公开(公告)号：US20210241173A1

公开(公告)日：2021-08-05

申请号：US17008218

申请日：2020-08-31

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Hang Li ,  Xi Chen ,  Di Wu ,  Xue Liu

IPC: G06N20/00 ,  H04W4/02 ,  G01P13/00 ,  G01S5/02 ,  G06N5/04

Abstract: A method of location determination with a WiFi transceiver and an AI model includes jointly training, based on various losses: a feature extractor, a location classifier, and a domain classifier. The domain classifier may include a first domain classifier and a second domain classifier. The losses used for training tend to cause feature data from the feature extractor to cluster even if a physical object in an environment has moved after training is completed. Then, the location classifier is able to accurately estimate the position of, for example, a person in a room, even if a door or window has changed from open to close or close to open between the time of training and the time of estimating the person's position.

公开(公告)号：US20240129048A1

公开(公告)日：2024-04-18

申请号：US17965360

申请日：2022-10-13

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Ju Wang ,  Xue Liu ,  Gregory Lewis Dudek

IPC: H04B17/318 ,  H04W72/00

CPC classification number: H04B17/318 ,  H04W72/005

Abstract: The present disclosure provides methods, apparatuses, and computer-readable mediums for performing ultra-wideband (UWB) remote control. In some embodiments, the method includes broadcasting an initial control request. The method further includes receiving, from one or more remote devices, at least one reply message comprising identification information and power spectrum information. The method further includes estimating, for each of the one or more remote devices, an angle indicating a pointing direction to that remote device relative to the remote control device. The method further includes determining a selected remote device that is being pointed at by the remote control device. The method further includes sending, to the one or more remote devices, a control signal comprising the identification information of the selected remote device and a control message indicating an action to be performed by the selected remote device.

公开(公告)号：US11930414B2

公开(公告)日：2024-03-12

申请号：US18133845

申请日：2023-04-12

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Jikun Kang ,  Xi Chen ,  Di Wu ,  Yi Tian Xu ,  Xue Liu ,  Gregory Lewis Dudek ,  Taeseop Lee ,  Intaik Park

IPC: H04W36/22 ,  G06N3/044 ,  G06N3/08 ,  H04W24/02 ,  H04W28/086

CPC classification number: H04W36/22 ,  G06N3/044 ,  G06N3/08 ,  H04W24/02 ,  H04W28/0861

Abstract: Hybrid use of dual policies is provided to improve a communication system. In a multiple access scenario, when an inactive user equipment (UE) transitions to an active state, it may be become a burden to a radio cell on which it was previously camping. In some embodiments, hybrid load balancing is provided using a hierarchical machine learning paradigm based on reinforcement learning in which an LSTM generates a goal for one policy influencing cell reselection so that another policy influencing handover over active UEs can be assisted. The communication system as influenced by the policies is modeled as a Markov decision process (MDP). The policies controlling the active UEs and inactive UEs are coupled, and measureable system characteristics are improved. In some embodiments, policy actions depend at least in part on energy saving.

公开(公告)号：US11750719B2

公开(公告)日：2023-09-05

申请号：US17957811

申请日：2022-09-30

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Jikun Kang ,  Xi Chen ,  Chengming Hu ,  Ju Wang ,  Gregory Lewis Dudek ,  Xue Liu

IPC: H04L67/5682 ,  H04L67/1004 ,  H04L41/16

CPC classification number: H04L67/5682 ,  H04L41/16 ,  H04L67/1004

Abstract: A server may be provided to obtain a load balancing artificial intelligence (AI) model for a plurality of base stations in a communication system. The server may obtain teacher models based on traffic data sets collected from the base stations, respectively; perform a policy rehearsal process including obtaining student models based on knowledge distillation from the teacher models, obtaining an ensemble student model by ensembling the student models, and obtaining a policy model by interacting with the ensemble student mode; provide the policy model to each of the base stations for a policy evaluation of the policy model; and based on a training continue signal being received from at least one of the base stations as a result of the policy evaluation, update the ensemble student model and the policy model by performing the policy rehearsal process on the student models.

公开(公告)号：US20230055079A1

公开(公告)日：2023-02-23

申请号：US17874925

申请日：2022-07-27

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Di WU ,  Michael Jenkin ,  Yi Tian Xu ,  Xue Liu ,  Xi Chen ,  Gregory Lewis Dudek

IPC: H04L41/147 ,  H04L41/16 ,  H04W28/08

Abstract: A method of forecasting a future load may include: obtaining source data sets and a target data set that have been collected from a plurality of source base stations and a target base station, respectively; among a plurality of source machine learning models, selecting at least one machine learn source model that has a traffic load prediction performance higher than that of a target machine learning model through a negative transfer analysis; obtaining model weights to be applied to the target machine learning model and the selected at least one source machine learning model via an attention neural network that is jointly trained with the target machine learning model and the selected source machine learning models; obtaining a load forecasting model for the target base station by combining the target machine learning model and the selected at least one source machine learning model according to the model weights; and predicting a future communication traffic load of the target base station based on the load forecasting model.

公开(公告)号：US20220295233A1

公开(公告)日：2022-09-15

申请号：US17829883

申请日：2022-06-01

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Xi Chen ,  Hang Li ,  Chenyi Zhou ,  Xue Liu ,  Di Wu ,  Gregory L. Dudek

IPC: H04W4/029 ,  H04R1/32 ,  G06Q30/02 ,  G06N3/04 ,  G06N3/08 ,  H04R3/12

Abstract: A location-aware electronic device is provided. The electronic device trains feature extraction layers, reconstruction layers, and classification layers. The training may be based on a reconstruction loss and/or a clustering loss. The electronic device processes a fingerprint to obtain an augmented fingerprint using randomization based on statistics of the fingerprint. The feature extraction layers provide feature data to both the reconstruction layers and the classification layers. The classification layers operate on the codes to obtain an estimated location label. An application processor operates on the estimated location label to provide a location-aware application result to a person.

公开(公告)号：US12165020B2

公开(公告)日：2024-12-10

申请号：US17139561

申请日：2020-12-31

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Di Wu ,  Jikun Kang ,  Hang Li ,  Xi Chen ,  Yi Tian Xu ,  Dmitriy Rivkin ,  Taeseop Lee ,  Intaik Park ,  Michael Jenkin ,  Xue Liu ,  Gregory Lewis Dudek

IPC: G06N20/00 ,  G06F18/20 ,  G06F18/21 ,  G06N3/044 ,  G06N3/08 ,  H04W28/02 ,  H04W28/08

Abstract: Rapid and data-efficient training of an artificial intelligence (AI) algorithm are disclosed. Ground truth data are not available and a policy must be learned based on limited interactions with a system. A policy bank is used to explore different policies on a target system with shallow probing. A target policy is chosen by comparing a good policy from the shallow probing with a base target policy which has evolved over other learning experiences. The target policy then interacts with the target system and a replay buffer is built up. The base target policy is then updated using gradients found with respect to the transition experience stored in the replay buffer. The base target policy is quickly learned and is robust for application to new, unseen, systems.

公开(公告)号：US11941663B2

公开(公告)日：2024-03-26

申请号：US17829883

申请日：2022-06-01

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Xi Chen ,  Hang Li ,  Chenyi Zhou ,  Xue Liu ,  Di Wu ,  Gregory L. Dudek

IPC: G06Q30/0251 ,  G06N3/04 ,  G06N3/08 ,  H04R1/32 ,  H04R3/12 ,  H04W4/029 ,  G06F1/3231 ,  H04W84/12

CPC classification number: G06Q30/0261 ,  G06N3/04 ,  G06N3/08 ,  G06Q30/0267 ,  H04R1/32 ,  H04R3/12 ,  H04W4/029 ,  G06F1/3231 ,  H04W84/12

Abstract: A location-aware electronic device is provided. The electronic device trains feature extraction layers, reconstruction layers, and classification layers. The training may be based on a reconstruction loss and/or a clustering loss. The electronic device processes a fingerprint to obtain an augmented fingerprint using randomization based on statistics of the fingerprint. The feature extraction layers provide feature data to both the reconstruction layers and the classification layers. The classification layers operate on the codes to obtain an estimated location label. An application processor operates on the estimated location label to provide a location-aware application result to a person.

公开(公告)号：US11847591B2

公开(公告)日：2023-12-19

申请号：US16953586

申请日：2020-11-20

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Di Wu ,  Yi Tian Xu ,  Xi Chen ,  Ju Wang ,  Michael Jenkin ,  Hang Li ,  Gregory Lewis Dudek ,  Xue Liu

IPC: G06Q10/04 ,  G06N20/20 ,  G06Q50/06

CPC classification number: G06Q10/04 ,  G06N20/20 ,  G06Q50/06

Abstract: A method, computer program, and computer system are provided for load forecasting. Datasets corresponding to source machine learning models and a target domain base model are identified. A set of forecasting models corresponding to the identified datasets are learned. An ensemble model is determined from the learned set of forecasting models based on gradient boosting. An available resource is allocated based on the ensemble model.

公开(公告)号：US20230114810A1

公开(公告)日：2023-04-13

申请号：US17957499

申请日：2022-09-30

Applicant: Samsung Electronics Co., Ltd.

Inventor： Hang LI ,  Ju Wang ,  Chengming Hu ,  Xi Chen ,  Xue Liu ,  Seowoo Jang ,  Gregory Lewis Dudek

IPC: H04L41/147 ,  H04L43/0876

Abstract: A server for predicting a future traffic load of a base station is provided. The server may obtain a first prediction model based on traffic data collected from the base station for a first period of time, obtain a second prediction model based on traffic data collected from the same base station for a second period time, and also based on knowledge transferred from the first prediction model. Each of the first prediction model and the second prediction model may include an encoder module, a reconstruction module, and a prediction module which are connected to form two paths, an encoder-reconstruction path and an encoder-prediction path, to preserve more information of historic traffic data.