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：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.

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.

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：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.