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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 KNOWLEDGE DISTILLATION, AND AN APPARATUS FOR THE SAME
    2.
    发明申请
    METHOD OF LOAD FORECASTING VIA KNOWLEDGE DISTILLATION, AND AN APPARATUS FOR THE SAME 有权

    公开(公告)号：US20230102489A1

    公开(公告)日：2023-03-30

    申请号：US17902626

    申请日：2022-09-02

    Applicant: SAMSUNG ELECTRONICS CO., LTD.

    Inventor： Chengming HU ,  Xi CHEN ,  Amal FERIANI ,  Ju WANG ,  Jikun KANG ,  Xue LIU ,  Gregory Lewis DUDEK ,  Seowoo JANG

    IPC: H04W28/16 ,  G06N20/00 ,  H04W24/02 ,  H04L41/16

    Abstract: A server may obtain teacher artificial intelligence (AI) models from source base stations; obtain target traffic data from a target base station; obtain an integrated teacher prediction based on the target traffic data by integrating teacher prediction results of the teacher AI models based on teacher importance weights; obtain a student AI model that is trained to converge a student loss on the target traffic data; update the teacher importance weights to converge a teacher loss between a student prediction of the student AI model on the target traffic data, and the integrated teacher prediction of the teacher AI models on the target traffic data; update the student AI model based on the updated teacher importance weights being applied to the teacher prediction results of the teacher AI models; and predict a communication traffic load of the target base station using the updated student AI model.

    MILLIMETER-WAVE BEAM ALIGNMENT ASSISTED BY ULTRA WIDE BAND (UWB) RADIO
    3.
    发明申请
    MILLIMETER-WAVE BEAM ALIGNMENT ASSISTED BY ULTRA WIDE BAND (UWB) RADIO 有权

    公开(公告)号：US20220116088A1

    公开(公告)日：2022-04-14

    申请号：US17410091

    申请日：2021-08-24

    Applicant: SAMSUNG ELECTRONICS CO., LTD.

    Inventor： Ju WANG ,  Xi CHEN ,  Xue LIU ,  Gregory Lewis DUDEK

    IPC: H04B7/06 ,  H04B17/318

    Abstract: A first device and second device communicate using mmWave communication with antenna alignment based on processing of ultra wide band (UWB) pulses. A limit on angle resolution due to a small number of antennas on either of the devices is relieved by using two or more carrier frequencies in the UWB pulses. A limit on angle resolution is further overcome in some situations by use of a neural network to refine angle estimates. In some situations, received power values are further used to select an angle for beam alignment.

    METHOD OF SHORT-TERM LOAD FORECASTING VIA ACTIVE DEEP MULTI-TASK LEARNING, AND AN APPARATUS FOR THE SAME
    6.
    发明申请
    METHOD OF SHORT-TERM LOAD FORECASTING VIA ACTIVE DEEP MULTI-TASK LEARNING, AND AN APPARATUS FOR THE SAME 有权

    公开(公告)号：US20220207357A1

    公开(公告)日：2022-06-30

    申请号：US17359919

    申请日：2021-06-28

    Applicant: SAMSUNG ELECTRONICS CO., LTD.

    Inventor： Di WU ,  Michael JENKIN ,  Xue LIU ,  Gregory Lewis DUDEK

    IPC: G06N3/08 ,  G06N3/04

    Abstract: A method of load forecasting using multi-task deep learning includes obtaining references data corresponding to commodity consuming objects, clustering the commodity consuming objects into clusters based on the obtained reference commodity consumption data; obtaining cluster models based on: reference commodity consumption data, reference environmental data, and reference calendar data; inputting, into the cluster models, present data corresponding to the commodity consuming objects; and predicting, based on an output of the cluster models, a future commodity consumption for the commodity consuming objects. The cluster models include multi-task learning processes having joint loss functions.

    HIERARCHICAL POLICY LEARNING FOR HYBRID COMMUNICATION LOAD BALANCING
    7.
    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.

    PROBABILISTIC MOBILITY LOAD BALANCING IN MULTI-BAND MOBILE COMMUNICATION NETWORK
    8.
    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
    9.
    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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