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：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：US20250168255A1

公开(公告)日：2025-05-22

申请号：US19028822

申请日：2025-01-17

Applicant: SAMSUNG ELECTRONICS CO., LTD.

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

IPC: 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：US12238190B2

公开(公告)日：2025-02-25

申请号：US18351201

申请日：2023-07-12

Applicant: SAMSUNG ELECTRONICS CO., LTD.

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

IPC: 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：US11696153B2

公开(公告)日：2023-07-04

申请号：US17391708

申请日：2021-08-02

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Xi Chen ,  Ju Wang ,  Hang Li ,  Yi Tian Xu ,  Di Wu ,  Xue Liu ,  Gregory Lewis Dudek ,  Taeseop Lee ,  Intaik Park

IPC: H04W16/18 ,  H04W16/22

CPC classification number: H04W16/22 ,  H04W16/18

Abstract: Transfer learning based on prediction determines a similarity between a source base station and a target base station. Importance of parameters is determined and training is adjusted to respect the importance of parameters. A lack of historical data is compensated by selecting a base station as source base station which has a larger amount of historical data.

公开(公告)号：US12218804B2

公开(公告)日：2025-02-04

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: H04L41/16 ,  H04L41/0816 ,  H04L41/082 ,  H04L41/147 ,  H04L43/0876 ,  H04W16/04 ,  H04W28/16

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.

公开(公告)号：US11991531B2

公开(公告)日：2024-05-21

申请号：US17570767

申请日：2022-01-07

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Chengming Hu ,  Xi Chen ,  Ju Wang ,  Hang Li ,  Jikun Kang ,  Yi Tian Xu ,  Xue Liu ,  Di Wu ,  Seowoo Jang ,  Intaik Park ,  Gregory Lewis Dudek

IPC: H04W16/22 ,  G06N3/047 ,  H04L41/14 ,  H04L41/147 ,  H04L41/16 ,  H04W24/02 ,  H04W24/08

CPC classification number: H04W16/22 ,  G06N3/047 ,  H04L41/145 ,  H04L41/147 ,  H04L41/16 ,  H04W24/02 ,  H04W24/08

Abstract: A method is provided. The method includes receiving a first dimension set, extracting a first latent feature set from the first dimension set, training a first base predictor based on the first feature set, generating a second dimension set based on the first dimension set, the second dimension set having fewer dimensions than the first dimension set, extracting a second latent feature set from the second dimension set, training a second base predictor based on the second feature set, and generating a traffic prediction based on the first base predictor and the second base predictor.

公开(公告)号：US20220004941A1

公开(公告)日：2022-01-06

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 ,  G06Q50/06 ,  G06N20/20

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：US12052584B2

公开(公告)日：2024-07-30

申请号：US18199666

申请日：2023-05-19

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Xi Chen ,  Ju Wang ,  Hang Li ,  Yi Tian Xu ,  Di Wu ,  Xue Liu ,  Gregory Lewis Dudek ,  Taeseop Lee ,  Intaik Park

IPC: H04W16/18 ,  H04W16/22

CPC classification number: H04W16/22 ,  H04W16/18

Abstract: Transfer learning based on prediction determines a similarity between a source base station and a target base station. Importance of parameters is determined and training is adjusted to respect the importance of parameters. A lack of historical data is compensated by selecting a base station as source base station which has a larger amount of historical data.

公开(公告)号：US20230353659A1

公开(公告)日：2023-11-02

申请号：US18351201

申请日：2023-07-12

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 ,  H04L67/1004 ,  H04L41/16

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.