公开(公告)号：US20250112804A1

公开(公告)日：2025-04-03

申请号：US18478908

申请日：2023-09-29

Applicant: QUALCOMM Incorporated

Inventor： Akash Sandeep DOSHI ,  June NAMGOONG ,  Taesang YOO ,  Thomas Markus HEHN ,  Tribhuvanesh OREKONDY

IPC: H04L25/02 ,  H04L5/00 ,  H04W72/1273

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a generative channel model (GCM) that outputs channel information pertaining to a digital twin (DT). The UE may receive a configuration of a downlink reference signal. The UE may perform channel estimation using the configuration of the downlink reference signal. The UE may transmit a report, wherein the report includes a precoding indicator, and wherein at least one of computation of the precoding indicator, or a channel estimation algorithm for the channel estimation, uses the GCM. Numerous other aspects are described.

公开(公告)号：US20240113795A1

公开(公告)日：2024-04-04

申请号：US17935006

申请日：2022-09-23

Applicant: QUALCOMM Incorporated

Inventor： Tribhuvanesh OREKONDY ,  Arash BEHBOODI ,  Hao YE ,  Joseph Binamira SORIAGA

IPC: H04B17/391

CPC classification number: H04B17/391

Abstract: Certain aspects of the present disclosure provide techniques and apparatuses for training and using machine learning models to estimate a representation of a channel between a transmitter and a receiver in a spatial environment. 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 information about the spatial environment. One or more actions are taken based on the estimated representation of the channel.

公开(公告)号：US20220123966A1

公开(公告)日：2022-04-21

申请号：US17504341

申请日：2021-10-18

Applicant: QUALCOMM Incorporated

Inventor： Arash BEHBOODI ,  Simeng ZHENG ,  Joseph Binamira SORIAGA ,  Max WELLING ,  Tribhuvanesh OREKONDY

IPC: H04L25/02 ,  H04L25/03 ,  H04B17/391

Abstract: A method performed by an artificial neural network includes determining a conditional probability distribution representing a channel based on a data set of transmit and receive sequences. The method also includes determining a latent representation of the channel based on the conditional probability distribution. The method further includes performing a channel-based function based on the latent representation.

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：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：US20240364437A1

公开(公告)日：2024-10-31

申请号：US18584572

申请日：2024-02-22

Applicant: QUALCOMM Incorporated

Inventor： Thomas Markus HEHN ,  Tribhuvanesh OREKONDY ,  Arash BEHBOODI ,  Ori SHENTAL ,  Taesang YOO ,  June NAMGOONG ,  Akash Sandeep DOSHI ,  Ashwin SAMPATH ,  Juan Carlos BUCHELI GARCIA ,  Joseph Binamira SORIAGA

IPC: H04B17/309 ,  H04B17/391

CPC classification number: H04B17/347 ,  H04B17/3913

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a network node may obtain position information associated with a first subset of network node positions, of a set of network node positions, in connection with a map associated with a wireless coverage area. The network node may obtain map information associated with the map and may determine, based on a machine learning component, a first plurality of predicted pathloss values associated with a second subset of network node positions of the set of network node positions. Numerous other aspects are described.

公开(公告)号：US20230155704A1

公开(公告)日：2023-05-18

申请号：US18054896

申请日：2022-11-12

Applicant: QUALCOMM Incorporated

Inventor： Tribhuvanesh OREKONDY ,  Arash BEHBOODI ,  Joseph Binamira SORIAGA ,  Max WELLING

IPC: H04B17/391 ,  H04L41/16 ,  H04L41/14 ,  H04B17/309

CPC classification number: H04B17/3912 ,  H04L41/16 ,  H04L41/145 ,  H04B17/309

Abstract: Certain aspects of the present disclosure provide techniques for wireless channel modeling. A set of input data is received for data transmitted, from a transmitter, as a signal in a wireless channel. A channel model is generated for the wireless channel using a generative adversarial network (GAN). A set of simulated output data is generated by transforming the first set of input data using the channel model.