Generating and calibrating signal strength prediction in a wireless network

    公开(公告)号:US11528620B2

    公开(公告)日:2022-12-13

    申请号:US17393329

    申请日:2021-08-03

    Abstract: A core network entity (CNE) can predict received signal strength values for base station (BS). In response to a triggering event, the CNE fetches information on BS configuration parameters, including at least one of: a BS class, a BS location, a BS height, an orientation of the BS, a BS antenna pattern, and topographical details surrounding the BS. The BS obtains, processes and forwards to the CNE, measurement reports created by a user equipment (UE) including a signal strength value and a location of the UE. The CNE pools the measurement reports based on the BS class and, in response to another triggering event, recalibrates signal strength prediction tools, which can predict received signal strength values from the BS to a location in a vicinity of the BSs. The CNE also pools and stores the measurement reports and corresponding BS configuration parameters, after post processing and compression.

    LOW COMPLEXITY ML AUGMENTED ROBUST CHANNEL ESTIMATION

    公开(公告)号:US20250158850A1

    公开(公告)日:2025-05-15

    申请号:US18930930

    申请日:2024-10-29

    Abstract: A method includes acquiring, by a processor of an electronic device, information associated with channel and noise covariances. The method includes determining one or more time-domain rectangular filters based on the information associated with the channel and noise covariances. The method includes generating one or more convolutional kernels based on the one or more rectangular filters applied to the channel and noise covariances in a time-domain. The method includes generating a codebook based on the one or more convolutional kernels, the codebook comprising N codewords. Further, the method can include establishing a communication link to a gNB configured to: receive a reference signal from a user equipment; receive the codebook; calculate channel statistics using a low complexity algorithm; execute a decision tree classifier to select a codeword from the codebook stored in memory of the gNB; and apply the selected codeword as convolution kernel for channel estimation.

    INTELLIGENT PROXIMITY SYSTEM
    5.
    发明申请

    公开(公告)号:US20250071508A1

    公开(公告)日:2025-02-27

    申请号:US18614620

    申请日:2024-03-22

    Abstract: An electronic device includes a transceiver configured to receive information related to an action request or rule. The electronic device further includes a processor operatively coupled to the transceiver. The processor is configured to determine that the action is location based, and identify a zone within which the electronic device is located. The identification of the zone is based on at least one of a first reception by the transceiver of at least one signal indicative of a location of the electronic device, and a second reception by the transceiver of at least one response to a transmission of a signal indicative of a location of the electronic device. The processor is further configured to trigger an action based on the action request or rule and the identified zone.

    PARAMETER OPTIMIZATION IN CELLULAR NETWORKS
    9.
    发明公开

    公开(公告)号:US20240334203A1

    公开(公告)日:2024-10-03

    申请号:US18408462

    申请日:2024-01-09

    CPC classification number: H04W24/02

    Abstract: A method includes partitioning a set of configuration management (CM) data for one or more cellular network devices into multiple distinct time intervals. The method also includes determining one or more temporal points of interest in each time interval based on whether CM changes exist during that time interval. The method also includes, for each temporal point of interest in each time interval, identifying a first set of data samples before that temporal point of interest and a second set of data samples after that temporal point of interest, and averaging features and a target key performance indicator (KPI) in the first set of data samples and in the second set of data samples. The method also includes performing regression analysis to determine an impact of the features on the target KPI.

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