公开(公告)号：US20230047986A1

公开(公告)日：2023-02-16

申请号：US17872667

申请日：2022-07-25

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Di Wu ,  Yi Tian Xu ,  Jimmy Li ,  Tianyu Li ,  Jikun Kang ,  Xue Liu ,  Xi Chen ,  Gregory Lewis Dudek ,  Seowoo Jang

IPC: H04W28/08

Abstract: Several policies are trained for determining communication parameters used by mobile devices in selecting a cell of a first communication network to operate on. The several policies form a policy bank. By adjusting the communication parameters, load balancing among cells of the first communication network is achieved. A policy selector is trained so that a target communication network, different than the first communication network, can be load balanced. The policy selector selects a policy from the policy bank for the target communication network. The target communication network applies the policy and the load is balanced on the target communication network. Improved load balancing leads to a reduction of the number of base stations needed in the target communication network.

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.