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公开(公告)号：US20240098533A1
公开(公告)日：2024-03-21
申请号：US18457960
申请日：2023-08-29
Applicant: Samsung Electronics Co., Ltd.
Inventor： Shiyang Leng , Jeongho Jeon , Kyeongin Jeong , Caleb K. Lo
Abstract: An AI/ML monitoring operation is based on a received monitoring configuration forming part of a configuration for using an AI/ML model for a communications system operation. Based on the monitoring configuration, AI/ML model assistance information is reported, including AI/ML model monitoring results from the AI/ML monitoring operation. AI/ML model management and adaptation information based on those AI/ML model monitoring results is received, an AI/ML model management and adaptation operation is performed. The AI/ML model management and adaptation information may include parameters that characterize an action of AI/ML model management and adaptation or an indication of an action of AI/ML model management and adaptation. The action of AI/ML model management and adaptation may comprise one of model switch, model refinement or update, or model transfer.
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公开(公告)号：US20240007982A1
公开(公告)日：2024-01-04
申请号：US18468668
申请日：2023-09-15
Applicant: Samsung Electronics Co., Ltd.
Inventor： Shiyang Leng , Jeongho Jeon , Qiaoyang Ye , Joonyoung Cho , Caleb K. Lo
CPC classification number: H04W56/005 , H04W74/0833 , H04B7/01 , H04W84/06
Abstract: Methods and apparatuses for uplink timing and frequency synchronization in a wireless communication system. A method for operating a user equipment (UE) includes receiving, from a base station (BS), information indicating satellite ephemeris information of a communication satellite associated with the BS, a common timing advance (TA), and a compensated frequency offset (FO). The method further includes transmitting a physical random access channel (PRACH) based on the common TA and the compensated FO and receiving a random access response (RAR) indicating a UE-specific TA and FO. The method further includes, for transmission of an uplink (UL) channel, adjusting a TA and pre-compensating a FO based on the UE-specific TA and FO, respectively.
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公开(公告)号：US12212437B2
公开(公告)日：2025-01-28
申请号：US17652083
申请日：2022-02-22
Applicant: Samsung Electronics Co., Ltd.
Inventor： Caleb K. Lo , Jeongho Jeon , Joonyoung Cho , Gilwon Lee , Jianzhong Zhang
Abstract: Capability of a user equipment to support machine learning adaptation by a base station of a reference signal pattern is signaled between the base station and the user equipment. Configuration information from the base station indicates one or more of enabling or disabling of machine learning adaptation of the reference signal pattern, a machine learning model used for machine learning adaptation of the reference signal pattern, updated model parameters for the machine learning model, or whether model parameters received from the user equipment will be used for machine learning adaptation of the reference signal pattern. Model training may be performed or model parameters received, and reference signals are received from the base station. Information on a reference signal pattern may be transmitted by the user equipment to the serving base station. Assistance information may be transmitted by the user equipment to the base station, which configures a reference signal pattern.
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公开(公告)号：US20220407745A1
公开(公告)日：2022-12-22
申请号：US17652083
申请日：2022-02-22
Applicant: Samsung Electronics Co., Ltd.
Inventor： Caleb K. Lo , Jeongho Jeon , Joonyoung Cho , Gilwon Lee , Jianzhong Zhang
Abstract: Capability of a user equipment to support machine learning adaptation by a base station of a reference signal pattern is signaled between the base station and the user equipment. Configuration information from the base station indicates one or more of enabling or disabling of machine learning adaptation of the reference signal pattern, a machine learning model used for machine learning adaptation of the reference signal pattern, updated model parameters for the machine learning model, or whether model parameters received from the user equipment will be used for machine learning adaptation of the reference signal pattern. Model training may be performed or model parameters received, and reference signals are received from the base station. Information on a reference signal pattern may be transmitted by the user equipment to the serving base station. Assistance information may be transmitted by the user equipment to the base station, which configures a reference signal pattern.
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公开(公告)号：US20210337420A1
公开(公告)日：2021-10-28
申请号：US17236895
申请日：2021-04-21
Applicant: Samsung Electronics Co., Ltd.
Inventor： Caleb K. Lo , Pranav Madadi , Jeongho Jeon , Joonyoung Cho , Junhyuk Song
Abstract: A service management and orchestration (SMO) entity enabling a functional split between a non-real-time (RT) radio access network (RAN) intelligent controller (RIC) and an external artificial intelligence (AI)/machine learning (ML) server will, during a data collection phase, utilize the SMO entity and the non-RT RIC to collect and process RAN data and non-RAN data and, during a data transfer phase, transfer processed RAN and non-RAN data from the SMO entity to an external AI/ML server via an interface. During a training model input phase, the SMO entity receives a trained AI/ML model, metadata, and training results from the external AI/ML server via an interface and, during a configuration phase, the SMO entity uses the trained AI/ML model within the SMO entity and the non-RT RIC to transfer configuration parameters to a near-RT RIC.
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Patent Agency Ranking
公开(公告)号：US12284077B2
公开(公告)日：2025-04-22
申请号：US18159660
申请日：2023-01-25
Applicant: Samsung Electronics Co., Ltd.
Inventor： Caleb K. Lo , Jeongho Jeon , Gilwon Lee
IPC: H04L41/0816 , H04B7/06 , H04L41/08 , H04L41/16
Abstract: Machine learning (ML) adaptation of any one of reference signal (RS) temporal density, RS frequency density, RS spatial density, or number of transmission/reception points (TRPs) that transmit RS provides configuration of lower RS densities or fewer TRPs that transmit RS without significant loss of throughput in appropriate circumstances. Determinations to switch from high density transmission to low density transmission, to reduce the number of antenna ports or TRPs that transmit RS, or to fallback to high density transmission may be made by the ML model, optionally with UE assistance information.
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公开(公告)号：US20240243794A1
公开(公告)日：2024-07-18
申请号：US18534419
申请日：2023-12-08
Applicant: Samsung Electronics Co., Ltd.
Inventor： Caleb K. Lo , Gilwon Lee , Eko Onggosanusi , Md. Saifur Rahman
CPC classification number: H04B7/0626 , H04L5/0051 , H04W8/22
Abstract: Apparatuses and methods for support of Toeplitz-based channel state information (CSI) feedback/reporting methods. A method performed by a user equipment (UE) includes transmitting capability information indicating a capability of the UE to support a Toeplitz-based method of determining channel state information (CSI) reports; receiving configuration information that indicates parameters for the Toeplitz-based method of determining the CSI reports; and receiving CSI reference signals (RSs). The method further includes measuring the CSI-RSs; determining, based on the configuration information and the measured CSI-RSs, a CSI report; and transmitting the CSI report.
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公开(公告)号：US20230308349A1
公开(公告)日：2023-09-28
申请号：US18159660
申请日：2023-01-25
Applicant: Samsung Electronics Co., Ltd.
Inventor： Caleb K. Lo , Jeongho Jeon , Gilwon Lee
IPC: H04L41/0816 , H04L41/16 , H04B7/06 , H04L41/08
CPC classification number: H04L41/0816 , H04L41/16 , H04B7/0686 , H04L41/0886
Abstract: Machine learning (ML) adaptation of any one of reference signal (RS) temporal density, RS frequency density, RS spatial density, or number of transmission/reception points (TRPs) that transmit RS provides configuration of lower RS densities or fewer TRPs that transmit RS without significant loss of throughput in appropriate circumstances. Determinations to switch from high density transmission to low density transmission, to reduce the number of antenna ports or TRPs that transmit RS, or to fallback to high density transmission may be made by the ML model, optionally with UE assistance information.
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公开(公告)号：US20250007544A1
公开(公告)日：2025-01-02
申请号：US18444385
申请日：2024-02-16
Applicant: Samsung Electronics Co., Ltd.
Inventor： Caleb K. Lo , Joonyoung Cho , Jianzhong Zhang , Chance Anthony Tarver
Abstract: Methods and apparatuses for dynamically adjusting parameters of a modulation scheme and parameters of a digital pre-distorter (DPD) to pre-compensate for distortion effects of a power amplifier (PA). A user equipment (UE) comprises a modulator configured to generate modulation symbols from input bits according to an adjusted symbol constellation, a DPD operably configured to generate pre-distorted symbols, and a PA configured to amplify the pre-distorted symbols to generate transmission symbols that include distortion effects of the PA. The adjusted symbol constellation is adjusted from a target symbol constellation such that the generated modulation symbols pre-compensate for the distortion effects. The DPD is further configured to generate the pre-distorted symbols from the modulation symbols using a pre-distortion function to further pre-compensate for the distortion effects, and update the pre-distortion function such that a difference between the transmission symbols and a scaled version of the target symbol constellation is reduced.
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公开(公告)号：US11838787B2
公开(公告)日：2023-12-05
申请号：US17236895
申请日：2021-04-21
Applicant: Samsung Electronics Co., Ltd.
Inventor： Caleb K. Lo , Pranav Madadi , Jeongho Jeon , Joonyoung Cho , Junhyuk Song
CPC classification number: H04W28/0263 , G06F9/5083 , H04W28/0284
Abstract: A service management and orchestration (SMO) entity enabling a functional split between a non-real-time (RT) radio access network (RAN) intelligent controller (RIC) and an external artificial intelligence (AI)/machine learning (ML) server will, during a data collection phase, utilize the SMO entity and the non-RT RIC to collect and process RAN data and non-RAN data and, during a data transfer phase, transfer processed RAN and non-RAN data from the SMO entity to an external AI/ML server via an interface. During a training model input phase, the SMO entity receives a trained AI/ML model, metadata, and training results from the external AI/ML server via an interface and, during a configuration phase, the SMO entity uses the trained AI/ML model within the SMO entity and the non-RT RIC to transfer configuration parameters to a near-RT RIC.
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