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1.发明公开公开(公告)号：US20230368584A1
公开(公告)日：2023-11-16
申请号：US18029886
申请日：2020-10-06
Applicant: LG ELECTRONICS INC.
Inventor： Ikjoo JUNG , Sangrim LEE , JaYeong KIM , Hojae LEE , Yeongjun KIM
CPC classification number: G07C5/008 , H04W72/25 , B60W60/001
Abstract: A method for performing reinforcement learning by a V2X communication device in an autonomous driving system, specifically, a method for performing reinforcement learning in consideration of an application rate of a reward according to age in terms of the freshness of a reward for an action, is proposed. An agent transmits an action message and controls a reflection rate of a reward through AoI management for a reward message, so that rewards transmitted from a plurality of devices are suitably reflected in an environment of a reinforcement learning-based autonomous driving system, and an optimal policy can be found accordingly.
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2.发明公开公开(公告)号：US20230318691A1
公开(公告)日：2023-10-05
申请号：US18022436
申请日：2020-08-19
Applicant: LG ELECTRONICS INC.
Inventor： Kijun JEON , Sangrim LEE , Hojae LEE , Kyungho LEE , Ikjoo JUNG
IPC: H04B7/08 , H04B7/0413 , G06N3/08
CPC classification number: H04B7/0868 , H04B7/0413 , G06N3/08
Abstract: Disclosed is a method for controlling, by a terminal, an operation of a deep neural network in a wireless communication system. The method according to an embodiment of the present disclosure receives a downlink from abase station in a wireless communication system; and preprocesses the downlink on the basis of the result of an operation of a deep neural network of a terminal, wherein at least one reference signal is applied to the downlink while a statistical feature related to noise of the downlink are maintained. The terminal of the present disclosure may be linked to an artificial intelligence module, a drone (unmanned aerial vehicle (UAV)), a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to 6G services, and the like.
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3.发明公开公开(公告)号：US20230318662A1
公开(公告)日：2023-10-05
申请号：US18042107
申请日：2020-08-20
Applicant: LG ELECTRONICS INC.
Inventor： Kijun JEON , Sangrim LEE , Hojae LEE , Ikjoo JUNG , Taehyun LEE
IPC: H04B7/0404 , G06N3/045 , H04B7/0452
CPC classification number: H04B7/0404 , G06N3/045 , H04B7/0452
Abstract: Disclosed is a method by which a terminal controls the calculations of a deep neural network in a wireless communication system. A method according to one embodiment of the present disclosure receives a downlink from a base station of a wireless communication system by using a multi input multi output (MIMO) operation, pre-processes the downlink on the basis of a result of calculations of a deep neural network in a terminal, acquires the number of a plurality of transmission antennas connected to the terminal, acquires the number of a plurality of reception antennas connected to the terminal, and forms a plurality of overlapping neural networks overlapping in the deep neural network, on the basis of a preset number of reference antennas, the number of transmission antennas, and the number of reception antennas. The terminal of the present disclosure can be linked to an artificial intelligence module, a drone (unmanned aerial vehicle (UAV)), a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to 6G services, and the like.
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4.发明公开公开(公告)号：US20240031786A1
公开(公告)日：2024-01-25
申请号：US18025977
申请日：2020-09-15
Applicant: LG ELECTRONICS INC.
Inventor： Ikjoo JUNG , Sangrim LEE , JaYeong KIM , Yeongjun KIM , Sungjin KIM
CPC classification number: H04W4/40 , H04W56/001
Abstract: A method for performing reinforcement learning by a V2X communication device in an autonomous driving system, specifically, a method for performing reinforcement learning in consideration of a reward application ratio over time, is proposed. Action information is transmitted to a second V2X communication device, reward information is received from the second V2X communication device, and reinforcement learning is performed on the basis of a reward, wherein a reward corresponding to a ratio determined by a first V2X communication device is applied to the reinforcement learning, the ratio is determined on the basis of a time interval from a time point of transmission of the action information to a time point of reception of the reward information, and the ratio is between 0 and 1, both inclusive.
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公开(公告)号：US20220393781A1
公开(公告)日：2022-12-08
申请号：US17776510
申请日：2020-07-09
Applicant: LG ELECTRONICS INC.
Inventor： Ilhwan KIM , Jong Ku LEE , Ikjoo JUNG , Sung Ryong HONG
IPC: H04B17/373 , H04W8/02 , G06N3/08 , G06N3/04
Abstract: The present disclosure relates to a method for operating a terminal and a base station in a wireless communication system and an apparatus for supporting the same. In an embodiment of the present disclosure, a method for operating a terminal in a wireless communication system may include: transmitting a first message including information related to learning; receiving a second message including configuration information for learning; transmitting an uplink reference signal; and transmitting channel information related to a downlink channel measured based on a downlink reference signal.
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Patent Agency Ranking
公开(公告)号：US20210297178A1
公开(公告)日：2021-09-23
申请号：US17035953
申请日：2020-09-29
Applicant: LG ELECTRONICS INC.
Inventor： Sunam KIM , ILHWAN KIM , Jongku LEE , Ikjoo JUNG
Abstract: Provided is a method for transmitting or receiving data, by a user equipment (UE), to or from a base station (BS). The method includes transmitting, by the UE, capability information of the UE to the BS, wherein the capability information includes information related to artificial intelligence (AI) calculation for the data transmission or reception, receiving, by the UE, at least one of a plurality of AI parameters from the BS, and applying the at least one AI parameter to an encoding process for the data transmission or a decoding process for the data reception, wherein the encoding process or the decoding process is performed by information on a network structure in the at least one AI parameter, and wherein the at least one AI parameter comprises a plurality of information for performing the encoding process or the decoding process by the network structure.
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7.发明申请公开(公告)号：US20250023609A1
公开(公告)日：2025-01-16
申请号：US18711460
申请日：2022-11-07
Applicant: LG ELECTRONICS INC.
Inventor： Kijun JEON , Sangrim LEE , Ikjoo JUNG , Hojae LEE , Taehyun LEE
Abstract: The present specification provides a method by which a terminal performs federated learning with a plurality of terminals in a wireless communication system. More specifically, the method performed by one terminal comprises the steps of: receiving, from a server, a channel state information reference signal (CSI-RS); transmitting, to the server, channel state information (CSI) calculated on the basis of the CSI-RS; receiving, from the server, (i) information about a global parameter for the federated learning and (ii) compression state information for determining a weight compression method of the one terminal on the basis of channel state information of each of channels between the server and the plurality of terminals; determining a weight compression scheme based on (i) a difference value between the global parameter and a global parameter received before receiving the global parameter and (ii) the compression state information; and transmitting, to the server, an updated local parameter on the basis of the determined weight compression scheme.
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8.发明申请公开(公告)号：US20250016619A1
公开(公告)日：2025-01-09
申请号：US18712453
申请日：2022-11-07
Applicant: LG ELECTRONICS INC.
Inventor： Kijun JEON , Sangrim LEE , Ikjoo JUNG , Hojae LEE , Taehyun LEE
Abstract: The present disclosure provides a method for one user equipment (UE) to perform federated learning with a plurality of UEs in a wireless communication system. More specifically, the method performed by the one UE comprises receiving, from a server, a channel state information reference signal (CSI-RS); transmitting, to the server, channel state information (CSI) calculated based on the CSI-RS; receiving, from the server, compression state information for determining a weight compression method of the one UE based on (i) information on a global parameter for the federated learning and (ii) channel state information of each of channels between the server and the plurality of UEs; determining the weight compression method based on (i) a difference between the global parameter and a global parameter received before a reception of the global parameter and (ii) the compression state information; and transmitting, to the server, a local parameter updated based on the determined weight compression method.
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公开(公告)号：US20250007582A1
公开(公告)日：2025-01-02
申请号：US18697654
申请日：2021-10-01
Applicant: LG Electronics Inc.
Inventor： Minseok JO , Sangrim LEE , Bonghoe KIM , Ikjoo JUNG , Hojae LEE
Abstract: The present disclosure provides a method for reporting, by a terminal, channel state information (CSI) in a wireless communication system. More specifically, the method includes: receiving, from a base station, a pilot signal related to calculation of a quantization rule, in which the quantization rule is determined based on an empirical distribution of an encoder neural network output of the terminal; transmitting, to the base station, quantization rule information related to the quantization rule calculated based on the pilot signal; and receiving, from the base station, information on a gradient calculated based on the quantization rule information, and the quantization rule information includes information on an empirically calculated variance with respect to the empirical distribution of the encoder neural network output.
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公开(公告)号：US20240414746A1
公开(公告)日：2024-12-12
申请号：US18702691
申请日：2022-10-17
Applicant: LG ELECTRONICS INC.
Inventor： Taehyun LEE , Sangrim LEE , Kijun JEON , Ikjoo JUNG , Yeongjun KIM
Abstract: The present disclosure provides a device and method for performing, based on channel information, a device grouping for federated learning based AirCOMP of a non-IID data environment in a communication system. The present disclosure also provides a device and method for performing effective federated learning in a non-IID environment including multiple devices. The present disclosure also provides a device and method for performing a device grouping in consideration of channel environment factors in order to apply AirComp based federated learning to a real communication environment.
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