Method and apparatus for support of machine learning or artificial intelligence techniques for csi feedback in fdd mimo systems

Patent metadata

Field	Value
Publication number	US-2022338189-A1
Application number	US-202217658977-A
Country	US
Kind code	A1
Filing date	Apr 12, 2022
Priority date	Apr 16, 2021
Publication date	Oct 20, 2022
Grant date	—

Abstract

Machine learning (ML) assisted channel state information (CSI) reporting or ML assisted CSI prediction includes receiving CSI reporting configurations that include indications that enable or disable at least one of: ML-assisted CSI prediction and artificial intelligence channel feature information (AI-CFI) reporting. ML model training is performed or trained ML model parameters are received, and CSI reference signals corresponding to at least one of the CSI reporting configurations are received. If ML-assisted CSI prediction is enabled, the CSI reporting configurations further include: a timing offset for future CSI prediction, and ML configurations including indication of an ML model used for the ML-assisted CSI prediction. If AI-CFI reporting is enabled, the CSI reporting configurations further include: a configuration for a report of the AI-CFI, and ML configurations including indication of an ML model used for the ML assisted-CSI feedback determination.

First claim

What is claimed is:

1 . A method, comprising:
indicating capability of a user equipment (UE) to support one of machine learning (ML) assisted channel state information (CSI) reporting or ML assisted CSI prediction; receiving configurations, wherein the configurations include one or more indications that enable or disable at least one of: ML-assisted CSI prediction and ML-assisted CSI reporting; one of performing ML model training or receiving trained ML model parameters; and receiving CSI reference signals corresponding to at least one of the configurations, wherein if ML-assisted CSI prediction is enabled, the configurations further include:
a timing offset for future CSI prediction, and
ML configurations including indication of an ML model used for the ML-assisted CSI prediction, and
the method further comprises determining and transmitting predicted CSI as feedback,
wherein if ML-assisted CSI reporting is enabled, the configurations further include:
a configuration for a CSI report quantity, artificial intelligence channel feature information (AI-CFI), and
ML configurations including indication of an ML model used for the ML assisted-CSI reporting, and
the method further comprises:
measuring the CSI reference signals based on the configuration, and
transmitting a CSI report that includes the AI-CFI.

2 . The method of claim 1 , wherein if ML-assisted CSI reporting is configured, the AI-CFI includes at least one of:
a quantized output of an ML model that corresponds to compressed knowledge of a channel, and a quantized output of an ML model that correspond to relevant features of the channel.

3 . The method of claim 1 , wherein the configurations include information to configure the AI-CFI, wherein the information is one of:
a quantization method to be used to quantize an output of the ML model, a number of quantization bits to be used, a compression ratio from original CSI to the AI-CFI, or a total number of CSI feedback bits.

4 . The method of claim 1 , wherein the configurations include additional information used for selecting the ML model, the additional information comprising signal-to-noise (SNR) ratio ranges.

5 . The method of claim 1 , wherein the configurations configure dynamic switching, based on a trigger, between:
the ML-assisted CSI reporting and CSI reporting without ML assistance, wherein the trigger comprises one of:
for aperiodic and semi-persistent reporting on a physical uplink shared channel (PUSCH), the trigger is one of a CSI format field within a downlink control information (DCI) format 0_1 or a report configuration identifier within a list of aperiodic or semi-persistent trigger events, and
for semi-persistent reporting on a physical uplink control channel (PUCCH), the trigger a dedicated field within a medium access control-control element (MAC-CE).

6 . The method of claim 1 , wherein if the ML-assisted CSI prediction is enabled, the timing offset for future CSI prediction is received as one of: a fixed value configured via a radio resource control (RRC) message or a set of values.

7 . The method of claim 1 , wherein the configurations dynamically switch between reporting a current instant CSI to ML-based future predicted CSI reporting using a triggering mechanism selected from:
for aperiodic and semi-persistent reporting on a physical uplink shared channel (PUSCH), the trigger is one of a field within downlink control information (DCI) format 0_1 or a report configuration identifier within one of a list of aperiodic CSI report triggers or an information element for semi-persistent CSI reporting on the PUSCH, or for semi-persistent reporting on a physical uplink control channel (PUCCH), the trigger is a dedicated field within a medium access control-control element (MAC-CE).

8 . A user equipment (UE), comprising:
a transceiver configured to:
indicate capability of the UE to support one of machine learning (ML) assisted channel state information (CSI) reporting or ML assisted CSI prediction,
receive configurations, wherein the configurations include one or more indications that enable or disable at least one of: ML-assisted CSI prediction and ML-assisted CSI reporting; and
a processor configured to:
one of perform ML model training or receive trained ML model parameters, wherein the transceiver is configured to receive CSI reference signals corresponding to at least one of the configurations,
wherein if ML-assisted CSI prediction is enabled, the configurations further include:
a timing offset for future CSI prediction, and
ML configurations including indication of an ML model used for the ML-assisted CSI prediction, and
the processor is further configured to determine and transmit predicted CSI as feedback,
wherein if ML-assisted CSI reporting is enabled, the configurations further include:
a configuration for a CSI report quantity, artificial intelligence channel feature information (AI-CFI), and
ML configurations including indication of an ML model used for the ML assisted-CSI reporting, and
the processor is further configured to:
measure the CSI reference signals based on the configuration, and
transmit a CSI report that includes the AI-CFI.

9 . The UE of claim 8 , wherein if ML-assisted CSI reporting is configured, the AI-CFI includes at least one of:
a quantized output of an ML model that corresponds to compressed knowledge of a channel, and a quantized output of an ML model that corresponds to relevant features of the channel.

10 . The UE of claim 8 , wherein the configurations information to configure the AI-CFI, wherein the information is one of:
a quantization method to be used to quantize an output of the ML model, a number of quantization bits to be used, a compression ratio from original CSI to the AI-CFI, or a total number of CSI feedback bits.

11 . The UE of claim 8 , wherein the configurations include additional information used for selecting the ML model, the additional information comprising signal-to-noise (SNR) ratio ranges.

12 . The UE of claim 8 , wherein the configurations configure dynamic switching, based on a trigger, between:
the ML-assisted CSI reporting and CSI reporting without ML assistance, wherein the trigger comprises one of:
for aperiodic and semi-persistent reporting on a physical uplink shared channel (PUSCH), the trigger is one of a CSI format field within a downlink control information (DCI) format 0_1 or a report configuration identifier within a list of aperiodic or semi-persistent trigger events, and
for semi-persistent reporting on a physical uplink control channel (PUCCH), the trigger is a dedicated field within a medium access control-control element (MAC-CE).

13 . The UE of claim 8 , wherein if the ML-assisted CSI prediction is enabled, the timing offset for future CSI prediction is received as one of: a fixed value configured via a radio resource control (RRC) message or a set of values.

14 . The UE of claim 8 , wherein the configurations dynamically switch between reporting a current instant CSI to ML-based future predicted CSI reporting using a triggering mechanism selected from:
for aperiodic and semi-persistent reporting on a physical uplink shared channel (PUSCH), the trigger is one of a field within downlink control information (DCI) format 0_1 or a report configuration identifier within one of a list of aperiodic CSI report triggers or an information element for semi-persistent CSI reporting on the PUSCH, or for semi-persistent reporting on a physical uplink control channel (PUCCH), the trigger is a dedicated field within a medium access c

Assignees

• Samsung Electronics Co Ltd

Inventors

• Madadi Pranav
• Jeon Jeongho
• Cho Joonyoung
• Ye Qiaoyang

Classifications

• H04W72/51 Primary

  based on terminal or device properties · CPC title

• H04W72/542

  using measured or perceived quality · CPC title

• H04B17/3913

  Predictive models, e.g. based on neural network models · CPC title

• H04B17/24

  with feedback of measurements to the transmitter · CPC title

• H04W72/0446

  Resources in time domain, e.g. slots or frames · CPC title