GROUP-COMMON REFERENCE SIGNAL FOR OVER-THE-AIR AGGREGATION IN FEDERATED LEARNING

    公开(公告)号:US20230084883A1

    公开(公告)日:2023-03-16

    申请号:US17898180

    申请日:2022-08-29

    Abstract: Aspects presented herein may enable a network entity to configure a group of UEs to simultaneously transmit reference signals and to simultaneously transmit gradient vectors to the network entity, such that the network entity may receive the gradient vectors from the group of UEs as an aggregated gradient vector over the air. In one aspect, a base transmits, to a group of UEs, a configuration that configures the group of UEs to simultaneously transmit one or more group-common reference signals and to simultaneously transmit one or more gradient vectors associated with a federated learning procedure. The network entity receives, from the group of UEs, the one or more group-common reference signals and the one or more gradient vectors based on the configuration via multiple channels. The network entity calculates an average gradient vector based on the one or more group-common reference signals and the one or more gradient vectors.

    REPORTING POTENTIAL VIRTUAL ANCHOR LOCATIONS FOR IMPROVED POSITIONING

    公开(公告)号:US20230019644A1

    公开(公告)日:2023-01-19

    申请号:US17374694

    申请日:2021-07-13

    Abstract: Disclosed are techniques for wireless positioning. In an aspect, a user equipment (UE) determines a positioning measurement of a first multipath component of a radio frequency (RF) signal transmitted by a transmission-reception point (TRP), determines a first additional positioning measurement of a second multipath component of the RF signal, determines a second additional positioning measurement of a third multipath component of the RF signal, and transmits a measurement report to a location server, the measurement report including at least the positioning measurement, the first additional positioning measurement, the second additional positioning measurement, and one or more parameters associated with the first additional positioning measurement and the second additional positioning measurement.

    ML MODEL TRAINING PROCEDURE
    206.
    发明申请

    公开(公告)号:US20220377844A1

    公开(公告)日:2022-11-24

    申请号:US17323242

    申请日:2021-05-18

    Abstract: This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for an ML model training procedure. A network entity may receive a trigger to activate an ML model training procedure based on at least one of an indication from an ML model repository or a protocol of the network entity. The network entity may transmit an ML model training request to activate the ML model training at one or more nodes. The one or more nodes may be associated with a RAN that may transmit, based on receiving the ML model training request, ML model training results indicative of a trained ML model. In aspects, an apparatus, such as a UE, may train the ML model based on an ML model training configuration received from the RAN, and transmit an ML model training report indicative of the trained ML model.

    NEURAL NETWORK BASED NONLINEAR MU-MIMO PRECODING

    公开(公告)号:US20220263544A1

    公开(公告)日:2022-08-18

    申请号:US17178175

    申请日:2021-02-17

    Abstract: A base station may apply a nonlinear precoding to data for MU-MIMO transmission to a set of paired UEs to generate a first set of precoder symbols, and apply a linear precoding to the first set of precoder symbols to generate a second set of precoder symbols using a linear precoding matrix. The base station may normalize the second set of precoder symbols, and scale the second set of precoder symbols, before transmission of the data, using a scaling factor based on one or more of modulation symbols or a channel matrix. The base station may apply the linear precoding to DMRS associated with the data. The base station may transmit the second set of precoder symbols based on the second set of precoder symbols and the DMRS to the set of paired UEs.

    POWER LEVEL DETERMINATION FOR TRANSMISSION OF REFERENCE SIGNALS

    公开(公告)号:US20220217724A1

    公开(公告)日:2022-07-07

    申请号:US17142182

    申请日:2021-01-05

    Abstract: Disclosed are techniques for determining tone patterns and associated power levels for transmission of reference signals. A tone pattern (e.g., with each tone pattern occupying a resource element in a resource block) can be determined for a reference signal for use in wireless communications between a receiving device and a transmitting device. A plurality of power levels for the tone pattern can be determined. The plurality of power levels can include a respective power level determined for each resource element associated with the tone pattern. One or more of the tone pattern or the plurality of power levels can be used (e.g., transmitted to the transmitting device) for transmission of the reference signal (e.g., from the transmitting device to the receiving device).

    NON-UNIFORM QUANTIZED FEEDBACK IN FEDERATED LEARNING

    公开(公告)号:US20220103221A1

    公开(公告)日:2022-03-31

    申请号:US17448226

    申请日:2021-09-21

    Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a client device may determine a feedback associated with a machine learning component based at least in part on applying the machine learning component. Accordingly, the client device may transmit a quantized value based at least in part on the feedback. The quantized value is determined based at least in part on distances between the feedback and a non-uniform set of quantized digits. Numerous other aspects are provided.

Patent Agency Ranking