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1.
公开(公告)号:US20250168657A1
公开(公告)日:2025-05-22
申请号:US18838362
申请日:2023-03-29
Applicant: QUALCOMM Incorporated
Inventor: Qiaoyu LI , Taesang YOO , Mahmoud TAHERZADEH BOROUJENI , Hamed PEZESHKI , Tianyang BAI , Tao LUO
Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, from a network entity, a configuration associated with channel measurement resources (CMRs). The UE may perform downlink channel measurements associated with the CMRs, wherein the downlink channel measurements are associated with one or more time instances. The UE may store the downlink channel measurements associated with the one or more time instances for a period of time at the UE. The UE may transmit, to the network entity and based at least in part on a request, at least a portion of the downlink channel measurements associated with the one or more time instances. Numerous other aspects are described.
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公开(公告)号:US20250150848A1
公开(公告)日:2025-05-08
申请号:US18502250
申请日:2023-11-06
Applicant: QUALCOMM Incorporated
Inventor: Mohammed Ali Mohammed HIRZALLAH , Taesang YOO , Rajat PRAKASH
Abstract: Techniques are provided for utilizing QCL relationships with AI/ML models and reference signals. An example method for providing positioning reference signal configuration information includes receiving, from a wireless node, an indication of a quasi co-location (QCL) relationship between an AI/ML model and a reference signal, configuring one or more positioning reference signal resources based at least in part on the indication of the QCL relationship, and providing configuration information for the one or more positioning reference signal resources to the wireless node.
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公开(公告)号:US20250150134A1
公开(公告)日:2025-05-08
申请号:US18837806
申请日:2022-04-30
Applicant: QUALCOMM Incorporated
Inventor: Chenxi HAO , Yuwei REN , Taesang YOO , Hao XU , Yu ZHANG , Rui HU , Wei XI , Ruiming ZHENG , Eren BALEVI , June NAMGOONG , Pavan Kumar VITTHALADEVUNI
Abstract: Aspects described herein relate to using machine learning (ML) models for performing channel state information (CSI) encoding or decoding, CSI-reference signal (RS) optimization, channel estimation, etc. The ML models can be trained by a user equipment (UE), separately by the UE and a network node (e.g., base station), or jointly by the UE and network node.
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公开(公告)号:US20250133418A1
公开(公告)日:2025-04-24
申请号:US18489723
申请日:2023-10-18
Applicant: QUALCOMM Incorporated
Inventor: Mohammed Ali Mohammed HIRZALLAH , Taesang YOO , Rajat PRAKASH
Abstract: Certain aspects of the present disclosure provide techniques for quasi-model (QML) relation indication and configuration for artificial intelligence (AI)/machine learning (ML) air interface operation. An example method, performed at a first wireless node, generally includes transmitting, to a second wireless node, an indication that a first machine learning (ML) model shares one or more properties with at least a second ML model; and utilizing at least the first ML model to communicate with the second wireless node.
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公开(公告)号:US20250126595A1
公开(公告)日:2025-04-17
申请号:US18728532
申请日:2023-02-21
Applicant: QUALCOMM Incorporated
Inventor: Sooryanarayanan GOPALAKRISHNAN , Jay Kumar SUNDARARAJAN , Taesang YOO , Alexandros MANOLAKOS , Krishna Kiran MUKKAVILLI , Naga BHUSHAN
IPC: H04W64/00
Abstract: Techniques are provided herein for signaling path selection modes for positioning. An example method of signaling a path selection mode includes obtaining one or more signal measurement values for a reference signal, determining one or more propagation paths based on the one or more signal measurement values and the path selection mode, and report the one or more propagation paths and the path selection mode to a location server.
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公开(公告)号:US20250097140A1
公开(公告)日:2025-03-20
申请号:US18965868
申请日:2024-12-02
Applicant: QUALCOMM Incorporated
Inventor: Tao LUO , Juan MONTOJO , Tamer KADOUS , Junyi LI , Xiaoxia ZHANG , Jing SUN , Taesang YOO , Siddhartha MALLIK
IPC: H04L45/16 , H03M13/09 , H04B7/06 , H04B7/216 , H04B17/24 , H04B17/318 , H04L1/00 , H04L1/1829 , H04L5/00 , H04L12/18 , H04L41/0668 , H04W24/02 , H04W24/04 , H04W24/08 , H04W48/08 , H04W52/14 , H04W52/16 , H04W52/24 , H04W52/34 , H04W52/38 , H04W52/42 , H04W52/50 , H04W52/54 , H04W52/56 , H04W72/044 , H04W72/12 , H04W72/20 , H04W72/21 , H04W72/52 , H04W72/53 , H04W72/54 , H04W76/19 , H04W76/22 , H04W92/20
Abstract: Various aspects of the disclosure relate to power control for independent links. For example, power control at a device may be based on transmissions on multiple links. In some aspects, the independent links may involve a first device (e.g., a user equipment) communicating via different independent links with different devices (e.g., transmit receive points (TRPs) or sets of TRPs). For example, the first device may communicate with a second device (e.g., a TRP) via a first link and communicate with a third device (e.g., a TRP) via a second link. In some scenarios, power control for the first device may be based on power control commands received on multiple links. In some scenarios, a power control constraint may be met taking into account the transmission power on multiple links.
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公开(公告)号:US20250062810A1
公开(公告)日:2025-02-20
申请号:US18450821
申请日:2023-08-16
Applicant: QUALCOMM Incorporated
Inventor: June NAMGOONG , Akash Sandeep DOSHI , Taesang YOO , Hyojin LEE
IPC: H04B7/06 , H04B7/0456 , H04W72/0457
Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, from a network node, decoder configuration information associated with a transmitter neural network configured to be used to generate at least one latent vector corresponding to one or more computation tasks of a plurality of computation tasks associated with a query-based cross-node machine learning system. The UE may receive, from the network node, query configuration information associated with a query-based decoder. The UE may transmit, to the network node and based at least in part on instantiation of the transmitter neural network by the UE, the at least one latent vector. Numerous other aspects are described.
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公开(公告)号:US20250056555A1
公开(公告)日:2025-02-13
申请号:US18932528
申请日:2024-10-30
Applicant: QUALCOMM Incorporated
Inventor: June NAMGOONG , Taesang YOO , Naga BHUSHAN , Krishna Kiran MUKKAVILLI , Tingfang JI
Abstract: A transmitting device for wireless communication calculates distortion error based on a non-distorted digital transmit waveform and a non-linearity. The transmitting device compresses the distortion error with an encoder neural network of an auto-encoder. The transmitting device transmits, to a receiving device, the compressed distortion error to compensate for the non-linearity in a power amplifier (PA).
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9.
公开(公告)号:US20250008513A1
公开(公告)日:2025-01-02
申请号:US18693029
申请日:2021-12-02
Applicant: QUALCOMM INCORPORATED
Inventor: Hyojin LEE , Chenxi HAO , June NAMGOONG , Taesang YOO , Wei XI , Pavan Kumar VITTHALADEVUNI , Yu ZHANG , Hwan Joon KWON
IPC: H04W72/1273 , H04L25/02 , H04W72/542
Abstract: Aspects described herein relate to encoding, based on an estimated channel matrix of a reference signal received from a base station, channel state information (CSI) using a machine learning (ML)-based CSI encoder, transmitting, to the base station, an output of the ML-based CSI encoder and assistance information related to the estimated channel matrix, and receiving, from the base station, a scheduling grant for a downlink channel having at least one parameter that is based on the output of the ML-based CSI encoder and the assistance information. Other aspects relate to receiving the CSI encoder output and assistance information, and transmitting the scheduling grant.
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公开(公告)号:US20240421875A1
公开(公告)日:2024-12-19
申请号:US18335726
申请日:2023-06-15
Applicant: QUALCOMM Incorporated
Inventor: Pavan Kumar VITTHALADEVUNI , Jay Kumar SUNDARARAJAN , Taesang YOO , Krishna Kiran MUKKAVILLI , Naga BHUSHAN
Abstract: Methods, systems, and devices for wireless communication are described. A user equipment (UE) may select a non-Discrete Fourier Transform (non-DFT) codebook of a set of non-DFT codebooks associated with a channel state feedback message. The UE may determine a set of singular vectors associated with the non-DFT codebook based on a first machine learning model, where the set of singular vectors corresponds to a subspace associated with the non-DFT codebook. The UE may compress the non-DFT codebook, the set of singular vectors, or both, based on a second machine learning model. The UE may transmit the channel state feedback message including the compressed non-DFT codebook, the compressed set of singular vectors, or both.
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