AI-AUGMENTED CHANNEL ESTIMATION
    1.
    发明公开

    公开(公告)号:US20230388158A1

    公开(公告)日:2023-11-30

    申请号:US18183114

    申请日:2023-03-13

    CPC classification number: H04L25/0254 H04L25/0256 H04L25/0204

    Abstract: A method includes determining estimated features comprising second order statistics based on at least one received signal. The method also includes classifying, using a machine learning network, each channel of the at least one received signal into a channel profile based on the estimated features. The method also includes obtaining multiple minimum mean square error (MMSE) channel estimation weights from a database based on the estimated features, the database storing (i) representative MMSE estimation weights and (ii) channel cluster representatives indexed by the estimated features. The method also includes applying a respective MMSE channel estimation weight for each channel.

    METHOD AND APPARATUS FOR BANDWIDTH-ADAPTIVE AND MODEL-ORDER-ADAPTIVE CHANNEL PREDICTION

    公开(公告)号:US20220278762A1

    公开(公告)日:2022-09-01

    申请号:US17541160

    申请日:2021-12-02

    Abstract: Methods and apparatuses for a channel estimation and prediction operation in a wireless communication systems. A method of a BS comprises: receiving an SRS; partitioning, based on a partition policy, a frequency band of the SRS into sub-bandwidths in a frequency domain; generating, based on previously stored CSI in memory and the partition policy, a set of chunks corresponding to respective sub-bandwidths; performing CHPD operations corresponding to the respective sub-bandwidths to generate channel parameters, wherein different CHPD operations are applied to the respective sub-bandwidths; combining the channel parameters predicted from the respective sub-bandwidths in the frequency domain; and performing, based on the combined channel parameters, a channel estimation and prediction 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.

    Codebook for AI-assisted channel estimation

    公开(公告)号:US12250035B2

    公开(公告)日:2025-03-11

    申请号:US18365874

    申请日:2023-08-04

    Abstract: A method includes identifying ACF information by: obtaining channel information including multiple channels of expected operation scenarios; and based on the channel information for each of the channels, determining MMSE channel estimation (CE) weights expressed in a form of ACFs and an SNR, and covariance matrices. The method includes clustering the MMSE CE weights into K clusters. A center ACF weight of each of the K clusters represents a codeword. The method includes determining a distance metric based on a cluster distance after a re-clustering. The method includes, in response to a determination that cluster distances before and after the clustering differ from each other by a non-negligibly, iteratively re-clustering the ACF information thereby updating the center ACF weights and cluster distances. The method includes generating the codebook to include an index k of each of the K clusters and the center ACF weight of each of the K clusters.

    CONTROL FOR MOBILITY CHANNEL PREDICTION

    公开(公告)号:US20210219161A1

    公开(公告)日:2021-07-15

    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.

    TRANSMISSION MODE ADAPTATION IN NEW RADIO WITH AI/ML ASSISTANCE

    公开(公告)号:US20240063931A1

    公开(公告)日:2024-02-22

    申请号:US18363670

    申请日:2023-08-01

    CPC classification number: H04B17/3913 H04B7/0417 H04B7/0626

    Abstract: Apparatuses and methods for transmission mode adaptation in New Radio (NR) with AI/ML assistance. A base station includes a transceiver configured to receive a set of input metrics. The set of input metrics comprises at least one metric derived from a channel state information (CSI) report. The base station further includes a processor operably coupled to the transceiver, the processor configured to determine, based on the set of input metrics, a first multiple-input multiple-output (MIMO) mode throughput prediction and a second MIMO mode throughput prediction, generate, based on the first MIMO mode throughput prediction, a predicted first MIMO mode throughput result, generate, based on the second MIMO mode throughput prediction, a predicted second MIMO mode throughput result, and select a MIMO mode based on the predicted first MIMO mode throughput result and the predicted second MIMO mode throughput result.

    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.

    CODEBOOK FOR AI-ASSISTED CHANNEL ESTIMATION
    10.
    发明公开

    公开(公告)号:US20240056138A1

    公开(公告)日:2024-02-15

    申请号:US18365874

    申请日:2023-08-04

    CPC classification number: H04B7/0456 H04L25/0204 H04L25/0256

    Abstract: A method includes identifying ACF information by: obtaining channel information including multiple channels of expected operation scenarios; and based on the channel information for each of the channels, determining MMSE channel estimation (CE) weights expressed in a form of ACFs and an SNR, and covariance matrices. The method includes clustering the MMSE CE weights into K clusters. A center ACF weight of each of the K clusters represents a codeword. The method includes determining a distance metric based on a cluster distance after a re-clustering. The method includes, in response to a determination that cluster distances before and after the clustering differ from each other by a non-negligibly, iteratively re-clustering the ACF information thereby updating the center ACF weights and cluster distances. The method includes generating the codebook to include an index k of each of the K clusters and the center ACF weight of each of the K clusters.

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