Machine learning model configuration for reduced capability user equipment

Patent metadata

Field	Value
Publication number	US-2024244454-A1
Application number	US-202118561814-A
Country	US
Kind code	A1
Filing date	Jul 27, 2021
Priority date	Jul 27, 2021
Publication date	Jul 18, 2024
Grant date	—

Abstract

Certain aspects of the present disclosure provide techniques for configuring machine learning models on user equipment, including reduced capability user equipment.

First claim

1 . An apparatus for wireless communications at a user equipment, comprising at least one memory comprising computer-executable instructions; and one or more processors configured to execute the computer-executable instructions and cause the apparatus to:
receive, at a user equipment from a network, control information, wherein:
the control information indicates a first configuration for receiving a first type of machine learning model and a second configuration for receiving a second type of machine learning model,
the first type of machine learning model is configured for a first type of user equipment, and
the second type of machine learning model is configured for a second type of user equipment;
determine to apply at least one of the first configuration or the second configuration based on whether the user equipment is the first type of user equipment or the second type of user equipment; and receive a first machine learning model from the network according to at least one of the first configuration or the second configuration based on the determining.

2 . The apparatus of claim 1 , wherein the first type of machine learning model results in a lower complexity machine learning operation than the second type of machine learning model.

3 . The apparatus of claim 2 , wherein the first type of user equipment is a reduced capability user equipment and the second type of user equipment is a regular capability user equipment.

4 . The apparatus of claim 3 , wherein:
the first configuration schedules a first scheduled downlink message, and the second configuration schedules a second scheduled downlink message.

5 . The apparatus of claim 3 , wherein:
the user equipment is the first type of user equipment, determining to apply at least one of the first configuration or the second configuration based on whether the user equipment is the first type of user equipment or the second type of user equipment comprises determining to apply the first configuration, and receiving the first machine learning model from the network according to at least one of the first configuration or the second configuration based on the determining comprises receiving the first machine learning model according to the first configuration.

6 . The apparatus of claim 3 , wherein:
the user equipment is the second type of user equipment, determining to apply at least one of the first configuration or the second configuration based on whether the user equipment is the first type of user equipment or the second type of user equipment comprises determining to apply the second configuration, and receiving the first machine learning model from the network according to at least one of the first configuration or the second configuration based on the determining comprises receiving the first machine learning model according to the second configuration.

7 . The apparatus of claim 6 , wherein the one or more processors are further configured to execute the computer-executable instructions and cause the apparatus to:
determine to apply the first configuration; and receive a second machine learning model from the network according to the first configuration.

8 . The apparatus of claim 7 , wherein the one or more processors are further configured to execute the computer-executable instructions and cause the apparatus to determine to apply one of the first machine learning model or the second machine learning model based on at least one condition of the user equipment.

9 . The apparatus of claim 8 , wherein the at least one condition of the user equipment comprises one or more of:
a battery state of the user equipment; a power state of the user equipment; a radio resource control (RRC) state of the user equipment; an active bandwidth part of the user equipment; a condition of a channel between the user equipment and the network; or a mobility state of the user equipment.

10 . The apparatus of claim 1 , wherein receiving the first machine learning model from the network according to at least one of the first configuration or the second configuration based on the determining comprises receiving the first machine learning model via one or more system information blocks (SIBs).

11 . The apparatus of claim 1 , wherein the control information comprises downlink control information (DCI) received via a physical downlink control channel (PDCCH).

12 . The apparatus of claim 11 , wherein the DCI comprises a bitmap or a codepoint configured to indicate a scheduled downlink message for receiving the first machine learning model.

13 . The apparatus of claim 11 , wherein the DCI includes a cyclic redundancy check (CRC) scrambled via a cell-specific or user equipment group-specific radio network temporary identifier (RNTI).

14 . The apparatus of claim 1 , wherein the control information comprises one or more medium access control (MAC) control elements (CEs).

15 . The apparatus of claim 14 , wherein downlink control information (DCI) scheduling the one or more MAC CEs includes a cyclic redundancy check (CRC) scrambled via a cell-specific or user equipment group-specific radio network temporary identifier (RNTI).

16 . The apparatus of claim 1 , wherein the control information comprises a radio resource control (RRC) message.

17 . The apparatus of claim 16 , wherein downlink control information (DCI) scheduling the RRC message includes a cyclic redundancy check (CRC) scrambled via a cell-specific or user equipment group-specific radio network temporary identifier (RNTI).

18 . The apparatus of claim 1 , wherein the control information comprises one or more system information blocks (SIBs).

19 . The apparatus of claim 18 , wherein downlink control information (DCI) scheduling the one or more SIBs includes a cyclic redundancy check (CRC) scrambled via a cell-specific or user equipment group-specific radio network temporary identifier (RNTI).

20 . An apparatus for wireless communications at a network entity, comprising at least one memory comprising computer-executable instructions; and one or more processors configured to execute the computer-executable instructions and cause the apparatus to:
control information for a user equipment, wherein:
the control information indicates a first configuration for receiving a first type of machine learning model and a second configuration for receiving a second type of machine learning model,
the first type of machine learning model is configured for a first type of user equipment, and
the second type of machine learning model is configured for a second type of user equipment;
transmit a first machine learning model of the first type according to the first configuration; and transmit a second machine learning model of the second type according to the second configuration.

21 . The apparatus of claim 20 , wherein the first type of machine learning model results in a lower complexity machine learning operation than the second type of machine learning model.

22 . The apparatus of claim 21 , wherein the first type of user equipment is a reduced capability user equipment and the second type of user equipment is a regular capability user equipment.

23 . The apparatus of claim 22 , wherein:
the first configuration schedules a first scheduled downlink message, and the second configuration schedules a second scheduled downlink message.

24 . The apparatus of claim 20 , wherein transmitting the first mach

Assignees

• Qualcomm Inc

Inventors

• Xu Huilin
• Namgoong June
• Ren Yuwei
• Huang Fei
• Zhang Duo

Classifications

• H04W72/1273

  of downlink data flows · CPC title

• H04L1/0061

  Error detection codes · CPC title

• H04W72/231

  the control data signalling from the layers above the physical layer, e.g. RRC or MAC-CE signalling · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N20/00

  Machine learning · CPC title