REPORTING POTENTIAL VIRTUAL ANCHOR LOCATIONS FOR IMPROVED POSITIONING

    公开(公告)号:WO2023288161A1

    公开(公告)日:2023-01-19

    申请号:PCT/US2022/072851

    申请日:2022-06-09

    Abstract: Disclosed are techniques for positioning. In an aspect, a positioning entity determines a first time of flight (ToF) of a first non-line-of-sight (NLOS) multipath component of a first radio frequency (RF) signal transmitted by a physical transmission-reception point (TRP) to at least a first user equipment (UE) (1410), determines a location of a virtual TRP associated with the physical TRP based at least on the first ToF (1420), and determines a location of at least a second UE based, at least in part, on the location of the virtual TRP (1430).

    TECHNIQUES FOR REPORTING CHANNEL STATE INFORMATION

    公开(公告)号:WO2022183471A1

    公开(公告)日:2022-09-09

    申请号:PCT/CN2021/079253

    申请日:2021-03-05

    Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive a first indication of a first number of antenna ports for which the UE is to report channel state information (CSI), and a second indication of a second number of antenna ports on which the UE is to measure CSI reference signals (CSI-RSs). The second number may be less than the first number. The UE may receive an indication of one or more antenna port parameters, where each may be associated with one of the first number of antenna ports or the second number of antenna ports. The UE may determine the CSI for the first number of antenna ports using the one or more antenna port parameters and measurements made by the UE on the second number of ports, and may transmit a report including the CSI for the first number of ports.

    MODEL DISCOVERY AND SELECTION FOR COOPERATIVE MACHINE LEARNING IN CELLULAR NETWORKS

    公开(公告)号:WO2022119630A1

    公开(公告)日:2022-06-09

    申请号:PCT/US2021/053414

    申请日:2021-10-04

    Abstract: An OAM core network may receive a request for a ML/NN model and features associated with a ML/NN procedure. The OAM core network may determine a latest update to the ML/NN model and features based on the request and generate a response to the request indicative of the latest update to the ML/NN model and features. In aspects, a base station may initiate the request for the ML/NN model and features by transmitting the request for the ML/NN model and features to the OAM core network. The base station may receive the generated response of the OAM core network based on the transmitted request. In further aspects, a UE may initiate the request for the ML/NN model and features by transmitting the request to the base station, where the UE may receive the ML/NN model and features from the base station based on the transmitted request.

    POSITION ESTIMATION USING SIGNALING BETWEEN USER EQUIPMENT DEVICES

    公开(公告)号:WO2022086632A1

    公开(公告)日:2022-04-28

    申请号:PCT/US2021/048543

    申请日:2021-08-31

    Abstract: Methods, devices, systems, and computer-readable media for supporting position estimation are disclosed. A method at a first user equipment (UE) device comprises sending or receiving, via a sidelink channel, a ranging signal between the first UE device and a second UE device. The method further comprises obtaining at least one ranging measurement based on the sending or receiving the ranging signal between the first UE device and a second UE device. The method further comprises broadcasting, via the sidelink channel (1) the at least one ranging measurement with a reference to the second UE device or (2) a position estimate for the first UE device based on the at least one ranging measurement.

    PAPR REDUCTION BASED ON PEAK REDUCTION TONES BY USING NEURAL NETWORKS

    公开(公告)号:WO2022046390A1

    公开(公告)日:2022-03-03

    申请号:PCT/US2021/044936

    申请日:2021-08-06

    Abstract: Various embodiments include methods for reducing peak to average power ratio of wireless transmission waveforms, performed in transmitter circuitry of a wireless communication. Various embodiments may include receiving frequency domain data tones, transforming the frequency domain data tones to time domain data signals, generating, using a set of peak reduction tone (PRT) neural networks, time domain PRTs using the time domain data signals in which the set of PRT neural networks have been trained in conjunction with an augmentation neural network, and a receiver neural network, generating an output of the augmentation neural network based on an input of final combined time domain signals including the time domain PRTs combined with previous combined time domain signals, and generating time domain wireless transmission waveforms that include the output of the augmentation neural network combined with the final combined time domain signals.

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