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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.

    HIERARCHICAL POLICY LEARNING FOR HYBRID COMMUNICATION LOAD BALANCING
    2.
    发明申请
    HIERARCHICAL POLICY LEARNING FOR HYBRID COMMUNICATION LOAD BALANCING 有权

    公开(公告)号：US20220150786A1

    公开(公告)日：2022-05-12

    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 ,  H04W28/08 ,  H04W24/02 ,  G06N3/08 ,  G06N3/04

    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.

    ENERGY SAVING IN CELLULAR WIRELESS NETWORKS VIA TRANSFER DEEP REINFORCEMENT LEARNING
    3.
    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.

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