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公开(公告)号:US20240323727A1
公开(公告)日:2024-09-26
申请号:US18186775
申请日:2023-03-20
Applicant: QUALCOMM Incorporated
Inventor: Weimin DUAN , Krishna Kiran MUKKAVILLI , Hyojin LEE
IPC: H04W24/10 , G01S5/00 , H04W72/044 , H04W72/21
CPC classification number: H04W24/10 , G01S5/0036 , H04W72/044 , H04W72/21 , G01S7/006 , G01S13/003 , G01S2205/008
Abstract: Disclosed are systems, apparatuses, processes, and computer-readable media for wireless communications. For example, a network device can receive one or more radio frequency (RF) sensing resources. The network device can determine RF sensing measurements based on the one or more RF sensing resources. For instance, the RF sensing measurements may be based on at least one use case for RF sensing. The network device can transmit, to a network entity, a measurement report comprising the RF sensing measurements for the at least one use case for the RF sensing.
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52.
公开(公告)号:US20230422301A1
公开(公告)日:2023-12-28
申请号:US17808253
申请日:2022-06-22
Applicant: QUALCOMM Incorporated
Inventor: Weimin DUAN , Yu ZHANG , Jing JIANG , Hyojin LEE , Seyong PARK
CPC classification number: H04W74/0816 , H04L5/0048 , H04W72/1242 , G01S7/023
Abstract: Disclosed are techniques for wireless sensing. In an aspect, a network node detects a collision between at least one wireless communication signal scheduled to be transmitted by the network node and two or more bundled radar reference signals scheduled to be transmitted by the network node, wherein the at least one wireless communication signal is scheduled to be transmitted during a gap between a first radar reference signal and a second radar reference signal of the two or more bundled radar reference signals; performs a collision avoidance operation based on detection of the collision, and transmits the two or more bundled radar reference signals.
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公开(公告)号:US20230422278A1
公开(公告)日:2023-12-28
申请号:US17809107
申请日:2022-06-27
Applicant: QUALCOMM Incorporated
Inventor: Weimin DUAN , June NAMGOONG , Hyojin LEE
CPC classification number: H04W72/048 , H04W72/0453 , H04L5/0051 , H04L25/0204
Abstract: Disclosed are techniques for wireless communication. In an aspect, a user equipment (UE) applies a plurality of positioning reference signal (PRS) processing windows to a PRS resource received from a network node over a multipath channel, determines a plurality of channel estimates for the PRS resource based on the plurality of PRS processing windows, and determines a plurality of positioning measurements of the PRS resource based on the plurality of channel estimates.
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公开(公告)号:US20230337297A1
公开(公告)日:2023-10-19
申请号:US17659249
申请日:2022-04-14
Applicant: QUALCOMM Incorporated
Inventor: Hyojin LEE , Weimin DUAN , Seyong PARK
Abstract: Certain aspects of the present disclosure provide techniques for adaptive grouping for cooperative radar sensing. A method for wireless communications by a first network entity includes receiving, from a second network entity, first grouping information comprising an indication of a first plurality of network entities, the first grouping information associated with a first time-period; receiving, from the second network entity, second grouping information comprising an indication of a second plurality of network entities, the second grouping information associated with a second time-period; receiving a first radar signal during the first time-period; receiving a second radar signal during the second time-period; transmitting, to the second network entity, first sensing information based on the first radar signal; and transmitting, to the second network entity, second sensing information based on the second radar signal.
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55.
公开(公告)号:US20230114870A1
公开(公告)日:2023-04-13
申请号:US17498651
申请日:2021-10-11
Applicant: QUALCOMM Incorporated
Inventor: June NAMGOONG , Taesang YOO , Hyojin LEE , Naga BHUSHAN , Weiliang ZENG
Abstract: A method of wireless communication by a user equipment (UE) includes receiving different sets of parameters from different sources as input to a receiver neural network. The method also includes receiving, from a base station, a set of target long-term energy values associated with the receiver neural network. The method further includes calculating a scaling factor for each of the different sets of parameters based on the set of target long-term energy values, and separately scaling each of the different sets of parameters based on the scaling factor calculated for that set in order to generate multiple sets of scaled parameters. The method still further includes transmitting the multiple sets of scaled parameters to the receiver neural network.
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