state information (CSI) reference signals for prediction based CSI feedback.
  • Example methods include overhead reduction applicable to predictions in frequency domain (FD) , spatial domain (SD) , time domain (TD) , or any combination thereof.
  • the overhead reduction is beneficial for improving downlink performance when a network entity transmits downlink signals from a large number of antenna ports (e.g., 128 ports, 256 ports, or more) , which conventionally result in a high overhead.
  • This disclosure provides methods for reducing CSI-RS overhead using machine-learning enabled CSI prediction, such as using FD prediction, SD prediction, or a joint prediction in FD, SD, and TD.
  • the disclosed methods may handle burst like traffic that usually has high overhead within the measurement window and could increase latency for the package with the arrival time within the measurement window.
  • the methods may reduce the CSI-RS overhead for each slot and avoids the latency increase.
  • a network entity may configure a UE about a CSI report based on FD prediction, SD prediction, or both.
  • the configuration includes a CSI-RS resource set for channel measurement of a target frequency band or a target spatial domain (e.g., corresponding to antenna ports configurations) .
  • the configuration indicates at least one of a subband of the target frequency band of the CSI-RS resource set for the CSI report, or a subset of antenna ports of the target spatial domain of the CSI-RS resource set for the CSI report.
  • the UE receives a CSI-RS according to the CSI-RS resource set of the configuration.
  • the UE may perform CSI prediction or CSI calculation using a subset of subbands or a subset of antenna ports for the CSI report.
  • the UE then transmits the CSI report based on measurements according to at least one of the subband of the target frequency band or the subset of antenna ports of the target spatial domain.
  • the CSI report includes channel information predicted based on the at least one of the subband of the target frequency band or the subset of antenna ports of the target spatial domain.
  • various methods and techniques are used to reduce or limit the CSI-RS overhead in such situations.
  • aspects of this disclosure for reducing overhead in CSI reporting include a wireless communication method by a UE.
  • the example method includes receiving, from a network entity, a CSI report configuration configuring: a CSI-RS resource set for channel measurement, one or more subbands for reporting CSI, and one or more antenna ports for reporting the CSI.
  • the method further includes receiving, from the network entity, a CSI-RS according to the CSI-RS resource set.
  • the method includes transmitting, to the network entity, a CSI report based on prediction or measurement of the CSI according to at least a subset of the one or more subbands or to at least a subset of the one or more antenna ports.
  • Complimentary aspects of the disclosure include an example method of configuring a CSI report of reduced overhead by a network entity.
  • the example method includes transmitting, to a UE, a CSI report configuration configuring: a CSI-RS resource set for channel measurement, one or more subbands for reporting CSI, and one or more antenna ports for reporting the CSI.
  • the method includes transmitting, to the UE, a CSI-RS according to the CSI-RS resource set.
  • the method further includes receiving, from the UE, a CSI report based on prediction or measurement of the CSI according to at least a subset of the one or more subbands or to at least a subset of the one or more antenna ports.
  • Fig. 1 illustrates a diagram 100 of a wireless communications system associated with multiple cells 190.
  • the wireless communications system includes user equipments (UEs) 102 and base stations/network entities 104.
  • Some base stations may include an aggregated base station architecture and other base stations may include a disaggregated base station architecture.
  • the aggregated base station architecture utilizes a radio protocol stack that is physically or logically integrated within a single radio access network (RAN) node.
  • RAN radio access network
  • a disaggregated base station architecture utilizes a protocol stack that is physically or logically distributed among two or more units (e.g., radio unit (RU) 106, distributed unit (DU) 108, central unit (CU) 110) .
  • RU radio unit
  • DU distributed unit
  • CU central unit
  • a CU 110 is implemented within a RAN node, and one or more DUs 108 may be co-located with the CU 110, or alternatively, may be geographically or virtually distributed throughout one or multiple other RAN nodes.
  • the DUs 108 may be implemented to communicate with one or more RUs 106. Any of the RU 106, the DU 108 and the CU 110 may be implemented as virtual units, such as a virtual radio unit (VRU) , a virtual distributed unit (VDU) , or a virtual central unit (VCU) .
  • the base station/network entity 104 e.g., an aggregated base station or disaggregated units of the base station, such as the RU 106 or the DU 108) , may be referred to as a transmission reception point (TRP) .
  • TRP transmission reception point
  • Operations of the base station (BS) 104 and/or network designs may be based on aggregation characteristics of base station functionality.
  • disaggregated base station architectures are utilized in an integrated access backhaul (IAB) network, an open-radio access network (O-RAN) network, or a virtualized radio access network (vRAN) , which may also be referred to a cloud radio access network (C-RAN) .
  • Disaggregation may include distributing functionality across the two or more units at various physical locations, as well as distributing functionality for at least one unit virtually, which may enable flexibility in network designs.
  • the various units of the disaggregated base station architecture, or the disaggregated RAN architecture may be configured for wired or wireless communication with at least one other unit.
  • the base stations (BSs) 104d, 104e and/or the RUs 106a, 106b, 106c, 106d may communicate with the UEs 102a, 102b, 102c, 102d, and/or 102s via one or more radio frequency (RF) access links based on a Uu interface.
  • RF radio frequency
  • multiple RUs 106 and/or BSs 104 may simultaneously serve the UEs 102, such as by intra-cell and/or inter-cell access links between the UEs 102 and the RUs 106/BSs 104.
  • the RU 106, the DU 108, and the CU 110 may include (or may be coupled to) one or more interfaces configured to transmit or receive information/signals via a wired or wireless transmission medium.
  • a wired interface may be configured to transmit or receive the information/signals over a wired transmission medium, such as via the fronthaul link 160 between the RU 106d and the baseband unit (BBU) 112 of the BS 104d associated with the cell 190d.
  • the BBU 112 includes a DU 108 and a CU 110, which may also have a wired interface (e.g., midhaul link) configured between the DU 108 and the CU 110 to transmit or receive the information/signals between the DU 108 and the CU 110.
  • a wired interface e.g., midhaul link
  • a wireless interface which may include a receiver, a transmitter, or a transceiver, such as an RF transceiver, configured to transmit and/or receive the information/signals via the wireless transmission medium, such as for information communicated between the RU 106a of the cell 190a and the BS 104e of the cell 190e via cross-cell communication beams 136-138 of the RU 106a and the BS 104e.
  • a wireless interface which may include a receiver, a transmitter, or a transceiver, such as an RF transceiver, configured to transmit and/or receive the information/signals via the wireless transmission medium, such as for information communicated between the RU 106a of the cell 190a and the BS 104e of the cell 190e via cross-cell communication beams 136-138 of the RU 106a and the BS 104e.
  • the RUs 106 may be configured to implement lower layer functionality.
  • the RU 106 is controlled by the DU 108 and may correspond to a logical node that hosts RF processing functions, or lower layer PHY functionality, such as execution of fast Fourier transform (FFT) , inverse FFT (iFFT) , digital beamforming, physical random access channel (PRACH) extraction and filtering, etc.
  • FFT fast Fourier transform
  • iFFT inverse FFT
  • PRACH physical random access channel extraction and filtering
  • the functionality of the RU 106 may be based on the functional split, such as a functional split of lower layers.
  • the RUs 106 may transmit or receive over-the-air (OTA) communication with one or more UEs 102.
  • the RU 106b of the cell 190b communicates with the UE 102b of the cell 190b via a first set of communication beams 132 of the RU 106b and a second set of communication beams 134b of the UE 102b, which may correspond to inter-cell communication beams or, in some examples, cross-cell communication beams.
  • the UE 102b of the cell 190b may communicate with the RU 106a of the cell 190a via a third set of communication beams 134a of the UE 102b and a fourth set of communication beams 136 of the RU 106a.
  • DUs 108 may control both real-time and non-real-time features of control plane and user plane communications of the RUs 106.
  • the BS 104 may include at least one of the RU 106, the DU 108, or the CU 110.
  • the BSs 104 provide the UEs 102 with access to a core network.
  • the BSs 104 may relay communications between the UEs 102 and the core network (not shown) .
  • the BSs 104 may be associated with macrocells for higher-power cellular base stations and/or small cells for lower-power cellular base stations.
  • the cell 190e may correspond to a macrocell
  • the cells 190a-190d may correspond to small cells.
  • Small cells include femtocells, picocells, microcells, etc.
  • a network that includes at least one macrocell and at least one small cell may be referred to as a “heterogeneous network. ”
  • Uplink transmissions from a UE 102 to a BS 104/RU 106 are referred to as uplink (UL) transmissions, whereas transmissions from the BS 104/RU 106 to the UE 102 are referred to as downlink (DL) transmissions.
  • Uplink transmissions may also be referred to as reverse link transmissions and downlink transmissions may also be referred to as forward link transmissions.
  • the RU 106d utilizes antennas of the BS 104d of cell 190d to transmit a downlink/forward link communication to the UE 102d or receive an uplink/reverse link communication from the UE 102d based on the Uu interface associated with the access link between the UE 102d and the BS 104d/RU 106d.
  • Communication links between the UEs 102 and the BSs 104/RUs 106 may be based on multiple-input and multiple-output (MIMO) antenna technology, including spatial multiplexing, beamforming, and/or transmit diversity.
  • the communication links may be associated with one or more carriers.
  • the UEs 102 and the BSs 104/RUs 106 may utilize a spectrum bandwidth of Y MHz (e.g., 5, 10, 15, 20, 100, 400, 800, 1600, 2000, etc. MHz) per carrier allocated in a carrier aggregation of up to a total of Yx MHz, where x component carriers (CCs) are used for communication in each of the uplink and downlink directions.
  • Y MHz e.g., 5, 10, 15, 20, 100, 400, 800, 1600, 2000, etc. MHz
  • CCs component carriers
  • the carriers may or may not be adjacent to each other along a frequency spectrum.
  • uplink and downlink carriers may be allocated in an asymmetric manner, with more or fewer carriers allocated to either the uplink or the downlink.
  • a primary component carrier and one or more secondary component carriers may be included in the component carriers.
  • the primary component carrier may be associated with a primary cell (Pcell) and a secondary component carrier may be associated with a secondary cell (Scell) .
  • Some UEs 102 may perform device-to-device (D2D) communications over sidelink.
  • D2D device-to-device
  • a sidelink communication/D2D link utilizes a spectrum for a wireless wide area network (WWAN) associated with uplink and downlink communications.
  • WWAN wireless wide area network
  • Such sidelink/D2D communication may be performed through various wireless communications systems, such as wireless fidelity (Wi-Fi) systems, Bluetooth systems, Long Term Evolution (LTE) systems, New Radio (NR) systems, etc.
  • Wi-Fi wireless fidelity
  • LTE Long Term Evolution
  • NR New Radio
  • the UEs 102 and the BSs 104/RUs 106 may each include multiple antennas.
  • the multiple antennas may correspond to antenna elements, antenna panels, and/or antenna arrays that may facilitate beamforming operations.
  • the RU 106b transmits a downlink beamformed signal based on a first set of communication beams 132 to the UE 102b in one or more transmit directions of the RU 106b.
  • the UE 102b may receive the downlink beamformed signal based on a second set of communication beams 134b from the RU 106b in one or more receive directions of the UE 102b.
  • the UE 102b may also transmit an uplink beamformed signal (e.g., sounding reference signal (SRS) ) to the RU 106b based on the second set of communication beams 134b in one or more transmit directions of the UE 102b.
  • the RU 106b may receive the uplink beamformed signal from the UE 102b in one or more receive directions of the RU 106b.
  • the UE 102b may perform beam training to determine the best receive and transmit directions for the beamformed signals.
  • the transmit and receive directions for the UEs 102 and the BSs 104/RUs 106 may or may not be the same.
  • beamformed signals may be communicated between a first base station/RU 106a and a second BS 104e.
  • the BS 104e of the cell 190e may transmit a beamformed signal to the RU 106a based on the communication beams 138 in one or more transmit directions of the BS 104e.
  • the RU 106a may receive the beamformed signal from the BS 104e of the cell 190e based on the RU communication beams 136 in one or more receive directions of the RU 106a.
  • the B S 104e transmits a downlink beamformed signal to the UE 102e based on the communication beams 138 in one or more transmit directions of the BS 104e.
  • the UE 102e receives the downlink beamformed signal from the BS 104e based on UE communication beams 130 in one or more receive directions of the UE 102e.
  • the UE 102e may also transmit an uplink beamformed signal to the BS 104e based on the UE communication beams 130 in one or more transmit directions of the UE 102e, such that the BS 104e may receive the uplink beamformed signal from the UE 102e in one or more receive directions of the BS 104e.
  • the BS 104 may include and/or be referred to as a network entity. That is, “network entity” may refer to the BS 104 or at least one unit of the BS 104, such as the RU 106, the DU 108, and/or the CU 110.
  • the BS 104 may also include and/or be referred to as a next generation evolved Node B (ng-eNB) , a next generation NB (gNB) , an evolved NB (eNB) , an access point, a base transceiver station, a radio base station, a radio transceiver, a transceiver function, a basic service set (BSS) , an extended service set (ESS) , a TRP, a network node, network equipment, or other related terminology.
  • ng-eNB next generation evolved Node B
  • gNB next generation NB
  • eNB evolved NB
  • an access point a base transceiver station, a radio base station, a radio transceiver, a transceiver function, a basic service set (BSS) , an extended service set (ESS) , a TRP, a network node, network equipment, or other related terminology.
  • BSS basic service set
  • ESS extended service set
  • the BS 104 or an entity at the BS 104 may be implemented as an IAB node, a relay node, a sidelink node, an aggregated (monolithic) base station, or a disaggregated base station including one or more RUs 106, DUs 108, and/or CUs 110.
  • a set of aggregated or disaggregated base stations may be referred to as a next generation-radio access network (NG-RAN) .
  • the UE 102a operates in dual connectivity (DC) with the BS 104e and the base station/RU 106a.
  • the BS 104e may be a master node and the base station/RU 160a may be a secondary node.
  • Uplink/downlink signaling may also be communicated via a satellite positioning system (SPS) 114.
  • the SPS 114 associated with the cell 190c may be in communication with one or more UEs 102, such as the UE 102c, and one or more BSs 104/RUs 106, such as the RU 106c.
  • the SPS 114 may correspond to one or more of a Global Navigation Satellite System (GNSS) , a global position system (GPS) , a non-terrestrial network (NTN) , or other satellite position/location system.
  • GNSS Global Navigation Satellite System
  • GPS global position system
  • NTN non-terrestrial network
  • the SPS 114 may be associated with LTE signals, NR signals (e.g., based on round trip time (RTT) and/or multi-RTT) , wireless local area network (WLAN) signals, a terrestrial beacon system (TBS) , sensor-based information, NR enhanced cell ID (NR E-CID) techniques, downlink angle-of-departure (DL-AoD) , downlink time difference of arrival (DL-TDOA) , uplink time difference of arrival (UL-TDOA) , uplink angle-of-arrival (UL-AoA) , and/or other systems, signals, or sensors.
  • NR signals e.g., based on round trip time (RTT) and/or multi-RTT
  • WLAN wireless local area network
  • TBS terrestrial beacon system
  • sensor-based information e.g., NR enhanced cell ID (NR E-CID) techniques, downlink angle-of-departure (DL-AoD) , downlink time difference of arrival (DL-TDOA)
  • any of the UEs 102 may include an CSI report component 140 configured to receive, from the network entity 104, a CSI report configuration configuring a CSI-RS resource set for channel measurement, one or more subbands for reporting CSI, and one or more antenna ports for reporting the CSI.
  • the CSI report component 140 is further configured to receive, from the network entity 104, a CSI-RS according to the CSI-RS resource set, and to transmit, to the network entity 104, a CSI report based on prediction or measurement of the CSI according to at least a subset of the one or more subbands or to at least a subset of the one or more antenna ports.
  • Fig. 1 describes a wireless communication system that may be implemented in connection with aspects of one or more other figures described herein.
  • 5G NR 5G Advanced and future versions
  • LTE Long Term Evolution
  • LTE-A LTE-advanced
  • 6G 6G
  • the UE 102 may report CSI based on ML based compression and/or prediction.
  • AI artificial intelligence
  • ML machine learning
  • Fig. 2 illustrates an example CSI measurement and time domain (TD) prediction 200 for reducing CSI-RS overhead, in accordance with aspects of this disclosure.
  • a UE may use TD CSI prediction 230 to reduce CSI-RS overhead, such as by measuring some CSIs and predicting future CSIs.
  • the UE measures a first set of CSI-RS resources 210 in the CSI measurement window 212.
  • the network entity does not transmit CSI-RS for the future CSI prediction slots 220 in the CSI prediction window 222.
  • the UE performs CSI prediction 230 using a trained machine learning (ML) model (not separately shown, but within the TD CSI prediction 230) .
  • ML machine learning
  • the network entity can trigger the aperiodic CSI-RS in the CSI measurement window and refrain from triggering aperiodic CSI-RS in the CSI prediction window 222.
  • the CSI-RS overhead reduction in time domain can reduce the overall overhead for the CSI-RS in some cases. Nonetheless, as demands for accuracy increases, the CSI-RS overhead within a measurement window may be high or increase accordingly.
  • the TD prediction may not help burst-like traffic that has high overhead within the measurement window. The high overhead increases latency for the package with the arrival time within the measurement window.
  • This disclosure introduces methods including spatial domain (SD) prediction, frequency domain (FD) prediction, and joint predictions of TD, SD, or FD to limit or reduce the CSI-RS overhead.
  • Fig. 3 illustrates an example diagram 300 of CSI report based on CSI-RS with overhead reduction, in accordance with aspects of this disclosure.
  • the UE 102 may report 302 the UE capabilities indicating the supported configurations for the CSI report based on CSI-RS with overhead reduction.
  • the UE capabilities may include at least one of: whether the UE 102 supports to report CSI for more subbands than the measured subbands (FD prediction) ; whether the UE 102 supports to report CSI for more ports than the measured ports (SD prediction) ; whether the UE 102 supports joint FD/SD prediction; or whether the UE 102 supports to report CSI for future slot (s) (TD prediction) based on FD/SD prediction.
  • the network entity 104 transmits 304 a control signaling configuring at least a CSI report configuration in the UE.
  • the control signaling indicates at least one of: a CSI-RS resource set for channel measurement, a subband for CSI report, ports for CSI report, or report quantity.
  • the control signaling may optionally indicate: a codebook for CSI report, interference measurement resource (e.g., CSI-RS, CSI interference measurement resource (CSI-IM) or SRS for interference measurement) , and/or configurations for SD/FD/TD prediction.
  • the network entity configures the CSI-RS resource for channel measurement based on a subset of or all the subbands and/or a subset of or all the ports for CSI report.
  • the network entity may configure the codebook based on a Typel/Type2 codebook or a codebook for ML based CSI compression, e.g., a codebook associated with at least one of the association ID, model ID or dataset ID.
  • the network entity 104 may transmit the control signaling by RRC signaling, e.g., RRCReconfiguration or CSI-ReportConfig.
  • the network entity may provide some of the configurations or update some of the configurations by medium access control (MAC) control element (CE) , e.g., MAC CE activating the (semi-persistent) CSI report, or downlink control information (DCI) , e.g., different triggering states for the DCI triggering the (aperiodic) CSI report may correspond to different configurations.
  • MAC medium access control
  • CE control element
  • DCI downlink control information
  • the UE may transmit 306 a report on the recommended CSI-RS configuration for SD/FD prediction after receiving 304 the control signaling.
  • the UE 102 may transmit 306 the report before receiving 304 the control signaling, e.g., during the UE capability report (e.g., the UE 102 transmitting 302 the UE capability along with the recommended CSI-RS configuration for SD/FD prediction) .
  • the network entity 104 may transmit 308 MAC CE or DCI activating or triggering the CSI report.
  • the network entity 104 may transmit 308 MAC CE or DCI activating or triggering the CSI-RS.
  • the network entity 104 transmits 310, to the UE 102, the configured CSI-RS resources for channel measurement.
  • the network entity 104 may also transmit 310 the configured CSI-RS or CSI interference measurement (CSI-IM) for interference measurement.
  • CSI-IM CSI interference measurement
  • the UE 102 may transmit 312 the CSI report including CSI based on the configured subbands and the configured ports.
  • the UE may transmit 312 the CSI report by an RRC message, e.g., an RRC message for performance monitoring, MAC CE, e.g., a MAC CE for performance monitoring, or uplink control information (UCI) on PUCCH, e.g., short PUCCH (PUCCH with less than 4 symbols) or long PUCCH (PUCCH with 4 or more symbols) or PUSCH.
  • RRC message e.g., an RRC message for performance monitoring
  • MAC CE e.g., a MAC CE for performance monitoring
  • uplink control information e.g., short PUCCH (PUCCH with less than 4 symbols) or long PUCCH (PUCCH with 4 or more symbols) or PUSCH.
  • Fig. 4 illustrates an example diagram 400 of UE behavior for the CSI report based on CSI-RS with overhead reduction, in accordance with aspects of this disclosure.
  • the UE may optionally transmit 402, to a network entity, the UE capability on supported configurations for CSI report based on predictions in SD, FD, TD, or any joint combination.
  • the UE receives 404 a control signaling from the network entity.
  • the control signaling configures at least: a first CSI report configuration in the UE.
  • the control signaling indicates at least a CSI-RS resource set for channel measurement, one or more subbands for CSI report, one or more ports for CSI report, or report quantity.
  • the control signaling optionally indicates a codebook for CSI report, interference measurement resource, and/or configurations for SD/FD/TD prediction.
  • the CSI-RS may be configured based on a subset of or all the subbands and/or a subset of or all the ports for CSI report.
  • the UE may optionally transmit 406 a report on recommended CSI-RS configuration for SD or FD prediction.
  • the recommended CSI-RS configuration may be transmitted with the UE capability.
  • the UE may optionally receive 408 a MAC CE or DCI triggering the configured CSIs report and/or the configured CSI-RS resources for channel measurement.
  • the UE may receive 408 the MAC CE or DCI triggering the configured CSI-RS or CSI-IM for interference measurement.
  • the UE receives 410 configured CSI-RS resources for channel measurement and/or the configured CSI-RS or CSI-IM for interference measurement.
  • the UE determines 411 whether to perform the CSI prediction or CSI calculation based on whether the CSI-RS are configured based on a subset of subbands or a subset of ports for CSI report.
  • the UE then performs measurement or prediction of CSI based on the configured subbands and the configured port.
  • the UE transmits 412 the CSI report including the predicted or measured CSI based on the configured subbands and the configured port.
  • Fig. 5 illustrates an example diagram 500 of network entity behavior for the CSI report based on CSI-RS with overhead reduction, in accordance with aspects of this disclosure.
  • the network entity may optionally receive 502, from the UE, the UE capability on supported configurations for CSI report based on predictions in SD, FD, TD, or any joint combination.
  • the network entity transmits 504 a control signaling to the UE.
  • the control signaling configures at least: a first CSI report configuration in the UE.
  • the control signaling indicates at least a CSI-RS resource set for channel measurement, one or more subbands for CSI report, one or more ports for CSI report, or report quantity.
  • the control signaling optionally indicates a codebook for CSI report, interference measurement resource, and/or configurations for SD/FD/TD prediction.
  • the CSI-RS may be configured based on a subset of or all the subbands and/or a subset of or all the ports for CSI report.
  • the network entity may optionally receive 506 a report on recommended CSI-RS configuration for SD or FD prediction.
  • the recommended CSI-RS configuration may be transmitted with the UE capability.
  • the network entity may optionally transmit 508 a MAC CE or DCI triggering the configured CSIs report and/or the configured CSI-RS resources for channel measurement.
  • the network entity may transmit 508 the MAC CE or DCI triggering the configured CSI-RS or CSI-IM for interference measurement.
  • the network entity transmits 510 configured CSI-RS resources for channel measurement and/or the configured CSI-RS or CSI-IM for interference measurement.
  • the network entity receives 512 the CSI report including the predicted or measured CSI based on the configured subbands and the configured port.
  • the network entity may optionally predict 513 the CSI for one or more subbands and/or one or more ports and transmits the PDSCH based on the predicted CSI.
  • a RRC signaling may indicate a RRC reconfiguration message from network entity to UE, or a System Information Block (SIB) , where the SIB can be an existing SIB (e.g., SIB1) or a new SIB (e.g., SIB J, where J is an integer above 21) transmitted by gNB.
  • SIB System Information Block
  • the network entity may receive the UE capability from a UE or from a core network (e.g., Access and Mobility Management Function (AMF) ) or another network entity.
  • AMF Access and Mobility Management Function
  • the network entity 102 may configure the UE 104 to report channel delay information for the network entity to determine the subband size and the CSI-RS density for each subband.
  • the network entity may configure at least one CSI-RS resource, e.g., 1-port CSI-RS resource, or at least one CSI-RS resource set, e.g., CSI-RS for tracking (tracking reference signal, TRS) for the channel delay information report.
  • the network entity may transmit the CSI-RS resource or resource set in aperiodic, semi-persistent or periodic manner.
  • the UE may report the channel delay information in aperiodic, semi-persistent or periodic manner based on uplink control information (UCI) on PUCCH or PUSCH or MAC CE or an RRC message.
  • UCI uplink control information
  • the UE may receive the configuration of the PUCCH or PUSCH resource by RRC signaling, MAC CE or DCI.
  • the channel delay information may include at least one of the following.
  • the channel delay information may include channel delay profile, e.g., delay for the strongest K path (s) , where K may be configured by the network entity or reported by the UE.
  • the channel delay information may include channel power delay profile, e.g., delay and power/amplitude for the strongest K path (s) or power/amplitude for the first M samples, where K and M may be configured by the network entity or reported by the UE.
  • the channel delay information may include channel impulse response (CIR) , e.g., delay, power/amplitude and phase for the strongest K path (s) or power/amplitude and phase for the first M samples, where K and M may be configured by the network entity or reported by the UE.
  • the channel delay information may further include channel delay spread, the maximum or average channel delay, or a UE recommended subband size.
  • the UE may report the channel delay information above based on a reference subcarrier spacing, which may be pre-defined or configured by the network entity or reported by the UE. In some implementations, the UE may report multiple channel delay information above based on multiple subcarrier spacings.
  • the network entity may configure the UE to report one or multiple channel delay information for one or multiple CSI-RS resources or CSI-RS resource sets.
  • the UE 102 may report the supported CSI-RS pattern information for FD prediction.
  • the UE may transmit the report by RRC message, e.g., UE capability or UE assistance information, or MAC CE, or UCI.
  • the UE may report at least one of the following on the supported CSI-RS pattern information.
  • the UE may report the supported/recommended FD density for CSI-RS from each port for a first set of subband, e.g., X RE (s) /RB or X RE (s) /subband.
  • the UE may report the supported/recommended FD density for CSI-RS from each port for a second set of subband, e.g., Y RE (s) /RB or Y RE (s) /subband.
  • the UE may report the subband index (es) for the first set of subband, the subband index (es) for the second set of subband, and/or the subband size.
  • the UE may report the supported/recommended FD density for CSI-RS from each port for the whole bandwidth, e.g., Z RE (s) /RB or Z RE (s) /subband.
  • the UE may report the supported/recommended association ID (s) for the FD prediction based CSI report.
  • the different association IDs may correspond to different scenarios and/or antenna structure in network entity side.
  • the UE may report the supported/recommended dataset ID (s) for the FD prediction based CSI report.
  • the different dataset IDs may correspond to different types of model input, e.g., different CSI-RS FD densities, and/or model output, e.g., different bandwidth or subband configuration for CSI report.
  • the UE may report the supported/recommended model ID (s) for the FD prediction based CSI report.
  • the different model IDs may correspond to different scenarios, antenna structures, model input types and/or model output types.
  • the UE may report the information above based on a reference bandwidth.
  • the reference bandwidth may be predefined, configured by the network entity or reported by the UE.
  • the UE may report multiple information above for multiple bandwidth configurations.
  • the UE may report the information above based on a reference subcarrier spacing, which may be predefined, configured by the network entity, or reported by the UE.
  • the UE may report multiple information above for multiple subcarrier spacings.
  • Fig. 6A illustrates an example CSI measurement 610 with frequency domain (FD) channel or CSI prediction with different FD density in different subbands, in accordance with aspects of this disclosure.
  • the network entity may configure an FD density for CSI-RS from each port for a first set of subbands/RBs 620, e.g., X RE (s) /RB or X RE (s) /subband, and the FD density for CSI-RS from each port for a second set of subbands/RBs 622, e.g., Y RE (s) /RB or Y RE (s) /subband.
  • the UE may predict the channel or CSI for the odd subbands based on the measured channel or CSI from the even subbands. Then the UE may report the CSI for each configured subband. In some other implementations, the UE may report the CSI for the first set of subbands and the network entity may predict the CSI for the other set of subbands.
  • the network entity may further configure at least one of the following on the FD density configuration for a CSI-RS resource. For example, the network entity may configure the subband/RB index (es) for the first set of subbands/RBs. The network entity may further configure the subband/RB index (es) for the second set of subbands/RBs, a subband size, an FD density for CSI-RS from each port for the whole bandwidth (e.g., Z RE (s) /RB or Z RE (s) /subband) , or the RB (s) /subband (s) for each CSI-RS port. The network entity may also configure the association ID for UE-side model based FD prediction, a dataset ID for UE-side model based FD prediction, and/or a machine learning (ML) model ID for UE-side model based FD prediction.
  • ML machine learning
  • Fig. 6B illustrates an example CSI measurement 612 with FD channel or CSI prediction with different CSI-RS ports subsets in different subbands (630 and 632) , in accordance with aspects of this disclosure.
  • the network entity may transmit different CSI-RS ports in different subbands/RBs (630 and 632) .
  • the network entity may transmit a first set of CSI-RS ports (e.g., port 3000 to port 3000+ p/2 -1, where p is number of ports) in odd subbands 630 and a second set of CSI-RS ports (e.g., port 3000 + p/2 to port 3000 + p -1, where p is number of ports) in even subbands 632.
  • a first set of CSI-RS ports e.g., port 3000 to port 3000+ p/2 -1, where p is number of ports
  • a second set of CSI-RS ports e.g., port 3000 + p/2 to port 3000 +
  • the UE may predict the channel from all the antenna ports according to CSI-RS in different subbands.
  • the UE may report the CSI measured from the subset of CSI-RS ports for each subband, and the network entity may predict the CSI-RS from all the CSI-RS ports for each subband based on the received CSI.
  • the network entity may configure common FD density for all the CSI-RS ports. In some other implementations, the network entity may configure different FD densities for different CSI-RS ports. In one example, the network entity may configure a first FD density for a first set of CSI-RS port (s) and configure a second FD density for a second set of CSI-RS port (s) .
  • Fig. 6C illustrates an example CSI measurement 614 with FD channel or CSI prediction with different FD density in different subbands (640 and 642) and different transmission occasions, in accordance with aspects of this disclosure.
  • the network entity may configure different FD density configurations above for different transmission occasions of a CSI-RS resource or of different CSI-RS resources from the same ports in different symbols/slots.
  • the UE may perform the FD prediction based on multiple transmission occasions of a CSI-RS resource or different CSI-RS resources from the same ports in different symbols/slots.
  • the UE measures the first set of subband 640 at different CSI-RS resources (different frequencies) at different times (different transmission occasions) .
  • the UE performs FD channel/CSI prediction based on the two measurements of the first set of subband 640 to determine the second set of subband 642 without CSI-RS.
  • the network entity may further configure the transmission occasions of the CSI-RS resource (s) for the CSI calculation.
  • the UE may report one CSI corresponding to one of the configured transmission occasions or multiple configured transmission occasions or report multiple CSIs corresponding to multiple configured transmission occasions (e.g., one CSI per transmission occasion) .
  • Fig. 6D illustrates an example CSI measurement 616 with FD channel/CSI prediction with different CSI-RS ports subsets in different subbands (650 and 652) and different transmission occasions, in accordance with aspects of this disclosure.
  • the UE measures the first set of subband 650 and the second set of subband 652 at different CSI-RS resources.
  • the UE performs FD channel/CSI prediction based on the measurements at an earlier time instance and predicts the CSI for both the first set of subband 650 and the second set of subband 652 at a future time instance (or predicting channel or CSI for all subbands and the transmission occasions) .
  • the network entity may configure the subband index (es) for the UE to report the subband CSIs based on a reference bandwidth, which may be pre-defined, or configured by the network entity or reported by the UE.
  • the network entity may provide the configuration by RRC signaling, MAC CE, or DCI.
  • the network entity may configure the UE to report at least one of the RI/PMI/CQI for each subband, and may configure the UE to report at least one of the RI/PMI/CQI for wideband. In one example, the network entity may configure the UE to report wideband RI/PMI/CQI and subband PMI/CQI. In another example, the network entity may configure the UE to report wideband RI/PMI and subband PMI. In another example, the network entity may configure the UE to report wideband RI/CQI and subband CQI.
  • the UE may calculate the subband/wideband RI/PMI/CQI based on the predicted channel or measured channel (this may be applicable for subbands with CSI-RS from all ports) from the received CSI-RS.
  • the network entity may configure the subband index (es) for the UE the report the subband CSIs from the subbands with CSI-RS.
  • the network entity may provide the configuration by RRC signaling, MAC CE or DCI.
  • the network entity may configure the UE to report at least one of the RI/PMI/CQI for each subband, and may configure the UE to report at least one of the RI/PMI/CQI for wideband.
  • the network entity may configure the UE to report wideband RI/PMI/CQI and subband PMI/CQI.
  • the network entity may configure the UE to report wideband RI/PMI and subband PMI.
  • the network entity may configure the UE to report wideband RI/CQI and subband CQI.
  • the UE may calculate the subband/wideband RI/PMI/CQI based on the measured channel from the received CSI-RS. Then based on the received subband CSI, the network entity may predict the CSI for other subbands.
  • the network entity may configure the UE to report signal-to-interference plus noise ratio (SINR) , e.g., layer 1 SINR, and/or interference strength, e.g., reference signal strength indication (RSSI) , and/or signal strength for each layer, for each configured subband in addition to the RI/PMI/CQI.
  • SINR signal-to-interference plus noise ratio
  • RSSI reference signal strength indication
  • the network entity may configure the UE to report wideband SINR and/or interference strength and/or signal strength for each layer in addition to the RI/PMI/CQI.
  • the network entity may further configure a CSI-RS resource or CSI CSI-IM resource for interference measurement. Then after receiving the CSI and L1-SINR and/or interference strength and/or signal strength for the configured subband (s) , the network entity may predict the precoder and MCS for other subband (s) .
  • the UE can identify the signal strength S j, b for layerj in a subband b as follows:
  • H k indicates the measured channel at subcarrier k with the dimension of N Rx ⁇ N Tx ;
  • N Rx is the number of receiving antenna ports in UE side and N Tx is the number of transmission antenna ports in network entity side;
  • N b is the number of subcarriers in subband b;
  • G b is the subcarrier set allocated for CSI-RS for subband b;
  • W k , j is the precoder for layerj for the subband b corresponding to subcarrier k, which are based on the reported PMI.
  • the UE may report the supported CSI-RS port information for SD prediction.
  • the UE may transmit the report by RRC message, e.g., UE capability or UE assistance information, or MAC CE, or UCI on PUCCH or PUSCH.
  • the UE may report at least one of the following on the supported CSI-RS pattern information.
  • the UE may report the supported/recommended C SI ports structure (s) , e.g., number of horizontal ports (N 1) , number of vertical ports (N2) , number of antenna panels (Ng) .
  • the UE may report one or multiple sets of supported/recommended measured CSI port index (es) or number of measured CSI ports for each CSI port structure.
  • the UE may report a set of measured port index (es) as a bitmap with N1 *N2 bit, where bit x indicates whether the CSI port x is supported/recommended as a measured port or not.
  • es measured port index
  • Fig. 7A illustrates an example 700 for the CSI-RS based on the non-zero-power (NZP) or zero-power (ZP) state for each CSI-RS port, in accordance with aspects of this disclosure.
  • the network entity may configure a NZP or ZP state indicator, corresponding to NZP CSI-RS ports and ZP CSI-RS ports.
  • the network entity may further configure the NZP or ZP state for each CSI-RS port or port group, and/or the number of CSI-RS ports.
  • the network entity may configure the codebook for CSI report based on the same number of ports as the number of CSI-RS ports.
  • the CSI-RS ports are one-to-one mapped to the ports for the configured codebook based on a pre-defined order or an order configured by the network entity.
  • the network entity may configure a bitmap with P bits indicating the NZP/ZP state for each CSI-RS port from the configured P CSI-RS ports, where bit x indicates the NZP/ZP state for the CSI-RS port x.
  • the network entity may configure a set of NZP or ZP CSI-RS port index (es) .
  • the network entity may configure the same NZP/ZP state for the CSI-RS ports in a CSI-RS port group.
  • the CSI-RS port group may be predefined or configured by the network entity or reported by the UE. For example, the UE may only support the SD prediction based on the measured port ⁇ 3000, 3002, 3004, ... ⁇ , then the ports ⁇ 3000, 3002, 3004 ⁇ should be within the same port group.
  • Different CSI-RS port groups may be orthogonal or non-orthogonal.
  • the UE may determine the REs for the NZP CSI-RS ports are not available for the resource mapping for at least one of the following downlink channels/signals: PDSCH, demodulation reference signal (DMRS) for PDSCH, phase tracking-reference signal (PT-RS) , PDCCH, DMRS for PDCCH.
  • PDSCH demodulation reference signal
  • DMRS demodulation reference signal
  • PT-RS phase tracking-reference signal
  • the UE may determine the REs for both the NZP and ZP CSI-RS ports are not available for the resource mapping for at least one of the following downlink channels/signals: PDSCH, DMRS for PDSCH, PT-RS, PDCCH, DMRS for PDCCH.
  • the network entity may configure whether the REs for NZP CSI-Rs ports are available or not available for the resource mapping for at least one of the following downlink channels/signals: PDSCH, DMRS for PDSCH, PT-RS, PDCCH, DMRS for PDCCH.
  • Fig. 7B illustrates an example 710 for the CSI-RS port to codebook port association, in accordance with aspects of this disclosure.
  • the network entity may provide a CSI-RS port and codebook port association indicator, which specifies how antenna ports for a codebook is mapped (and not mapped) to a CSI-RS port.
  • the bit x indicates whether the port x for the configured codebook is mapped to a CSI-RS port or not.
  • the mapping order between the CSI-RS port (s) and the measured port (s) from the configured codebook may be pre-defined or configured by the network entity.
  • the first mapped port from the codebook may be associated with the first CSI-RS port and then the next mapped port.
  • the order of the port for a codebook may be based on the horizontal port index, the vertical port index, and the polarization.
  • the network entity may configure at least one of the following for a CSI-RS resource: CSI-RS port to codebook port association or a number of CSI-RS ports.
  • the network entity may configure the associated port in the codebook for each CSI-RS port by CSI-RS port to codebook port association.
  • the network entity may configure a number of CSI-RS ports, and the network entity and UE determine the associated port in the codebook for each CSI-RS port based on a pre-defined rule.
  • Fig. 8A illustrates an example 800 for calculation of a rank indicator (RI) , precoder matrix indicator (PMI) , and/or channel quality indicator (CQI) based on the predicted channel for all the CSI-RS ports, in accordance with aspects of this disclosure.
  • a UE may measure 820 channel or CSI from a subset of ports.
  • the UE performs 822 channel prediction and predicts 824 channel from all the ports (e.g., SD, FD, and/or TD prediction) , including the measured subset of ports.
  • the UE performs 826 RI/PMI/CQI calculation, and transmits 828 the calculated RI/PMI/CQI to the network entity.
  • the network entity may configure the UE to report at least one of the RI/PMI/CQI for each subband, and may configure the UE to report at least one of the RI/PMI/CQI for wideband. In one example, the network entity may configure the UE to report wideband RI/PMI/CQI and subband PMI/CQI. In another example, the network entity may configure the UE to report wideband RI/PMI and subband PMI. In another example, the network entity may configure the UE to report wideband RI/CQI and subband CQI.
  • the UE assumes or determines the transmission signal of PDSCH antenna ports, e.g., antenna ports [1000, 1001, ... 1000+v-i] for v layers would result in signals equivalent to corresponding symbols transmitted on the predicted CSI-RS antenna ports, e.g., antenna ports [3000, 3001, ... 3000+P-1 ] , where P indicates the number of CSI-RS antenna ports, as given by
  • x (i) indicates the PDSCH symbols i from the layer mapping
  • W (i) indicates the precoder corresponding to the reported PMI.
  • a UE may measure 830 channel or CSI from a subset of ports.
  • the UE performs 832 channel prediction and predicts 834 channel from all the ports (e.g., SD, FD, and/or TD prediction) , excluding the measured subset of ports.
  • the UE performs 836 RI/PMI/CQI calculation, and transmits 838 the calculated RI/PMI/CQI to the network entity.
  • the UE assumes or determines the transmission signal of PDSCH antenna ports, e.g., antenna ports [1000, 1001, ... 1000+v-i] for v layers would result in signals equivalent to corresponding symbols transmitted on the predicted CSI-RS antenna ports and the received CSI-RS antenna ports, e.g., antenna ports [3000, 3001, ... 3000+P-1 ] , where P indicates the number of CSI-RS antenna ports, as given by
  • x (i) indicates the PDSCH symbols i from the layer mapping
  • W (i) indicates the precoder corresponding to the reported PMI.
  • the network entity may configure whether the UE should calculate the subband/wideband RI/PMI/CQI based on the predicted channel for all the ports configured by the codebook for CSI calculation as shown in Figure 8A, or based on the predicted channel for all the ports configured by the codebook for CSI calculation excluding the ports mapped to the (NZP) CSI-RS ports and measured channel for the (NZP) CSI-RS ports as shown in Figure 8B.
  • the network entity may transmit the configuration by RRC signaling, e.g., an RRC parameter associated with the CSI report configuration, MAC CE, e.g., the MAC CE activating the semi-persistent CSI report, or DCI, e.g., CSI request field in a DCI corresponding to an aperiodic CSI triggering state.
  • RRC signaling e.g., an RRC parameter associated with the CSI report configuration
  • MAC CE e.g., the MAC CE activating the semi-persistent CSI report
  • DCI e.g., CSI request field in a DCI corresponding to an aperiodic CSI triggering state.
  • the network entity may configure the UE to report both types of RI/PMI/CQI based on the RI/PMI/CQI calculation schemes above.
  • the UE may report whether the UE calculates the subband/wideband RI/PMI/CQI based on the predicted channel for all the ports configured by the codebook for CSI calculation as shown in Figure 8A, or based on the predicted channel for all the ports configured by the codebook for CSI calculation excluding the ports mapped to the (NZP) CSI-RS ports and measured channel for the (NZP) CSI-RS ports, as shown in Figure 8B.
  • the UE may transmit the report by an RRC message, e.g., UE capability report or UE assistance information report, or MAC CE, or UCI, e.g., an indication in the CSI report.
  • RRC message e.g., UE capability report or UE assistance information report, or MAC CE
  • UCI e.g., an indication in the CSI report.
  • the network entity may configure the UE to report the CSI based on the received CSI-RS on the (NZP) CSI-RS ports.
  • the network entity may configure the UE to report at least one of the RI/PMI/CQI for each subband, and may configure the UE to report at least one of the RI/PMI/CQI for wideband.
  • the network entity may configure the UE to report wideband RI/PMI/CQI and subband PMI/CQI.
  • the network entity may configure the UE to report wideband RI/PMI and subband PMI.
  • the network entity may configure the UE to report wideband RI/CQI and subband CQI.
  • the UE may calculate the subband/wideband RI/PMI/CQI based on the measured channel from the received CSI-RS. Then based on the received CSI corresponding to a subset of antenna ports, the network entity may predict the CSI for more ports.
  • the network entity may configure the UE to report SINR, e.g., layer 1 SINR, and/or interference strength, e.g., RSSI, for each configured subband in addition to the CSI above.
  • SINR e.g., layer 1 SINR
  • interference strength e.g., RSSI
  • the network entity may configure the UE to report wideband SINR and/or interference strength additionally.
  • the network entity may further configure a CSI-RS resource or CSI CSI-IM resource for interference measurement. Then after receiving the CSI and interference strength corresponding to a subset of antenna ports, the network entity may predict the precoder and MCS when transmitting the PDSCH from more ports.
  • the UE may report the PMI based on the channel eigenvector according to the (NZP) CSI-RS ports.
  • the UE may report all the coefficients or a subset of coefficients, e.g., strongest X, for the channel eigenvector.
  • the UE may report the amplitude and phase.
  • the UE may report absolute value for the amplitude/phase for each reported coefficient.
  • the UE may report absolute value for the amplitude of the coefficient with strongest amplitude and report differential amplitude for other coefficient with the strongest amplitude as the reference.
  • the value of X may be pre-defined or configured by the network entity or reported by the UE.
  • the UE may further report an indicator indicating the location of the X coefficients within the channel eigenvector matrix.
  • the UE may further report an indicator indicating the location for the strongest coefficient.
  • the UE can identify the channel eigenvector for a subband b at CMR instance t based on the singular value decomposition (SVD) of the channel covariance matrix as follows:
  • U b is the left singular vector
  • S b is the singular values
  • V b is the right singular vector (channel eigenvector) .
  • the UE may report the PMI based on a codebook configured by the network entity and the ports for the codebook mapped to the CSI-RS ports.
  • Fig. 9 illustrates an example 900 for polarization specific configuration, in accordance with aspects of this disclosure.
  • the network entity may configure separate number of horizontal ports (N1) and vertical ports (N2) for each polarization, and the CSI-RS port and codebook mapping.
  • the measured ports for different polarizations are different, and the network entity may configure (N1, N2) for the first polarization as (8, 4) and (N1, N2) for the second polarization as (4, 2) .
  • the network entity may configure common or separate values for number of panels for different polarizations.
  • the network entity may configure common or separate oversampling factors for the beam selection, e.g., oversampling factor for horizontal beams (O 1) and oversampling factor for vertical beams (O2) .
  • the network entity may configure multiple codebooks, where different codebooks correspond to different polarizations.
  • the network entity may configure common value for a subset of parameters for the codebooks, e.g., codebook type, RI restriction, and so on.
  • the UE may report separate PMIs indicating the precoders for different polarizations separately. In some other implementations, the UE may report one PMI indicating the precoder for all the polarization.
  • the network entity and UE may determine the beam corresponding to one PMI based on the ports from a codebook mapped to the CSI-RS ports.
  • the network entity can configure two schemes for the CSI feedback.
  • the UE reports the precoder indicating the antenna co-phasing between two polarizations, where precoder for a layer can be as follows:
  • the network entity and UE may determine q l, m and q′ l’, m’ by removing the elements of that are not mapped to any (NZP) CSI-RS port.
  • NZP any CSI-RS port.
  • the network entity and UE may determine q l, m and q′ l’, m’ by removing the elements of that are not mapped to any (NZP) CSI-RS port.
  • NZP N 1 N 2 elements
  • the ports for the codebook corresponding to the first and second polarization are mapped to X 1 and X 2 (NZP) CSI-RS ports respectively, then after removal of the unused ports, q l, m and q′ l’, m’ contain X 1 and X 2 elements respectively.
  • the UE reports the PMI information indicating the value of for at least one of the parameters of l, m, l’, m’ and n for each layer.
  • the precoder should be normalized. Thus, for N L layers, the precoder for each layer should be multiplied by or
  • the UE reports N L wideband beam index for N L layers based on the beams in W1, where each beam index corresponding to one layer.
  • a beam index indicates the value of (l, m, l ’, m ’) .
  • the UE calculates the co-phasing between two polarizations, and compressed the polarizations from N3 subbands into Mv coefficients based on Mv frequency domain (FD) basis. Then for one layer the precoder for all the subbands can be generated as follows:
  • W FD is a Mv by N3 matrix indicating Mv FD basis from a set of FD basis, e.g., DFT basis as defined in 38.214 section 5.2.2.2.5, based on number of subbands.
  • the UE can apply a time offset to keep the first coefficient always from the first FD basis.
  • the UE reports a subset of or all the coefficients from the 2 by Mv matrix and Mv-1 FD basis.
  • the reported precoder and the precoder used for CQI calculation should be normalized.
  • the precoder for each layer should be multiplied by or
  • Type2/eType2 codebook the UE reports the precoder based on similar approach as scheme B for Type 1 codebook, where the UE can report more than one beams for each layer.
  • the UE can report L beams for each layer in W 1
  • the enhanced beam combining matrix is a 2L by Mv matrix.
  • the UE can report one or multiple non-zero-power coefficients for the enhanced beam combining matrix and the UE may report amplitude and phase for each coefficient.
  • Type2 codebook may indicate Type2 codebook or eType2 codebook.
  • the network entity may configure the UE to report the CSI for a set of subbands and a set of ports based on one or multiple transmission occasions of the CSI-RS from a subset of RBs/subbands and from a subset of ports.
  • the network entity may provide the configurations based on the parameters for FD prediction and SD prediction accordingly. Then, the UE may report the CSI based on FD prediction and SD prediction.
  • the network entity may configure the UE to report the CSI for future slots. Then the UE may predict the CSI for future slots based on the received transmission occasion (s) of the CSI-RS.
  • the network entity may configure the UE to report the CSI for future slots for a set of subbands and/or a set of ports based on one or multiple transmission occasions of the CSI-RS from a subset of RBs/subbands and/or from a subset of ports.
  • the network entity may provide at least one of the following configurations for TD prediction.
  • the network entity may provide measurement window configuration, which configures the transmission occasions of the CSI-RS resource (s) for a UE to measure.
  • the network entity may provide prediction window configuration, which configures the predicted slot (s) .
  • the network entity may provide codebook for predicted CSI quantization, which configures how to quantize the predicted PMIs, e.g., eType2/FeType2 codebook for predicted PMI (e.g., as defined in the third generation partnership project (3GPP) technical specification (TS) 38.214 section 5.2.2.2) , or a codebook associated with at least one of the following: association ID, model ID and/or dataset ID for ML based CSI compression.
  • 3GPP third generation partnership project
  • the UE may perform the TD/FD/SD prediction based on a single model. Then the network entity may configure at least one of the parameters: an association ID, model ID or dataset ID, for the UE to identify the model for the joint TD/FD/SD prediction. In some other implementations, the UE may perform the TD/FD/SD prediction based on separate models. Then the network entity may configure at least one the parameters for TD/FD/SD prediction separately: association ID, model ID or dataset ID for the UE to identify the models for TD/FD/SD prediction separately. The UE may report whether the UE supports TD/FD/SD prediction based on a joint model or separate models.
  • the network entity may configure the UE to report the CSI based on ML based CSI compression.
  • the UE may perform the TD/FD/SD prediction and CSI compression based on a single model.
  • the network entity may configure at least one of the parameters: an association ID, model ID or dataset ID, for the UE to identify the model for the joint TD/FD/SD prediction and CSI compression.
  • the UE may perform the TD/FD/SD prediction and CSI compression based on separate models.
  • the network entity may configure at least one of the parameters for TD/FD/SD prediction and CSI compression separately: an association ID, model ID or dataset ID, for the UE to identify the models for the TD/FD/SD prediction and CSI compression separately.
  • the UE may report whether the UE supports TD/FD/SD prediction and CSI compression based on a joint model or separate models.
  • the network entity may perform SD/FD/TD prediction.
  • the network entity may configure the UE to report the CSI for a subset of subbands and a subset of ports based on one or multiple transmission occasions of the CSI-RS from the subset of subbands and the subset of ports.
  • the network entity may predict the CSI for other subbands and more ports.
  • the UE may calculate and report the CSI based on the embodiments for CSI report for FD prediction and SD prediction.
  • the network entity may configure a measurement window for the UE to identify the transmission occasions for the CSI-RS for CSI report, and the network entity configures the UE to report the CSI based on multiple transmission occasions.
  • the network entity may configure a codebook for the CSI compression for the multiple CSIs, e.g., eType2/FeType2 codebook for predicted PMI as defined in 3GPP TS 38.214 section 5.2.2.2, or a codebook associated with at least one of the following: association ID, model ID and/or dataset ID for ML based CSI compression. Then the network entity can predict the CSI for future slots based on the received CSIs.
  • the network entity may perform one or multiple of SD/FD/TD prediction, and the UE may perform the one or multiple of SD/FD/TD prediction excluding the prediction that the network entity performs.
  • the network entity may perform the FD prediction, and the UE may perform SD/TD prediction.
  • the network entity may perform SD prediction, and the UE may perform FD/TD prediction.
  • the network entity may perform FD/TD prediction, and the UE may perform SD prediction.
  • Other combinations are considered as different examples.
  • the network entity may configure the parameters for the UE-side SD/FD/TD prediction based on the embodiments above and configure the UE to calculate and report the CSI to facilitate the network entity-side SD/FD/TD prediction based on the embodiments above.
  • Fig. 10 illustrates an example 1000 for activation/deactivation for CSI-RS transmission occasions, in accordance with aspects of this disclosure.
  • the network entity transmits some CSI-RS 1010 and refrains from transmitting some other CSI-RS 1020.
  • the UE measures the CSI-RS in the CSI measurement window 1012 (or CSI-RS activation window) and performs CSI prediction in the CSI prediction window 1022 (or the CSI-RS deactivation window) .
  • the network entity may activate/deactivate some transmission occasions for periodic or semi-persistent CSI-RS resource or CSI-RS resource set. For example, for some of the transmission occasions within a CSI prediction window, the network entity may deactivate such transmission occasions.
  • the network entity may transmit such configuration by RRC signaling, MAC CE or DCI.
  • the network entity may configure at least one of the following for the activation/deactivation window, including the duration for the activation/deactivation window, the periodicity for the activation/deactivation window, or the starting point for the activation/deactivation window within each period.
  • the UE may determine the periodic or semi-persistent CSI-RS are transmitted on the transmission occasions in the activation window.
  • the UE may determine the periodic or semi-persistent CSI-RS are not transmitted on the transmission occasions in the deactivation window.
  • the UE may determine the REs for the periodic or semi-persistent CSI-RS transmission occasions in the deactivation window are available for the resource mapping for at least one of the following: PDSCH, DMRS for PDSCH, PT-RS, PDCCH, DMRS for PDCCH.
  • Fig. 11 illustrates a flowchart of a method 1100 of wireless communication at a UE.
  • the method may be performed by the UE 102, the UE apparatus 1302, etc., which may include the memory 1326′, 1306′, 1316, and which may correspond to the entire UE 102 or the entire UE apparatus 1302, or a component (e.g., the CSI configuration component 120) of the UE 102 or the UE apparatus 1302, such as the wireless baseband processor 1326 and/or the application processor 1306.
  • the UE apparatus 1302 may include the memory 1326′, 1306′, 1316, and which may correspond to the entire UE 102 or the entire UE apparatus 1302, or a component (e.g., the CSI configuration component 120) of the UE 102 or the UE apparatus 1302, such as the wireless baseband processor 1326 and/or the application processor 1306.
  • the method 1100 starts where the UE optionally transmits 1102, to a network entity, UE capability information for supporting CSI report based on at least one of a spatial domain (SD) prediction, an frequency domain (FD) prediction, or a time domain (TD) prediction, wherein the UE capability information includes at least one of: channel delay information for the FD prediction; or supported CSI-RS port information for the SD prediction (similar to operations 302 and 402 of Figs. 3 and 4) .
  • SD spatial domain
  • FD frequency domain
  • TD time domain
  • the UE receives 1104, from the network entity, a channel state information (CSI) report configuration configuring: a CSI reference signal (CSI-RS) resource set for channel measurement, one or more subbands for reporting CSI, and one or more antenna ports for reporting the CSI (similar to operations 304 and 404 of Figs. 3 and 4) .
  • CSI channel state information
  • the UE receives 1110, from the network entity, a CSI-RS according to the CSI-RS resource set (similar to operations 310 and 410 of Figs. 3 and 4) .
  • the UE transmits 1112, to the network entity, a CSI report based on prediction or measurement of the CSI according to at least a subset of the one or more subbands or to at least a subset of the one or more antenna ports (similar to operations 312 and 412 of Figs. 3 and 4) .
  • the CSI report configuration further includes at least one of: a report quantity; a codebook for the CSI report, one or more interference measurement resources, a configuration for spatial domain (SD) prediction associated with the one or more antenna ports; a configuration for frequency domain (FD) prediction; or a configuration for time domain (TD) prediction, wherein the SD prediction, the FD prediction, and the TD prediction use machine-learning models for predicting non-measured values based on measured values.
  • SD spatial domain
  • FD frequency domain
  • TD time domain
  • the method 1100 further includes transmitting, to the network entity, UE capability information for supporting CSI report based on at least one of an SD prediction, an FD prediction, or a TD prediction, wherein the UE capability information includes at least one off channel delay information for the FD prediction; or supported CSI-RS port information for the SD prediction.
  • an CSI-RS density for each subband is determined based on the channel delay information; and wherein the channel delay information includes at least one of: a channel delay profile indicating a delay for a number of paths; a channel power delay profile indicating delays and amplitudes of signal samples or paths; a channel impulse response; a channel delay spread; a maximum channel delay; an average channel delay; or a subband size recommended by the UE.
  • the method 1100 further includes transmitting, to the network entity, a supported CSI-RS pattern information associated with the FD prediction, wherein the supported CSI-RS pattern information includes at least one of: an FD density for CSI-RS from each antenna port for a set of subbands; one or more subband indexes associated with the set of subbands; a subband size; an FD density for CSI-RS from each antenna port for an entire bandwidth; a dataset identifier (ID) associated with a prediction scenario, the dataset ID corresponding to at least one of: a type of model input or a type of model output; a machine learning (ML) model ID associated with the prediction scenario, the model ID corresponding to at least one of: a type of model input or a type of model output; or an ID associated with antenna structure of the network entity.
  • ID dataset identifier
  • ML machine learning
  • the CSI report configuration further includes: one or multiple subband index (es) for the UE to perform CSI measurement and report CSI based on a reference bandwidth; and an indication to include, in the CSI report, at least one of: a rank indicator (RI) for each subband, a precoder matrix indicator (PMI) for each subband, a channel quality indicator (CQI) for each subband, an RI for a wideband, a PMI for a wideband, or a CQI for a wideband.
  • RI rank indicator
  • PMI precoder matrix indicator
  • CQI channel quality indicator
  • the supported CSI-RS port information includes at least one of: a number of horizontal ports, a number of vertical ports, a number of antenna panels, one or more supported sets of CSI-RS port indexes, one or more measured CSI-RS ports for each CSI-RS port structure, an association ID, a dataset ID, or a machine learning (ML) model ID.
  • ML machine learning
  • the CSI report configuration further includes at least one of: a non-zero-power (NZP) or zero-power (ZP) state for each CSI-RS port or port group; a number of CSI-RS ports; or a port association between a CSI-RS port and a codebook.
  • NZP non-zero-power
  • ZP zero-power
  • the method 1100 further includes transmitting, in the CSI report from the UE, a PMI based on: a channel eigenvector according to NZP CSI-RS ports indicated in the CSI report configuration; or a codebook indicated in the CSI report configuration, wherein the codebook is polarization-specific.
  • the UE determines a beam corresponding to a PMI based on antenna ports from a codebook mapped to the number of CSI-RS ports.
  • the CSI report configuration further includes time domain (TD) prediction information for the UE to calculate CSI for future slots based on received transmission occasions of CSI-RS resources associated with the CSI-RS resource set.
  • the TD prediction information includes at least one of: a measurement window configuration indicating the transmission occasions of the CSI-RS resources; one or more predicted slots; a codebook for predicted CSI quantization; a duration, a periodicity, and a starting point within each period of an activation window; or a duration, a periodicity, and a starting point within each period of a deactivation window.
  • the method 1100 further includes performing, by the UE, the FD prediction, the SD prediction, or the TD prediction using a single machine learning (ML) model, wherein the single machine learning model provides for CSI compression.
  • the UE may perform, with the network entity, joint FD prediction, SD prediction, or the TD prediction using respective machine learning (ML) models of the UE and of the network entity.
  • Fig. 12 is a flowchart of a method 1200 of wireless communication at a network entity.
  • the method 1200 is complementary to the method 1100 of Fig. 11.
  • the method 1200 may be performed by one or more network entities 104, which may correspond to a base station or a unit of the base station, such as the RU 106, the DU 108, the CU 110, an RU processor 1406, a DU processor 1426, a CU processor 1446, etc.
  • the one or more network entities 104 may include memory 1406’/1426’/1446’, which may correspond to an entirety of the one or more network entities 104, or a component of the one or more network entities 104, such as the RU processor 1406, the DU processor 1426, or the CU processor 1446.
  • the network entity optionally receives 1202, from a UE, UE capability information for supporting CSI report based on at least one of a spatial domain (SD) prediction, an frequency domain (FD) prediction, or a time domain (TD) prediction, wherein the UE capability information includes at least one of: channel delay information for the FD prediction; or supported CSI-RS port information for the SD prediction (similar to operations 302 and 502 of Figs. 3 and 5) .
  • SD spatial domain
  • FD frequency domain
  • TD time domain
  • the network entity transmits 1204, to the UE, a channel state information (CSI) report configuration configuring: a CSI reference signal (CSI-RS) resource set for channel measurement, one or more subbands for reporting CSI, and one or more antenna ports for reporting the CSI (similar to operations 304 and 504 of Figs. 3 and 5) .
  • CSI channel state information
  • the network entity transmits 1210, to the UE, a CSI-RS according to the CSI-RS resource set (similar to operations 310 and 510 of Figs. 3 and 5) .
  • the network entity receives 1212, from the UE, a CSI report based on prediction or measurement of the CSI according to at least a subset of the one or more subbands or to at least a subset of the one or more antenna ports (similar to operations 312 and 512 of Figs. 3 and 5) .
  • the CSI report configuration further includes at least one of: a report quantity; a codebook for the CSI report, one or more interference measurement resources, a configuration for SD prediction associated with the one or more antenna ports; a configuration for FD prediction; or a configuration for TD prediction, wherein the SD prediction, the FD prediction, and the TD prediction use machine-learning models for predicting non-measured values based on measured values.
  • an CSI-RS density for each subband is determined based on channel delay information, and wherein the channel delay information includes at least one of: a channel delay profile indicating a delay for a number of paths; a channel power delay profile indicating delays and amplitudes of signal samples or paths; a channel impulse response; a channel delay spread; a maximum channel delay; an average channel delay; or a subband size recommended by the UE.
  • the channel delay information includes at least one of: a channel delay profile indicating a delay for a number of paths; a channel power delay profile indicating delays and amplitudes of signal samples or paths; a channel impulse response; a channel delay spread; a maximum channel delay; an average channel delay; or a subband size recommended by the UE.
  • the network entity transmits the CSI report configuration based on: receiving, from the UE (102) , a supported CSI-RS pattern information associated with the FD prediction, wherein the supported CSI-RS pattern information includes at least one of: an FD density for CSI-RS from each antenna port for a set of subbands; one or more subband indexes associated with the set of subbands; a subband size; an FD density for CSI-RS from each antenna port for an entire bandwidth; a dataset ID associated with a prediction scenario, the dataset ID corresponding to at least one of: a type of model input or a type of model output; a machine learning (ML) model ID associated with the prediction scenario, the model ID corresponding to at least one of: a type of model input or a type of model output; or an ID associated with antenna structure of the network entity.
  • the supported CSI-RS pattern information includes at least one of: an FD density for CSI-RS from each antenna port for a set of subbands; one or more subband indexes associated
  • the network entity receives, from the UE, UE capability information.
  • the UE capability information includes supported CSI-RS port information, which includes at least one of: a number of horizontal ports, a number of vertical ports, a number of antenna panels, one or more supported sets of CSI-RS port indexes, one or more measured CSI-RS ports for each CSI-RS port structure, an association ID, a dataset ID, or a machine learning (ML) model ID.
  • ML machine learning
  • a UE apparatus 1302 may perform the method 1100.
  • the one or more network entities (or BS) 104 may perform the method 1200.
  • Fig. 13 is a diagram 1300 illustrating an example of a hardware implementation for a UE apparatus 1302.
  • the UE apparatus 1302 may be the UE 102, a component of the UE 102, or may implement UE functionality.
  • the UE apparatus 1302 may include an application processor 1306, which may have on-chip memory 1306’.
  • the application processor 1306 may be coupled to a secure digital card 1308 and/or a display 1310.
  • the application processor 1306 may also be coupled to a sensor (s) module 1312, a power supply 1314, an additional module of memory 1316, a camera 1318, and/or other related components.
  • the sensor (s) module 1312 may control a barometric pressure sensor/altimeter, a motion sensor such as an inertial management unit (IMU) , a gyroscope, accelerometer (s) , a light detection and ranging (LIDAR) device, a radio-assisted detection and ranging (RADAR) device, a sound navigation and ranging (SONAR) device, a magnetometer, an audio device, and/or other technologies used for positioning.
  • a motion sensor such as an inertial management unit (IMU) , a gyroscope, accelerometer (s) , a light detection and ranging (LIDAR) device, a radio-assisted detection and ranging (RADAR) device, a sound navigation and ranging (SONAR) device, a magnetometer, an audio device, and/or other technologies used for positioning.
  • IMU inertial management unit
  • a gyroscope such as an inertial management unit (IMU) , a gy
  • the UE apparatus 1302 may further include a wireless baseband processor 1326, which may be referred to as a modem.
  • the wireless baseband processor 1326 may have on-chip memory 1326′.
  • the wireless baseband processor 1326 may also be coupled to the sensor (s) module 1312, the power supply 1314, the additional module of memory 1316, the camera 1318, and/or other related components.
  • the wireless baseband processor 1326 may be additionally coupled to one or more subscriber identity module (SIM) card (s) 1320 and/or one or more transceivers 1330 (e.g., wireless RF transceivers) .
  • SIM subscriber identity module
  • the UE apparatus 1302 may include a Bluetooth module 1332, a WLAN module 1334, an SPS module 1336 (e.g., GNSS module) , and/or a cellular module 1338.
  • the Bluetooth module 1332, the WLAN module 1334, the SPS module 1336, and the cellular module 1338 may each include an on-chip transceiver (TRX) , or in some cases, just a transmitter (TX) or just a receiver (RX) .
  • TRX on-chip transceiver
  • the Bluetooth module 1332, the WLAN module 1334, the SPS module 1336, and the cellular module 1338 may each include dedicated antennas and/or utilize antennas 1340 for communication with one or more other nodes.
  • the UE apparatus 1302 may communicate through the transceiver (s) 1330 via the antennas 1340 with another UE 102 (e.g., sidelink communication) and/or with a network entity 104 (e.g., uplink/downlink communication) , where the network entity 104 may correspond to a base station or a unit of the base station, such as the RU 106, the DU 108, or the CU 110.
  • another UE 102 e.g., sidelink communication
  • a network entity 104 e.g., uplink/downlink communication
  • the wireless baseband processor 1326 and the application processor 1306 may each include a computer-readable medium /memory 1326′, 1306′, respectively.
  • the additional module of memory 1316 may also be considered a computer-readable medium /memory.
  • Each computer-readable medium /memory 1326′, 1306′, 1316 may be non-transitory.
  • the wireless baseband processor 1326 and the application processor 1306 may each be responsible for general processing, including execution of software stored on the computer-readable medium /memory 1326′, 1306′, 1316.
  • the software when executed by the wireless baseband processor 1326 /application processor 1306, causes the wireless baseband processor 1326 /application processor 1306 to perform the various functions described herein.
  • the computer-readable medium / memory may also be used for storing data that is manipulated by the wireless baseband processor 1326 /application processor 1306 when executing the software.
  • the wireless baseband processor 1326 /application processor 1306 may be a component of the UE 102.
  • the UE apparatus 1302 may be a processor chip (e.g., modem and/or application) and include just the wireless baseband processor 1326 and/or the application processor 1306. In other examples, the UE apparatus 1302 may be the entire UE 102 and include the additional modules of the apparatus 1302.
  • the CSI report component 140 is configured to receive, from the network entity 104, a CSI report configuration configuring: a CSI-RS resource set for channel measurement, one or more subbands for reporting CSI, and one or more antenna ports for reporting the CSI.
  • the CSI report component 140 is further configured to receive, from the network entity 104, a CSI-RS based on the CSI-RS resource set, and to transmit, to the network entity 104, a CSI-RS according to the CSI-RS resource set.
  • the CSI report component 140 is configured to transmit, to the network entity 104, a CSI report based on prediction or measurement of the CSI according to at least a subset of the one or more subbands or to at least a subset of the one or more antenna ports.
  • the CSI report component 140 may be within the application processor 1306 (e.g., at 140a) , the wireless baseband processor 1326 (e.g., at 140b) , or both the application processor 1306 and the wireless baseband processor 1326.
  • the CSI report component 140a-140b may be one or more hardware components specifically configured to carry out the stated processes/algorithm, implemented by one or more processors configured to perform the stated processes/algorithm, stored within a computer-readable medium for implementation by the one or more processors, or a combination thereof.
  • Fig. 14 is a diagram 1400 illustrating an example of a hardware implementation for one or more network entities 104.
  • the one or more network entities 104 may be a base station, a component of a base station, or may implement base station functionality.
  • the one or more network entities 104 may include, or may correspond to, at least one of the RU 106, the DU 108, or the CU 110.
  • the CU 110 may include a CU processor 1446, which may have on-chip memory 1446′.
  • the CU 110 may further include an additional module of memory 1456 and/or a communications interface 1448, both of which may be coupled to the CU processor 1446.
  • the CU 110 may communicate with the DU 108 through a midhaul link 162, such as an F1 interface between the communications interface 1448 of the CU 110 and a communications interface 1428 of the DU 108.
  • the DU 108 may include a DU processor 1426, which may have on-chip memory 1426′. In some aspects, the DU 108 may further include an additional module of memory 1436 and/or the communications interface 1428, both of which may be coupled to the DU processor 1426.
  • the DU 108 may communicate with the RU 106 through a fronthaul link 160 between the communications interface 1428 of the DU 108 and a communications interface 1408 of the RU 106.
  • the RU 106 may include an RU processor 1406, which may have on-chip memory 1406′. In some aspects, the RU 106 may further include an additional module of memory 1416, the communications interface 1408, and one or more transceivers 1430, all of which may be coupled to the RU processor 1406. The RU 106 may further include antennas 1440, which may be coupled to the one or more transceivers 1430, such that the RU 106 may communicate through the one or more transceivers 1430 via the antennas 1440 with the UE 102.
  • the on-chip memory 1406′, 1426′, 1446′and the additional modules of memory 1416, 1436, 1456 may each be considered a computer-readable medium /memory. Each computer-readable medium /memory may be non-transitory. Each of the processors 1406, 1426, 1446 is responsible for general processing, including execution of software stored on the computer-readable medium /memory. The software, when executed by the corresponding processor (s) 1406, 1426, 1446 causes the processor (s) 1406, 1426, 1446 to perform the various functions described herein.
  • the computer-readable medium /memory may also be used for storing data that is manipulated by the processor (s) 1406, 1426, 1446 when executing the software.
  • the CSI configuration component 150 may sit at any of the one or more network entities 104, such as at the CU 110; both the CU 110 and the DU 108; each of the CU 110, the DU 108, and the RU 106; the DU 108; both the DU 108 and the RU 106; or the RU 106.
  • the CSI configuration component 150 may perform various operations and signaling (such as the operations in Figs. 3 and 5) according to the examples provided herein and be within one or more processors of the one or more network entities 104, such as the RU processor 1406 (e.g., at 150a) , the DU processor 1426 (e.g., at 150b) , and/or the CU processor 1446 (e.g., at 150c) . As discussed in Fig. 1 and implemented with respect to Figs.
  • the CSI configuration component 150 is configured to transmit, to a UE 102, a CSI report configuration configuring: a CSI-RS resource set for channel measurement, one or more subbands for reporting CSI, and one or more antenna ports for reporting the CSI.
  • the CSI configuration component 150 is configured to transmit, to the UE 102, a CSI-RS according to the CSI-RS resource set.
  • the CSI configuration component 150 is configured to receive, from the UE 102, a CSI report based on prediction or measurement of the CSI according to at least a subset of the one or more subbands or to at least a subset of the one or more antenna ports.
  • the CSI configuration component 150a-150c may be one or more hardware components specifically configured to carry out the stated processes/algorithm, implemented by one or more processors 1406, 1426, 1446 configured to perform the stated processes/algorithm, stored within a computer-readable medium for implementation by the one or more processors 1406, 1426, 1446, or a combination thereof.
  • processors include microprocessors, microcontrollers, graphics processing units (GPUs) , central processing units, application processors, digital signal processors (DSPs) , reduced instruction set computing (RISC) processors, systems-on-chip (SoC) , baseband processors, field programmable gate arrays (FPGAs) , programmable logic devices (PLDs) , state machines, gated logic, discrete hardware circuits, and other similar hardware configured to perform the various functionality described throughout this disclosure.
  • GPUs graphics processing units
  • DSPs digital signal processors
  • RISC reduced instruction set computing
  • SoC systems-on-chip
  • FPGAs field programmable gate arrays
  • PLDs programmable logic devices
  • One or more processors in the processing system may execute software, which may be referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.
  • Software may be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software components, applications, software applications, software packages, routines, subroutines, objects, executables, threads of execution, procedures, functions, or any combination thereof.
  • Computer-readable media includes computer storage media and may include a random-access memory (RAM) , a read-only memory (ROM) , an electrically erasable programmable ROM (EEPROM) , optical disk storage, magnetic disk storage, other magnetic storage devices, combinations of these types of computer-readable media, or any other medium that may be used to store computer executable code in the form of instructions or data structures that may be accessed by a computer.
  • Storage media may be any available media that may be accessed by a computer.
  • aspects, implementations, and/or use cases described herein may be implemented across many differing platform types, devices, systems, shapes, sizes, and packaging arrangements.
  • the aspects, implementations, and/or use cases may come about via integrated chip implementations and other non-module-component based devices, such as end- user devices, vehicles, communication devices, computing devices, industrial equipment, retail/purchasing devices, medical devices, artificial intelligence (AI) -enabled devices, machine learning (ML) -enabled devices, etc.
  • the aspects, implementations, and/or use cases may range from chip-level or modular components to non-modular or non-chip-level implementations, and further to aggregate, distributed, or original equipment manufacturer (OEM) devices or systems incorporating one or more techniques described herein.
  • OEM original equipment manufacturer
  • Devices incorporating the aspects and features described herein may also include additional components and features for the implementation and practice of the claimed and described aspects and features.
  • transmission and reception of wireless signals necessarily includes a number of components for analog and digital purposes, such as hardware components, antennas, RF-chains, power amplifiers, modulators, buffers, processor (s) , interleavers, adders/summers, etc.
  • Techniques described herein may be practiced in a wide variety of devices, chip-level components, systems, distributed arrangements, aggregated or disaggregated components, end-user devices, etc., of varying configurations.
  • “may” refers to a permissible feature that may or may not occur
  • “might” refers to a feature that probably occurs
  • “may” refers to a capability (e.g., capable of) .
  • the phrase “For example” often carries a similar connotation to “may” and, therefore, “may” is sometimes excluded from sentences that include “for example” or other similar phrases.
  • Combinations such as “at least one of A, B, or C” or “one or more of A, B, or C” include any combination of A, B, and/or C, such as A and B, A and C, B and C, or A and B and C, and may include multiples of A, multiples of B, and/or multiples of C, or may include A only, B only, or C only.
  • Sets may be interpreted as a set of elements where the elements number one or more.
  • ordinal terms such as “first” and “second” do not necessarily imply an order in time, sequence, numerical value, etc., but are used to distinguish between different instances of a term or phrase that follows each ordinal term.
  • Reference numbers, as used in the specification and figures, are sometimes cross-referenced among drawings to denote same or similar features.
  • a feature that is exactly the same in multiple drawings may be labeled with the same reference number in the multiple drawings.
  • a feature that is similar among the multiple drawings, but not exactly the same, may be labeled with reference numbers that have different leading numbers, but have one or more of the same trailing numbers (e.g., 206, 306, 406, etc., may refer to similar features in the drawings) .
  • an “X” is used to universally denote multiple variations of a feature. For instance, “X06” may universally refer to all reference numbers that end in “06” (e.g., 206, 306, 406, etc. ) .
  • an expression of “X/Y” may include meaning of “X or Y” . It is noted that throughout this disclosure, an expression of “X/Y” may include meaning of “X and Y” . It is noted that throughout this disclosure, an expression of “X/Y” may include meaning of “X and/or Y” . It is noted that throughout this disclosure, an expression of “ (A) B” or “B (A) ” may include concept of “only B” . It is noted that throughout this disclosure, an expression of “ (A) B” or “B (A) ” may include concept of “A+B” or “B+A” .
  • any sentence, paragraph, (sub) -bullet, point, action, or claim described in each of the foregoing or the following embodiment (s) /implementations/concept (s) may be implemented independently and separately to form a specific method.
  • Dependency e.g., “based on, ” “more specifically, ” “where” or etc., in embodiment (s) /implementations/concept (s) mentioned in this disclosure is just one possible embodiment which would not restrict the specific method.
  • a user device in which the techniques of this disclosure may be implemented may be any suitable device capable of wireless communications such as a smartphone, a tablet computer, a laptop computer, a mobile gaming console, a point-of-sale (POS) terminal, a health monitoring device, a drone, a camera, a media-streaming dongle or another personal media device, a wearable device such as a smartwatch, a wireless hotspot, a femtocell, or a broadband router.
  • the user device in some cases may be embedded in an electronic system such as the head unit of a vehicle or an advanced driver assistance system (ADAS) .
  • ADAS advanced driver assistance system
  • the user device may operate as an internet-of-things (IoT) device or a mobile-internet device (MID) .
  • IoT internet-of-things
  • MID mobile-internet device
  • the user device may include one or more general-purpose processors, a computer-readable memory, a user interface, one or more network interfaces, one or more sensors, etc.
  • Modules may be software modules (e.g., code stored on non-transitory machine-readable medium) or hardware modules.
  • a hardware module is a tangible unit capable of performing certain operations and may be configured or arranged in a certain manner.
  • a hardware module may comprise dedicated circuitry or logic that is permanently configured (e.g., as a special-purpose processor, such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC) ) to perform certain operations.
  • a hardware module may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations.
  • the decision to implement a hardware module in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations.
  • the techniques When implemented in software, the techniques may be provided as part of the operating system, a library used by multiple applications, a particular software application, etc.
  • the software may be executed by one or more general-purpose processors or one or more special-purpose processors.
  • Example 1 is a method of wireless communications by a user equipment (UE) , the method comprising:
  • CSI channel state information
  • CSI-RS CSI reference signal
  • a CSI report based on prediction or measurement of the CSI according to at least a subset of the one or more subbands or to at least a subset of the one or more antenna ports.
  • Example 2 is a method of example 1, wherein the CSI report configuration further comprises at least one of:
  • SD spatial domain
  • TD prediction a configuration for time domain (TD) prediction, wherein the SD prediction, the FD prediction, and the TD prediction use machine-learning models for predicting non-measured values based on measured values.
  • Example 3 is a method of example 1 or 2, further comprising:
  • UE capability information for supporting CSI report based on at least one of an SD prediction, an FD prediction, or a TD prediction, wherein the UE capability information includes at least one of:
  • Example 4 is a method of any one of examples 1 to 3, wherein an CSI-RS density for each subband is determined based on the channel delay information; and wherein the channel delay information includes at least one of:
  • a channel power delay profile indicating delays and amplitudes of signal samples or paths
  • Example 5 is a method of any one of examples 1 to 4, further comprising:
  • the network entity transmitting, to the network entity, a supported CSI-RS pattern information associated with the FD prediction, wherein the supported CSI-RS pattern information includes at least one of:
  • a dataset identifier associated with a prediction scenario, the dataset ID corresponding to at least one of: a type of model input or a type of model output;
  • ML machine learning
  • Example 6 is a method of any one of examples 1 to 5, wherein the CSI report configuration further includes:
  • PMI precoder matrix indicator
  • CQI channel quality indicator
  • Example 7 is a method of any one of examples 1 to 3, wherein the supported CSI-RS port information comprises at least one of:
  • Example 8 is a method of example 7, wherein the configuration further includes at least one of:
  • NZP non-zero-power
  • ZP zero-power
  • Example 9 is a method of example 8, further comprising:
  • codebook indicated in the configuration, wherein the codebook is polarization-specific.
  • Example 10 is a method of example 9, further comprising:
  • Example 11 is a method of example 10, wherein the configuration further comprises time domain (TD) prediction information for the UE to calculate CSI for future slots based on received transmission occasions of CSI-RS resources associated with the CSI-RS resource set, wherein the TD prediction information includes at least one of:
  • Example 12 is a method of any one of examples 3 to 11, further comprising:
  • the UE performing, by the UE, the FD prediction, the SD prediction, or the TD prediction using a single machine learning (ML) model, wherein the single machine learning model provides for CSI compression.
  • ML machine learning
  • Example 13 is a method of examples 3 to 15, further comprising:
  • Example 14 is a method of wireless communications by a network entity, the method comprising:
  • CSI channel state information
  • CSI-RS CSI reference signal
  • a CSI report based on prediction or measurement of the CSI according to at least a subset of the one or more subbands or to at least a subset of the one or more antenna ports.
  • Example 15 is a method of example 14, wherein the CSI report configuration further comprises at least one of:
  • SD spatial domain
  • TD prediction a configuration for time domain (TD) prediction, wherein the SD prediction, the FD prediction, and the TD prediction use machine-learning models for predicting non-measured values based on measured values.
  • Example 16 is a method of example 14 or 15, further comprising:
  • UE capability information for supporting CSI report based on at least one of an SD prediction, an FD prediction, or a TD prediction, wherein the UE capability information includes at least one of:
  • Example 17 is a method of any one of examples 14 to 16, wherein an CSI-RS density for each subband is determined based on the channel delay information; and wherein the channel delay information includes at least one of:
  • a channel power delay profile indicating delays and amplitudes of signal samples or paths
  • Example 18 is a method of any one of examples 14 to 17, wherein transmitting the configuration is based on:
  • the supported CSI-RS pattern information includes at least one of:
  • a dataset identifier associated with a prediction scenario, the dataset ID corresponding to at least one of: a type of model input or a type of model output;
  • ML machine learning
  • Example 19 is a method of any one of examples 14 to 18, wherein the configuration further includes:
  • PMI precoder matrix indicator
  • CQI channel quality indicator
  • Example 20 is a method of any one of examples 14 to 16, wherein the supported CSI-RS port information comprises at least one of:
  • Example 21 is a method of example 20, wherein the configuration further includes at least one of:
  • NZP non-zero-power
  • ZP zero-power
  • Example 22 is a method of example 21, further comprising:
  • codebook indicated in the configuration, wherein the codebook is polarization-specific.
  • Example 23 is a method of example 22, further comprising:
  • Example 24 is a method of example 23, wherein the configuration further comprises time domain (TD) prediction information for the UE to calculate CSI for future slots based on received transmission occasions of CSI-RS resources, wherein the TD prediction information includes at least one of:
  • Example 25 is a method of examples 16 to 24, further comprising:
  • Example 26 is an apparatus comprising:
  • radio frequency (RF) modems one or more radio frequency (RF) modems
  • a processor coupled to the one or more RF modems
  • At least one memory storing executable instructions, the executable instructions to manipulate at least one of the processor or the one or more RF modems to perform the method of any of examples 1 to 25.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

L'invention concerne des procédés, des systèmes et des techniques permettant à un équipement utilisateur (UE) de réduire le surdébit dans des signaux de référence d'informations d'état de canal (CSI) pour une rétroaction de CSI basée sur une prédiction. Un procédé donné à titre d'exemple consiste à recevoir, en provenance d'une entité de réseau, une configuration de rapport de CSI configurant : un ensemble de ressources de signal de référence de CSI (CSI-RS) pour une mesure de canal, une ou plusieurs sous-bandes pour rapporter des CSI, et un ou plusieurs ports d'antenne pour rapporter les CSI. Le procédé consiste en outre à recevoir, en provenance de l'entité de réseau, un CSI-RS selon l'ensemble de ressources CSI-RS. Le procédé consiste à transmettre, à l'entité de réseau, un rapport de CSI sur la base d'une prédiction ou d'une mesure des CSI selon au moins un sous-ensemble de la ou des sous-bandes ou à au moins un sous-ensemble du ou des ports d'antenne.
PCT/CN2024/111036 2024-08-09 2024-08-09 Réduction du surdébit dans des signaux de référence d'informations d'état de canal (csi) pour une rétroaction de csi basée sur une prédiction Pending WO2026031168A1 (fr)

Priority Applications (1)

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024025731A1 (fr) * 2022-07-28 2024-02-01 Interdigital Patent Holdings, Inc. Procédés de prédiction de faisceau hiérarchique basés sur de multiples cri
WO2024030604A1 (fr) * 2022-08-05 2024-02-08 Interdigital Patent Holdings, Inc. Validation d'intelligence artificielle (ia)/apprentissage automatique (ml) dans une gestion de faisceau et une prédiction de faisceau hiérarchique

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024025731A1 (fr) * 2022-07-28 2024-02-01 Interdigital Patent Holdings, Inc. Procédés de prédiction de faisceau hiérarchique basés sur de multiples cri
WO2024030604A1 (fr) * 2022-08-05 2024-02-08 Interdigital Patent Holdings, Inc. Validation d'intelligence artificielle (ia)/apprentissage automatique (ml) dans une gestion de faisceau et une prédiction de faisceau hiérarchique

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