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.

公开(公告)号：US20250158688A1

公开(公告)日：2025-05-15

申请号：US18505966

申请日：2023-11-09

Applicant: QUALCOMM Incorporated

Inventor： Maximilian Wolfgang Martin ARNOLD ,  Bence MAJOR ,  Arash BEHBOODI ,  Hanno ACKERMANN ,  Fabio Valerio MASSOLI ,  Joseph Binamira SORIAGA ,  Fatih Murat PORIKLI

IPC: H04B7/06

Abstract: A processor-implemented method for multimodal beam management implemented by a network device includes receiving, by the network device, a stream of inputs from one or more sensors. The network device generates a digital twin modeling an environment of a region observed by the one or more sensors. The digital twin includes one or more objects detected based on the stream of inputs. The network device manages a wireless communication signal beam for communicating with at least one user equipment (UE) in the region observed by the one or more sensors based at least in part on the digital twin.

公开(公告)号：US20230362038A1

公开(公告)日：2023-11-09

申请号：US18158885

申请日：2023-01-24

Applicant: QUALCOMM Incorporated

Inventor： Fabio Valerio MASSOLI ,  Arash BEHBOODI ,  Hamed PEZESHKI ,  Joseph Binamira SORIAGA ,  Taesang YOO ,  Tao LUO

IPC: H04L25/02 ,  G06N20/00

CPC classification number: H04L25/024 ,  G06N20/00

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for wireless channel estimation using machine learning. A sensing matrix is processed using a set of one or more layers of a machine learning model, based on a learned sparsifying dictionary, to generate a set of associated sparse vector representations. A channel estimation is determined based on output of a final layer of the set of one or more layers of the machine learning model.

公开(公告)号：US20240248952A1

公开(公告)日：2024-07-25

申请号：US18475995

申请日：2023-09-27

Applicant: QUALCOMM Incorporated

Inventor： Gianluigi SILVESTRI ,  Fabio Valerio MASSOLI ,  Tribhuvanesh OREKONDY ,  Arash BEHBOODI ,  Joseph Binamira SORIAGA

IPC: G06F17/16

CPC classification number: G06F17/16

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for reinforcement-learning-based compressed sensing. An observed signal tensor comprising a plurality of elements is accessed, and a subset of elements of a sensing matrix is generated based on processing, from among the plurality of elements, a subset of elements of the observed signal tensor using an acquisition neural network. A subset of elements of a reconstructed signal tensor is generated based on processing a second subset of elements of the observed signal tensor and the subset of elements of the sensing matrix using a reconstruction neural network. At least the first subset of elements of the reconstructed signal tensor is output.

公开(公告)号：US20230239179A1

公开(公告)日：2023-07-27

申请号：US18100263

申请日：2023-01-23

Applicant: QUALCOMM Incorporated

Inventor： Arash BEHBOODI ,  Anna KUZINA ,  Fabio Valerio MASSOLI ,  Kumar PRATIK

IPC: H04L25/02

CPC classification number: H04L25/0242

Abstract: A processor-implemented method for estimating a channel by a deep generative model includes receiving, at a device, an observation of the channel and mapping, at the device, the observation to a mean value associated with the channel and a covariance matrix associated with the channel. The processor-implemented method also includes reconstructing, at the device, the channel based on the mean value and the covariance matrix.