SELF-TUNING FIXED-POINT LEAST-SQUARES SOLVER

    公开(公告)号:US20230412428A1

    公开(公告)日:2023-12-21

    申请号:US18310422

    申请日:2023-05-01

    CPC classification number: H04L25/024

    Abstract: A method and device for self-tuning scales of variables for processing in fixed-point hardware. The device includes a sequence of fixed-point arithmetic circuits configured to receive at least one input signal and output at least one output signal. The circuits are preconfigured with control scales associated with each of the input and output signals. A first circuit in the sequence is configured to receive a first input signal having a dynamic true scale that is different from the control scale associated with the first input signal. Each of the circuits is further configured to determine, for each of the output signals, an adaptive scale from the control scale associated with the output signal based on the true scale of the first input signal and the control scale associated with the first input signal, and generate, from the input signal, the output signal having the associated adaptive scale.

    CHANNEL ESTIMATION AND PREDICTION WITH MEASUREMENT IMPAIRMENT

    公开(公告)号:US20210281334A1

    公开(公告)日:2021-09-09

    申请号:US17188934

    申请日:2021-03-01

    Abstract: A base station (UE) is configured to perform a computer-implemented method for antenna fault detection and correction. The computer-implemented method includes acquiring one or more sounding reference signals (SRSs) received from at least one gNB antenna; detecting an antenna failure based on the one or more SRSs; estimating a noise power based on the antenna failure and a history of received SRSs; detecting a missing SRS based on the noise power and the history of received SRSs; and handling the missing SRS. Handling the missing SRS is based on performing at least one of: replacing an SRS measurement with a predicted SRS value for the missing SRS when the predicted SRS is available; or avoiding use of the missing SRS in a sequential SRS prediction when the predicted SRS is unavailable.

    LOW COMPLEXITY MACHINE LEARNING BASED CHANNEL CLASSIFIER

    公开(公告)号:US20230082795A1

    公开(公告)日:2023-03-16

    申请号:US17932274

    申请日:2022-09-14

    Abstract: A method includes storing multiple signals received from a user equipment (UE) in a queue. The method also includes estimating a sounding reference signal (SRS) signal-to-noise-ratio (SNR) and determining a filtered SNR based on the received signals. The method also includes computing one or more features based on the filtered SNR and at least some of the received signals in the queue. The method also includes determining (i) a channel condition of the UE and (ii) a speed range of the UE based on the one or more computed features, wherein the channel condition of the UE comprises line-of-sight (LoS) or non-line-of-sight (NLoS). The method also includes determining a transmission configuration based on the channel condition of the UE and the speed range of the UE.

    SYSTEM AND METHOD FOR USER EQUIPMENT ASSISTED OVER-THE-AIR CALIBRATION

    公开(公告)号:US20230067500A1

    公开(公告)日:2023-03-02

    申请号:US17823491

    申请日:2022-08-30

    Abstract: A method includes receiving, by a base station, a sounding reference signal (SRS) symbol from a user equipment (UE). The method also includes estimating, by the base station, an uplink (UL) channel from the UE to a full dimensional multiple-input multiple-output (FD-MIMO) base station base band based on the received SRS symbol. The method also includes receiving, by the base station from the UE, an estimate of a downlink (DL) channel from the FD-MIMO base station base band to the UE. The method also includes performing, by the base station, a joint calibration by applying one or more calibration algorithms using channel state information (CSI) of the UL channel and the DL channel.

    Control for mobility channel prediction

    公开(公告)号:US11516689B2

    公开(公告)日:2022-11-29

    申请号:US17129797

    申请日:2020-12-21

    Abstract: A method for operating a base station comprises receiving channel information from a plurality of UEs; determining, based on the channel information, one or more UEs on which to base a channel prediction; computing a first set of metrics and a second set of metrics corresponding to the plurality of UEs, wherein computing the first set of metrics has a lower complexity than computing the second set of metrics; performing a selection process on the plurality of UEs based on the first and second set of metrics associated with the plurality of UEs; selecting a first subset of UEs from the plurality of UEs based on a first set of metrics; selecting, from the first subset, a second subset of UEs based on a second set of metrics; and performing the channel prediction based on the second subset of UEs.

    MULTIPLE ANTENNA CHANNEL TRACKING UNDER PRACTICAL IMPAIRMENT

    公开(公告)号:US20220271852A1

    公开(公告)日:2022-08-25

    申请号:US17481048

    申请日:2021-09-21

    Abstract: Methods and apparatuses for a BS in a communication system. The method comprises: identifying antenna groups; identifying channel coefficients for each of the antenna groups to perform a channel tracking and prediction operation; receiving, from a user equipment (UE), an uplink signal to perform the channel tracking and prediction operation; and performing, based at least in part on the received uplink signal, a channel coefficient tracking operation for the channel coefficients of the antenna groups, respectively, the channel coefficient tracking operation including a channel subspace parameter tracking operation and a subspace coefficient tracking operation.

    Self-tuning fixed-point least-squares solver

    公开(公告)号:US12284058B2

    公开(公告)日:2025-04-22

    申请号:US18310422

    申请日:2023-05-01

    Abstract: A method and device for self-tuning scales of variables for processing in fixed-point hardware. The device includes a sequence of fixed-point arithmetic circuits configured to receive at least one input signal and output at least one output signal. The circuits are preconfigured with control scales associated with each of the input and output signals. A first circuit in the sequence is configured to receive a first input signal having a dynamic true scale that is different from the control scale associated with the first input signal. Each of the circuits is further configured to determine, for each of the output signals, an adaptive scale from the control scale associated with the output signal based on the true scale of the first input signal and the control scale associated with the first input signal, and generate, from the input signal, the output signal having the associated adaptive scale.

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