WO-2026096106-A1 - TIMESTAMPING FOR DATA COLLECTION FOR MACHINE LEARNING-BASED POSITIONING

Abstract

Disclosed are techniques for data collection for a machine learning model for positioning. In an aspect, a first device transmits, to a second device, a request for timestamping assistance information associated with a set of timestamps associated with a set of data samples for training or monitoring the machine learning model. The first device receives the set of data samples from the second device, wherein the set of data samples includes channel measurements for input into the machine learning model, labels for output by the machine learning model, or both.

Inventors

• HIRZALLAH, Mohammed Ali Mohammed
• MANOLAKOS, Alexandros
• FISCHER, SVEN

Assignees

• QUALCOMM INCORPORATED

Dates

Publication Date

20260507

Application Date

20250918

Priority Date

20241029

Claims (20)

1. CLAIMS
2. What is claimed is:
3. 1. A first device, comprising:
4. one or more memories;
5. one or more transceivers; and
6. one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors, either alone or in combination, configured to:
7. transmit, via the one or more transceivers, to a second device, a request for timestamping assistance information associated with a set of timestamps associated with a set of data samples for training or monitoring the machine learning model; and
8. receive, via the one or more transceivers, the set of data samples from the second device, wherein the set of data samples includes channel measurements for input into the machine learning model, labels for output by the machine learning model, or both.
9. 2. The first device of claim 1, wherein the one or more processors, either alone or in combination, are further configured to:
10. receive, via the one or more transceivers, the set of timestamps associated with the set of data samples from the second device; and
11. receive, via the one or more transceivers, the timestamping assistance information from the second device.
12. 3. The first device of claim 2, wherein the timestamping assistance information comprises:
13. assistance for timestamping of the channel measurements for input into the machine learning model,
14. assistance for timestamping of the labels for output by the machine learning model,
15. assistance for timestamping of both the channel measurements for input into the machine learning model and the labels for output by the machine learning model. one or more types of the set of timestamps associated with the set of data samples,
16. one or more resolutions of the set of timestamps associated with the set of data samples, or
17. a combination thereof.
18. 4. The first device of claim 2, wherein the timestamping assistance information is received in:
19. a Long-Tenn Evolution (LTE) positioning protocol (LPP) provide assistance data message,
20. a New Radio positioning protocol type A (NRPPa) assistance information message,

Description

TIMESTAMPING FOR DATA COLLECTION FOR MACHINE LEARNINGBASED POSITIONING TECHNICAL FIELD [0001] Aspects of the disclosure relate generally to wireless technologies. BACKGROUND [0002] Wireless communication systems have developed through various generations, including a first-generation analog wireless phone service (1G), a second-generation (2G) digital wireless phone service (including interim 2.5G and 2.75G networks), a third-generation (3G) high speed data, Internet-capable wireless service and a fourth-generation (4G) service (e.g., Long Term Evolution (LTE) orWiMax). There are presently many different types of wireless communication systems in use, including cellular and personal communications service (PCS) systems. Examples of known cellular systems include the cellular analog advanced mobile phone system (AMPS), and digital cellular systems based on code division multiple access (CDMA), frequency division multiple access (FDMA), time division multiple access (TOMA), the Global System for Mobile communications (GSM), etc. [0003] A fifth generation (5G) wireless standard, referred to as New Radio (NR), enables higher data transfer speeds, greater numbers of connections, and beter coverage, among other improvements. The 5G standard, according to the Next Generation Mobile Networks Alliance, is designed to provide higher data rates as compared to previous standards, more accurate positioning (e.g., based on reference signals for positioning (RS-P), such as downlink, uplink, or sidelink positioning reference signals (PRS)), RF sensing, and other technical enhancements. These enhancements, as well as the use of higher frequency bands, enable improved RF sensing and 5G-based positioning. SUMMARY [0004] The following presents a simplified summary relating to one or more aspects disclosed herein. Thus, the following summary should not be considered an extensive overview' relating to all contemplated aspects, nor should the following summary be considered to identify key or critical elements relating to all contemplated aspects or to delineate the scope associated with any particular aspect. Accordingly, the following summary has the sole purpose to present certain concepts relating to one or more aspects relating to the mechanisms disclosed herein in a simplified form to precede the detailed description presented below. [0005] In an aspect, a method of data collection for a machine learning model for positioning performed by a first device includes transmitting, to a second device, a request for timestamping assistance information associated with a set of timestamps associated with a set of data samples for training or monitoring the machine learning model; and receiving the set of data samples from the second device, wherein the set of data samples includes channel measurements for input into the machine learning model, labels for output by the machine learning model, or both. [0006] In an aspect, a first device includes one or more memories; one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors, either alone or in combination, configured to: transmit, via the one or more transceivers, to a second device, a request for timestamping assistance information associated with a set of timestamps associated with a set of data samples for training or monitoring the machine learning model; and receive, via the one or more transceivers, the set of data samples from the second device, wherein the set of data samples includes channel measurements for input into the machine learning model, labels for output by the machine learning model, or both. [0007] In an aspect, a first device includes means for transmitting, to a second device, a request for timestamping assistance information associated with a set of timestamps associated with a set of data samples for training or monitoring the machine learning model; and means for receiving the set of data samples from the second device, wherein the set of data samples includes channel measurements for input into the machine learning model, labels for output by the machine learning model, or both. [0008] In an aspect, a non-transitory computer-readable medium stores computer-executable instructions that, when executed by a first device, cause the first device to: transmit, to a second device, a request for timestamping assistance information associated with a set of timestamps associated with a set of data samples for training or monitoring the machine learning model; and receive the set of data samples from the second device, wherein the set of data samples includes channel measurements for input into the machine learning model, labels for output by the machine learning model, or both. [0009] Other objects and advantages associated with the aspects disclosed herein will be apparent to those skilled in the art based on the accompanying drawings and detailed desc