公开(公告)号：US20240112009A1

公开(公告)日：2024-04-04

申请号：US17935046

申请日：2022-09-23

Applicant: QUALCOMM Incorporated

Inventor： Tribhuvanesh OREKONDY ,  Arash BEHBOODI ,  Kumar PRATIK ,  Joseph Binamira SORIAGA ,  Shreya KADAMBI

IPC: G06N3/08 ,  H04B17/391

CPC classification number: G06N3/08 ,  H04B17/391

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for training and using machine learning models to estimate a layout of a spatial area. An example method generally includes estimating a representation of a channel using a machine learning model trained to generate the estimated representation of the channel based on a location of a transmitter in a spatial environment, a location of a receiver in the spatial environment, and a three-dimensional representation of the spatial environment. One or more actions are taken based on the estimated representation of the channel.

公开(公告)号：US20230152419A1

公开(公告)日：2023-05-18

申请号：US18054298

申请日：2022-11-10

Applicant: QUALCOMM Incorporated

Inventor： Shreya KADAMBI ,  Arash BEHBOODI ,  Joseph Binamira SORIAGA ,  Max WELLING

IPC: G01S5/02 ,  G01S5/00 ,  G01S5/10

CPC classification number: G01S5/0273 ,  G01S5/0027 ,  G01S5/0215 ,  G01S5/10

Abstract: Certain aspects of the present disclosure provide methods, apparatus, and systems for predicting a location of a device in a spatial environment using a machine learning model. An example method generally includes measuring a plurality of signals received from a network entity at a device. A channel state information (CSI) measurement is generated from the measured plurality of signals. Generally, the CSI measurement includes a multipath component. Positions of one or more anchors in a spatial environment are identified based on a machine learning model trained to identify the positions of the one or more anchors based on the CSI measurement. A location of the device is estimated based on the identified positions of the one or more anchors.

公开(公告)号：US20250131606A1

公开(公告)日：2025-04-24

申请号：US18492572

申请日：2023-10-23

Applicant: QUALCOMM Incorporated

Inventor： Shubhankar Mangesh BORSE ,  Risheek GARREPALLI ,  Qiqi HOU ,  Jisoo JEONG ,  Shreya KADAMBI ,  Munawar HAYAT ,  Fatih Murat PORIKLI

IPC: G06T11/00 ,  G06T3/40

Abstract: A processor-implemented method includes receiving a text-semantic input at a first stage of a neural network, including a first convolutional block and no attention layers. The method receives, at a second stage, a first output from the first stage. The second stage comprises a first down sampling block including a first attention layer and a second convolutional block. The method receives, at a third stage, a second output from the second stage. The third stage comprises a first up sampling block including a second attention layer and a first set of convolutional blocks. The method receives, at a fourth stage, the first output from the first stage and a third output from the third stage. The fourth stage comprises a second up sampling block including no attention layers and a second set of convolutional blocks. The method generates an image at the fourth stage, based on the text-semantic input.

公开(公告)号：US20250131325A1

公开(公告)日：2025-04-24

申请号：US18492492

申请日：2023-10-23

Applicant: QUALCOMM Incorporated

Inventor： Risheek GARREPALLI ,  Shubhankar Mangesh BORSE ,  Jisoo JEONG ,  Qiqi HOU ,  Shreya KADAMBI ,  Munawar HAYAT ,  Fatih Murat PORIKLI

IPC: G06N20/00

Abstract: A method for training a diffusion model includes compressing the diffusion model by removing at least one of: one or more model parameters or one or more giga multiply-accumulate operations (GMACs). The method also includes performing guidance conditioning to train the compressed diffusion model, the guidance conditioning combining a conditional output and an unconditional output from respective teacher models. The method further includes performing, after the guidance conditioning, step distillation on the compressed diffusion model.

公开(公告)号：US20250131277A1

公开(公告)日：2025-04-24

申请号：US18492529

申请日：2023-10-23

Applicant: QUALCOMM Incorporated

Inventor： Risheek GARREPALLI ,  Shubhankar Mangesh BORSE ,  Jisoo JEONG ,  Qiqi HOU ,  Shreya KADAMBI ,  Munawar HAYAT ,  Fatih Murat PORIKLI

IPC: G06N3/09

Abstract: A method for training a control neural network includes initializing a baseline diffusion model for training the control neural network, each stage of a control neural network training pipeline corresponding to an element of the baseline diffusion model. The method also includes training, the control neural network, in a stage-wise manner, each stage of the control neural network training pipeline receiving an input from a previous stage of the control neural network training pipeline and the corresponding element of the diffusion model.

公开(公告)号：US20250131276A1

公开(公告)日：2025-04-24

申请号：US18492508

申请日：2023-10-23

Applicant: QUALCOMM Incorporated

Inventor： Risheek GARREPALLI ,  Shubhankar Mangesh BORSE ,  Jisoo JEONG ,  Qiqi HOU ,  Shreya KADAMBI ,  Munawar HAYAT ,  Fatih Murat PORIKLI

IPC: G06N3/09

Abstract: A method for training a diffusion model includes randomly selecting, for each iteration of a step distillation training process, a teacher model of a group of teacher models. The method also includes applying, at each iteration, a clipped input space within step distillation of the randomly selected teacher model. The method further includes updating, at each iteration, parameters of the diffusion model based on guidance from the randomly selected teacher model.

公开(公告)号：US20240144087A1

公开(公告)日：2024-05-02

申请号：US18340671

申请日：2023-06-23

Applicant: QUALCOMM Incorporated

Inventor： Fabio Valerio MASSOLI ,  Ang LI ,  Shreya KADAMBI ,  Hao YE ,  Arash BEHBOODI ,  Joseph Binamira SORIAGA ,  Bence MAJOR ,  Maximilian Wolfgang Martin ARNOLD

IPC: G06N20/00 ,  H04B7/06

CPC classification number: G06N20/00 ,  H04B7/0695

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for beam selection using machine learning. A plurality of data samples corresponding to a plurality of data modalities is accessed. A plurality of features is generated by, for each respective data sample of the plurality of data samples, performing feature extraction based at least in part on a respective modality of the respective data sample. The plurality of features is fused using one or more attention-based models, and a wireless communication configuration is generated based on processing the fused plurality of features using a machine learning model.