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    TRAFFIC SCENARIO CLUSTERING AND LOAD BALANCING WITH DISTILLED REINFORCEMENT LEARNING POLICIES
    1.
    发明申请
    TRAFFIC SCENARIO CLUSTERING AND LOAD BALANCING WITH DISTILLED REINFORCEMENT LEARNING POLICIES 有权

    公开(公告)号：US20230117162A1

    公开(公告)日：2023-04-20

    申请号：US17870212

    申请日：2022-07-21

    Applicant: SAMSUNG ELECTRONICS CO., LTD.

    Inventor： Jimmy LI ,  Di WU ,  Yi Tian XU ,  Tianyu LI ,  Seowoo JANG ,  Xue LIU ,  Gregory Lewis DUDEK

    IPC: H04W28/08 ,  G06N20/00

    Abstract: The present disclosure provides for methods, apparatuses, and non-transitory computer-readable storage media for load balancing traffic scenarios by a network device. In an embodiment, a method includes training a plurality of learning agents to load balance a respective plurality of traffic scenarios to obtain a plurality of control policies. The method further includes performing at least one clustering iteration. Each clustering iteration includes selecting a pair of control policies and merging the pair of control policies into a clustered control policy that replaces the pair of control policies. The method further includes determining to stop the performing of the at least one clustering iteration when a quantity of control policies remaining in the plurality of control policies meets a predetermined value. The method further includes deploying to each base station of a plurality of base stations a corresponding control policy from the plurality of control policies.

    METHOD OF LOAD FORECASTING VIA ATTENTIVE KNOWLEDGE TRANSFER, AND AN APPARATUS FOR THE SAME
    3.
    发明申请
    METHOD OF LOAD FORECASTING VIA ATTENTIVE KNOWLEDGE TRANSFER, AND AN APPARATUS FOR THE SAME 有权

    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.

    PROBABILISTIC MOBILITY LOAD BALANCING IN MULTI-BAND MOBILE COMMUNICATION NETWORK
    4.
    发明申请
    PROBABILISTIC MOBILITY LOAD BALANCING IN MULTI-BAND MOBILE COMMUNICATION NETWORK 有权

    公开(公告)号：US20240422642A1

    公开(公告)日：2024-12-19

    申请号：US18593262

    申请日：2024-03-01

    Applicant: SAMSUNG ELECTRONICS CO., LTD.

    Inventor： MHD Saria AL LAHHAM ,  Di WU ,  Abu Zafar Ekram HOSSAIN ,  Xue LIU ,  Gregory Lewis DUDEK

    IPC: H04W36/30 ,  H04B17/318 ,  H04W36/06 ,  H04W76/20

    Abstract: A method performed by an electronic device of a wireless communication system, includes: receiving channel qualities and movements reports from a plurality of user equipments; based on the channel qualities and the movements reports, determining whether a load balancing index (LBI) is lower than a predetermined threshold; based on identifying that the LBI is lower than the predetermined threshold, determining an assignment matrix between the plurality of UEs and the plurality of operating bands by calculating minimized maximum band loads and minimized number of inter-frequency handovers (HOs); and transmitting a message to a first UE of the plurality of UEs, wherein the message indicates that the first UE is assigned to a first operating band of the plurality of the operating bands, based on the assignment matrix.

    ENERGY SAVING IN CELLULAR WIRELESS NETWORKS VIA TRANSFER DEEP REINFORCEMENT LEARNING
    5.
    发明申请
    ENERGY SAVING IN CELLULAR WIRELESS NETWORKS VIA TRANSFER DEEP REINFORCEMENT LEARNING 有权

    公开(公告)号：US20240406861A1

    公开(公告)日：2024-12-05

    申请号：US18609797

    申请日：2024-03-19

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Di WU ,  Yi Tian XU ,  Michael JENKIN ,  Seowoo JANG ,  Abu Zafar Ekram HOSSAIN ,  Xue LIU ,  Gregory Lewis DUDEK

    IPC: H04W52/02 ,  H04L41/16

    Abstract: The present disclosure provides methods, apparatuses, systems, and computer-readable mediums for operating a target base station by an apparatus. A method includes collecting a plurality of trajectories corresponding to the target base station and a plurality of source base stations, clustering, using an unsupervised reinforcement learning model, the plurality of trajectories into a plurality of clusters including a target cluster, selecting, as a target trajectory, a selected trajectory from the target cluster that maximizes an energy-saving parameter of the target base station, and applying, to the target base station, an energy-saving control policy corresponding to the target trajectory. The target cluster corresponds to the target base station and at least one source base station from among the plurality of source base stations.

    IMAGE MORPHING ADAPTION METHOD AND APPARATUS
    9.
    发明公开
    IMAGE MORPHING ADAPTION METHOD AND APPARATUS 审中-公开

    公开(公告)号：US20230289918A1

    公开(公告)日：2023-09-14

    申请号：US18199127

    申请日：2023-05-18

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Di WU ,  Siyuan Huang ,  Ye Sun ,  Lei LI ,  Lie Zou

    IPC: G06T3/00 ,  G06T3/60 ,  G06T3/40

    CPC classification number: G06T3/0093 ,  G06T3/60 ,  G06T3/40

    Abstract: Disclosed herein are an image morphing adaption method and apparatus. The method includes, during a rotation process of a display device, processing, by rotating and then clipping, an image to be displayed at at least one morphing adaption processing time, and displaying the processed image at the at least one morphing adaption processing time. A rotation angle of the image is substantially equal to a rotation angle of the display device at the corresponding time, a rotation direction of the image is opposite to a rotation direction of the display device, the morphing adaption processing time is determined based on a preset morphing adaption processing frequency, and the morphing adaption processing frequency is determined by down-conversion based on a current screen refresh frequency. According to such a method, images displayed by the display device during the rotation process substantially match the view angle of users, improving a viewing experience.

    COORDINATED LOAD BALANCING IN MOBILE EDGE COMPUTING NETWORK
    10.
    发明公开
    COORDINATED LOAD BALANCING IN MOBILE EDGE COMPUTING NETWORK 审中-公开

    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.

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