Methods for Error Cause Determination for Two-Sided Models Independently Trained by Different Vendors

Patent metadata

Field	Value
Publication number	US-2026037359-A1
Application number	US-202418794901-A
Country	US
Kind code	A1
Filing date	Aug 5, 2024
Priority date	Aug 5, 2024
Publication date	Feb 5, 2026
Grant date	—

Abstract

An example Wireless Transmit/Receive Unit (WTRU) comprising a processor is provided. The processor is configured to receive configuration information for a machine learning (ML) model. The processor is further configured to receive a request for activating an assessment mode associated with the ML model. The processor is further configured to collect measurements for the assessment mode. The processor is further configured to determine an error cause associated with the ML model based on the measurements. The processor is further configured to send one or more reports that include at least one of an indication of the error cause, an indication of a measurement related to the error cause, or an indication of a mitigation action for the error cause.

First claim

1 . A Wireless Transmit/Receive Unit (WTRU) comprising:
a processor configured to:
receive configuration information for a machine learning (ML) model;
receive a request for activating an assessment mode associated with the ML model;
collect measurements for the assessment mode;
determine an error cause associated with the ML model based on the measurements, wherein the determined error cause includes an indication of at least one of: data distribution measurements being out-of-distribution (OOD) with respect to a dataset used to train the ML model, or an identity of a device at which the error cause occurred when implementing the ML model; and
send one or more reports that include at least one of: an indication of the error cause, an indication of a measurement related to the error cause, or an indication of a mitigation action for the error cause.

2 . The WTRU of claim 1 , wherein the request includes an indication of a time window, and wherein the processor is configured to collect the measurements during the time window indicated in the request.

3 . The WTRU of claim 1 , wherein the ML model is a two-sided ML model that includes a first-side model associated with a first node and a second-side model associated with a second node.

4 . The WTRU of claim 3 , wherein the first node is the WTRU.

5 . The WTRU of claim 3 , wherein the processor, to determine the error cause, is configured to associate an error with at least one of: the first-side model, the second-side model, or interoperability between the first-side model and the second-side model.

6 . The WTRU of claim 3 , wherein the processor, to determine the error cause, is configured to:
determine whether data associated with the collected measurements is out-of-distribution (OOD) with respect to training data used to train the first-side model or the second-side model; and determine that the error cause corresponds to data drift based on a determination that the data associated with the collected measurements is OOD.

7 . The WTRU of claim 1 , wherein the configuration information includes one or more parameters for the assessment mode, wherein the one or more parameters include at least one of: an identifier associated with a dataset used to train a reference ML encoder, an identifier associated with parameters of the reference ML encoder, parameters for out-of-distribution (OOD) detection, or one or more threshold values associated with one or more intermediate key performance indicators (KPIs).

8 . The WTRU of claim 1 , wherein the processor, to collect the measurements, is configured to:
determine end-to-end (E2E) performance statistics associated with one or more of: hybrid automatic repeat request (HARQ) acknowledgement or negative acknowledgement (ACK/NACK), block error rate (BLER), rank indicator (RI), or channel quality indicator (CQI).

9 . The WTRU of claim 1 , wherein the processor, to collect the measurements, is configured to:
determine one or more intermediate key performance indicators (KPIs), wherein the one or more KPIs include at least one of square generalized cosine similarity (SGCS) or normalized mean square error (NMSE).

10 . The WTRU of claim 1 , wherein the request includes an indication of a time window, and wherein the processor, to collect the measurements, is configured to:
based on a length of the time window exceeding a threshold, determine whether the data distribution measurements are out-of-distribution (OOD) with respect to the dataset used to train the ML model.

11 . The WTRU of claim 1 , wherein the processor is configured to:
in response to a determination that the error cause corresponds to data drift, send, in the one or more reports, one or more channel state information (CSI) data distribution metrics relative to the dataset used to train the ML model.

12 . The WTRU of claim 1 , wherein the processor is configured to:
in response to a determination that the error cause is associated with a WTRU-side ML encoder, send, in the one or more reports, one or more intermediate key performance indicators (KPIs).

13 . The WTRU of claim 1 , wherein the processor is configured to:
in response to a failure to determine the error cause, send a report that includes an indication of one or more intermediate key performance indicators (KPIs) and one or more channel state information (CSI) data distribution metrics relative to the dataset used to train the ML model.

14 . The WTRU of claim 1 , wherein the mitigation action includes at least one of: a recommended mitigation action, or a determined mitigation action performed at the WTRU in response to the determination of the error cause.

15 . A method performed by a Wireless Transmit/Receive Unit (WTRU), the method comprising:
receiving configuration information for a machine learning (ML) model; receiving a request for activating an assessment mode associated with the ML model; collecting measurements for the assessment mode; determining an error cause associated with the ML model based on the measurements, wherein the determined error cause includes an indication of at least one of: data distribution measurements being out-of-distribution (OOD) with respect to a dataset used to train the ML model, or an identity of a device at which the error cause occurred when implementing the ML model; and sending one or more reports that include at least one of: an indication of the error cause, an indication of a measurement related to the error cause, or an indication of a mitigation action for the error cause.

16 . The method of claim 15 , wherein the request includes an indication of a time window, and wherein collecting the measurements is during the time window indicated in the request.

17 . The method of claim 15 , wherein the ML model is a two-sided ML model that includes a first-side model associated with a first node and a second-side model associated with a second node.

18 . The method of claim 17 , wherein the first node is the WTRU.

19 . The method of claim 17 , wherein determining the error cause comprises associating an error with at least one of: the first-side model, the second-side model, or interoperability between the first-side model and the second-side model.

20 . The method of claim 17 , wherein determining the error cause comprises associating an error with data drift, and wherein associating the error with data drift comprises determining that data associated with the collected measurements is out-of-distribution (OOD) with respect to training data used to train the first-side model or the second-side model.

Assignees

• Interdigital Patent Holdings Inc

Inventors

• Beluri Mihaela
• Tooher Patrick
• Lee Moon Il
• Patani Keya
• Narayanan Thangaraj Yugeswar Deenoo

Classifications

• G06Q10/06393

  Score-carding, benchmarking or key performance indicator [KPI] analysis · CPC title

• G06F11/079 Primary

  Root cause analysis, i.e. error or fault diagnosis (in a hardware test environment G06F11/22; in a software test environment G06F11/36) · CPC title

• G06N3/00

  Computing arrangements based on biological models · CPC title

• G06N20/00

  Machine learning · CPC title

• H04L1/0026 Primary

  Transmission of channel quality indication · CPC title