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.

公开(公告)号：US11751115B2

公开(公告)日：2023-09-05

申请号：US17363918

申请日：2021-06-30

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 ,  G06N3/08 ,  H04W24/02 ,  G06N3/044 ,  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：US20230156520A1

公开(公告)日：2023-05-18

申请号：US17965294

申请日：2022-10-13

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Di WU ,  Manyou Ma ,  Yi Tian Xu ,  Jimmy Li ,  Seowoo Jang ,  Xue Liu ,  Gregory Lewis Dudek

IPC: H04W28/08 ,  H04W28/02

CPC classification number: H04W28/0925 ,  H04W28/0226

Abstract: A method includes obtaining at least one policy parameter of a neural network corresponding to a load balancing policy, receiving trajectories for each mobile device in a plurality of mobile devices of the wireless network, each trajectory corresponding to a sequence of states of a respective mobile device, wherein the sequence of states is generated based on a continuous interaction of an existing policy of the respective mobile device with the wireless network, estimating advantage functions for each mobile device in the plurality of mobile devices based on the trajectories for each respective mobile device, and updating the at least one policy parameter based on the estimated advantage functions such that the load balancing policy is determined based on states of each mobile device in the plurality of mobile devices.

公开(公告)号：US11461699B2

公开(公告)日：2022-10-04

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 ,  G01S5/02 ,  G06N5/04 ,  G01P13/00 ,  H04W84/12

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：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：US20240422667A1

公开(公告)日：2024-12-19

申请号：US18631726

申请日：2024-04-10

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Junliang LUO ,  Yi Tian Xu ,  Di Wu ,  Xue Liu ,  Gregory Lewis Dudek

IPC: H04W52/02 ,  H04L41/16 ,  H04W28/24

Abstract: A method performed by at least one processor of a network device in communication with a plurality of base stations, the method including: receiving historical data collected by one or more base stations from the plurality of base stations, the historical data indicating one or more of a power consumption, handover data, and quality of service (QOS); generating, from the historical data, training data comprising a plurality of cell states and a corresponding random action for each cell state; and training one or more neural network estimators based on the training data, where the one or more neural network estimators comprise one or more of a power consumption estimator, a QoS estimator, and a handover prediction estimator, and where each base station from the plurality of base stations is associated with a respective cell.

公开(公告)号：US20240353958A1

公开(公告)日：2024-10-24

申请号：US18529852

申请日：2023-12-05

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Francois Robert HOGAN ,  Trevor Ablett ,  Xue Liu ,  Gregory Lewis Dudek ,  Amal Feriani

IPC: G06F3/042 ,  G06F3/03 ,  G06T3/02 ,  G06T7/73

CPC classification number: G06F3/0425 ,  G06F3/0325 ,  G06T3/02 ,  G06T7/73 ,  G06T2207/30208

Abstract: A method performed by an electronic device, includes: obtaining an image of a set of markers; based on the image, detecting an arrangement of the set of markers; based on the arrangement of the set of markers, performing a measurement about a deformation of the set of markers; based on the measurement about the deformation of the set of markers, generating a plurality of signals; transforming the plurality of signals to a plurality of input commands. The plurality of input commands are used to control the electronic device.

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.