公开(公告)号：US20230006913A1

公开(公告)日：2023-01-05

申请号：US17806716

申请日：2022-06-13

Applicant: Samsung Electronics Co., Ltd.

Inventor： Caleb K. Lo ,  Jeongho Jeon ,  Haichuan Ding ,  Joonyoung Cho ,  Pranav Madadi ,  Qiaoyang Ye

IPC: H04L43/55 ,  H04W24/10 ,  H04B17/309 ,  H04L43/0864

Abstract: UE capability for support of machine-learning (ML) based channel environment classification may be reported by a user equipment to a base station, where the channel environment classification classifies a channel environment of a channel between the UE and a base station based on one or more of UE speed or Doppler spread, UE trajectory, frequency selectivity or delay spread, coherence bandwidth, coherence time, radio resource management (RRM) metrics, block error rate, throughput, or UE acceleration. The user equipment may receive configuration for ML based channel environment classification, including at least enabling/disabling of ML based channel environment classification. When ML based channel environment classification is enabled, UE assistance information for ML based channel environment classification, and/or an indication of the channel environment (which may be a pre-defined channel environment associated with a lookup table), may be transmitted by the user equipment to the base station.

公开(公告)号：US20200382361A1

公开(公告)日：2020-12-03

申请号：US15929951

申请日：2020-05-29

Applicant: Samsung Electronics Co., Ltd

Inventor： Vikram Chandrasekhar ,  Yongseok Park ,  Shan Jin ,  Pranav Madadi ,  Eric Johnson ,  Jianzhong Zhang ,  Russell Ford

IPC: H04L12/24 ,  G06N20/00 ,  G06N5/04

Abstract: A method for discovering and diagnosing network anomalies. The method includes receiving key performance indicator (KPI) data and alarm data. The method includes extracting features based on samples obtained by discretizing the KPI data and the alarm data. The method includes generating a set of rules based on the features. The method includes identifying a sample as a normal sample or an anomaly sample. In response to identifying the sample as the anomaly sample, the method includes identifying a first rule that corresponds to the sample, wherein the first rule indicates symptoms and root causes of an anomaly included in the sample. The method further includes applying the root causes to derive a root cause explanation of the anomaly and performing a corrective action to resolve the anomaly based on the first rule.

公开(公告)号：US20250106592A1

公开(公告)日：2025-03-27

申请号：US18883976

申请日：2024-09-12

Applicant: Samsung Electronics Co., Ltd.

Inventor： Haofan Lu ,  Vishnu Vardhan Ratnam ,  Hao Chen ,  Lianjun Li ,  Pranav Madadi

IPC: H04W4/02 ,  H04B7/06

Abstract: Methods and systems for user velocity tracking using wireless technology. A computer-implemented method includes sending one or more frames to one or more transceivers to initiate a process of frame exchanges, receiving, based on the frame exchanges, channel state information (CSI) having one or more CSI impairments, processing the CSI to adjust the one or more CSI impairments of the CSI to generate a plurality of cleaned CSI metrics, and estimating a velocity of a user using the plurality of cleaned CSI metrics.

公开(公告)号：US20240396596A1

公开(公告)日：2024-11-28

申请号：US18617385

申请日：2024-03-26

Applicant: Samsung Electronics Co., Ltd.

Inventor： Pranav Madadi ,  Joonyoung Cho ,  Jianzhong Zhang

IPC: H04B7/0452 ,  H04W72/23

Abstract: Vector perturbation precoding of data is enabled or disabled by a base station, which transmits to a user equipment information including vector perturbation schemes and parameters related to vector perturbation precoding. Perturbation vectors are determined based on the vector perturbation schemes, perturbation vectors, and data signals are modulated with the perturbation vectors, with the modulated data signals being transmitted to the user equipment. The vector perturbation precoding parameters may include a power normalization factor, a modulo threshold, and quantization parameters. A downlink control information may indicate a switch between the vector perturbation schemes and a linear precoding scheme.

公开(公告)号：US20240015531A1

公开(公告)日：2024-01-11

申请号：US18181447

申请日：2023-03-09

Applicant: Samsung Electronics Co., Ltd.

Inventor： Daoud Burghal ,  Yang Li ,  Pranav Madadi ,  Jeongho Jeon ,  Joonyoung Cho ,  Jianzhong Zhang

IPC: H04W24/02 ,  H04L1/00

CPC classification number: H04W24/02 ,  H04L1/0073

Abstract: A method includes receiving a pilot signal or a measurement report from a user equipment (UE) or a base station (BS). The method also includes updating a CSI buffer with channel state information (CSI) obtained from the pilot signal or the measurement report, the CSI buffer configured to store previous uplink or downlink channel estimates. The method also includes providing at least a portion of the CSI buffer to a CSI predictor comprising an artificial intelligence (AI) model that utilizes one or more weight sharing mechanisms, the AI model comprising a sequence of layers. The method also includes predicting temporal CSI using the CSI predictor. Depending on the configured output, the method can also include and/or be used for denoising and frequency extrapolation.

公开(公告)号：US20230324533A1

公开(公告)日：2023-10-12

申请号：US18160920

申请日：2023-01-27

Applicant: Samsung Electronics Co., Ltd.

Inventor： Jeongho Jeon ,  Pranav Madadi ,  Daoud Burghal ,  Joonyoung Cho ,  Jianzhong Zhang

IPC: G01S13/00 ,  G01S13/04 ,  G01S7/00

CPC classification number: G01S13/003 ,  G01S13/04 ,  G01S7/006

Abstract: Joint configuration of cellular communication and bistatic object sensing involves transmitting, to a UE, a bistatic object sensing configuration. The bistatic object sensing configuration configures the UE to one of receive a sensing signal transmitted by a base station or transmit the sensing signal for reception by the base station. The bistatic object sensing configuration indicates sensing transmission power, waveform, and sensing resources and periodicity for the sensing signal, and may configure the UE to one of receive or transmit an object detection report.

公开(公告)号：US20220417779A1

公开(公告)日：2022-12-29

申请号：US17807353

申请日：2022-06-16

Applicant: Samsung Electronics Co., Ltd.

Inventor： Pranav Madadi ,  Jeongho Jeon ,  Joonyoung Cho

IPC: H04W24/10

Abstract: Sparsity assisted channel state information (CSI) reporting or two-step CSI reporting is enabled or disabled for a user equipment. The configuration enabling/disabling sparsity assisted truncation for CSI reporting or two-step CSI reporting includes a domain for transformation of CSI for the sparsity assisted CSI reporting, and a time window for correlated channel. When the configuration enables sparsity assisted truncation for CSI reporting, a fixed number of non-zero coefficients and one or more threshold values for delay/angle or doppler for truncation are configured. When sparsity assisted truncation for CSI reporting is enabled, received the CSI reference signals are measured based on the configuration and a CSI report is transmitted indicating a specific range for which truncation occurred. When two-step CSI reporting is enabled, CSI compression corresponding to the at least one configuration and the time window is performed and CSI feedback is transmitted along with a flag.

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

IPC: H04W28/02 ,  G06F9/50

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.