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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
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.
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2.发明公开公开(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.
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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.
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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
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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公开(公告)号：US20240310162A1
公开(公告)日：2024-09-19
申请号：US18378447
申请日：2023-10-10
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Kaleem Siddiqi , Affan Jilani , Charlotte Morissette , Francois Hogan , Michael Jenkin , Gregory Lewis Dudek
Abstract: A method performed by an optical tactile sensor comprising a camera, includes: measuring a plurality of relations between the optical tactile sensor and an object; detecting a contact of a membrane on the object; detecting a plurality of markers on the membrane; performing a plurality of first operations by using a tactile perception module; and performing a second operation by using a three-dimensional (3D) perception module, based on a result of the performing of the plurality of first operations.
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Patent Agency Ranking
公开(公告)号：US20240198517A1
公开(公告)日：2024-06-20
申请号：US18385696
申请日：2023-10-31
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Sahand REZAEI-SHOSHTARI , David Meger , Francois Robert Hogan , Gregory Lewis Dudek , Charlotte Morissette
CPC classification number: B25J9/163 , B25J9/161 , B25J9/1664 , B25J13/003
Abstract: Provided is a method for training a hypernetwork to provide a policy for use on a previously-unseen task. The hypernetwork may be trained at a robot factory and then shipped with a robot. At the point of deployment, the robot may be given a context for the previously-unseen task. The robot then uses the context and the hypernetwork to create a policy for performing the previously-unseen task. The policy represents an artificial intelligence machine generated for the previously-unseen task.
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公开(公告)号：US20240129048A1
公开(公告)日：2024-04-18
申请号：US17965360
申请日：2022-10-13
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Ju Wang , Xue Liu , Gregory Lewis Dudek
IPC: H04B17/318 , H04W72/00
CPC classification number: H04B17/318 , H04W72/005
Abstract: The present disclosure provides methods, apparatuses, and computer-readable mediums for performing ultra-wideband (UWB) remote control. In some embodiments, the method includes broadcasting an initial control request. The method further includes receiving, from one or more remote devices, at least one reply message comprising identification information and power spectrum information. The method further includes estimating, for each of the one or more remote devices, an angle indicating a pointing direction to that remote device relative to the remote control device. The method further includes determining a selected remote device that is being pointed at by the remote control device. The method further includes sending, to the one or more remote devices, a control signal comprising the identification information of the selected remote device and a control message indicating an action to be performed by the selected remote device.
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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.
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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.
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10.发明申请公开(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.
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