Orthogonal frequency-division multiplexing equalization using deep neural network

    公开(公告)号:US11005697B2

    公开(公告)日:2021-05-11

    申请号:US16558942

    申请日:2019-09-03

    Abstract: Orthogonal frequency-division multiplexing (OFDM) equalization using a Deep Neural Network (DNN) may be provided. First, a signal in a packet structure may be received at an OFDM receiver from an OFDM transmitter. The signal may have distortion. Training constellation points, pilot constellation points, and data constellation points may be extracted from the signal based on the packet structure. Each data constellation point may correspond to a data subcarrier within a data symbol of the signal. Next, the training constellation points and the pilot constellation may be provided as input for the data symbol to a DNN. A coefficient for each data subcarrier within the data symbol that reverses the distortion may be received as output from the DNN. Then, the coefficient for each data subcarrier may be applied to the corresponding data constellation point to determine a per subcarrier constellation point prediction.

    OPTIMIZING WIRELESS NETWORKS BY PREDICTING APPLICATION PERFORMANCE WITH SEPARATE NEURAL NETWORK MODELS

    公开(公告)号:US20190205749A1

    公开(公告)日:2019-07-04

    申请号:US15860307

    申请日:2018-01-02

    CPC classification number: G06N3/08 G06N7/005

    Abstract: A network device that is configured to optimize network performance collects a training dataset representing one or more network device states. The network device trains a first model with the training dataset. The first model may be trained to generate one or more fabricated attributes of artificial network traffic through the network device. The network device trains a second model with the training dataset. The second model may be trained to generate a predictive experience metric that represents a predicted performance of an application program of a client device communicating traffic via the network. The network device generates the fabricated attributes based on the training of the first model. The network device generates the predictive experience metric based on the training of the second model and using the one or more fabricated attributes. The network device alters configurations of the network based on the predictive experience metric.

    ORTHOGONAL FREQUENCY-DIVISION MULTIPLEXING EQUALIZATION USING DEEP NEURAL NETWORK

    公开(公告)号:US20210067397A1

    公开(公告)日:2021-03-04

    申请号:US16558942

    申请日:2019-09-03

    Abstract: Orthogonal frequency-division multiplexing (OFDM) equalization using a Deep Neural Network (DNN) may be provided. First, a signal in a packet structure may be received at an OFDM receiver from an OFDM transmitter. The signal may have distortion. Training constellation points, pilot constellation points, and data constellation points may be extracted from the signal based on the packet structure. Each data constellation point may correspond to a data subcarrier within a data symbol of the signal. Next, the training constellation points and the pilot constellation may be provided as input for the data symbol to a DNN. A coefficient for each data subcarrier within the data symbol that reverses the distortion may be received as output from the DNN. Then, the coefficient for each data subcarrier may be applied to the corresponding data constellation point to determine a per subcarrier constellation point prediction.

    Forward predictive precoded MIMO
    5.
    发明授权

    公开(公告)号:US11936453B2

    公开(公告)日:2024-03-19

    申请号:US17165368

    申请日:2021-02-02

    CPC classification number: H04B7/0626 H04B7/0452 H04B7/0456 H04L25/0226

    Abstract: Multi-User Multiple Input, Multiple Output (MU-MIMO) data transmissions are provided with a forward-predictive precoding matrix to mitigate the effects of a change in a state of a communication channel. First and second soundings are performed, at first and second times, to a receive antenna over a channel and, responsive to each of the soundings, first and second Channel State Information (CSI) are received. Based on the first and second CSI, a change in a state of the channel over a time period between the first and second time is determined. Based on the change in the state of the channel, a forward-predictive channel state matrix and/or a forward-predictive precoding matrix are determined that reflect a state of the channel at a future time and that are consistent with the determined change in the state over the time period. The forward-predictive precoding matrix is applied to a data transmission.

    LEARNING-BASED WIRELESS TRANSMISSION PARAMETER ADAPTATION BASED ON CLIENT ACTIVITY DETECTION

    公开(公告)号:US20200287639A1

    公开(公告)日:2020-09-10

    申请号:US16292998

    申请日:2019-03-05

    Abstract: An access point (AP) is configured to transmit packets to a client device over a communication channel. The AP determines a motion indictor indicative of motion of the client device based on a sequence of channel state information measurements, and measures a signal-to-noise ratio (SNR). The AP selects a transmission parameter among candidate transmission parameters using a learning-based algorithm based on observation parameters including the motion indicator, the SNR, and a device identifier for the client device. The AP employs the transmission parameter to transmit packets to the client device, and measures a transmission performance associated with the transmission parameter based on the transmitted packets. The AP updates the learning-based algorithm based on the observation parameters and the transmission performance for a next pass through the selecting, the employing, and the measuring.

    Method and apparatus for tracking assets in one or more optical domains

    公开(公告)号:US09904883B2

    公开(公告)日:2018-02-27

    申请号:US15130239

    申请日:2016-04-15

    CPC classification number: G02B27/32 G06Q10/0833

    Abstract: In one implementation, a method of tracking assets includes obtaining a first image in a first optical domain, where the first optical domain is characterized by a first frequency range. The method also includes detecting a tracking apparatus (e.g., a tag) within the first image in the first optical domain, where a first feature of the tracking apparatus serves as a beacon enabling optical discrimination of the tracking apparatus in the first frequency range. The method further includes determining information associated with the tracking apparatus based on the arrangement of a second feature of the tracking apparatus provided to convey a data set associated with the tracking apparatus.

    Method and Apparatus for Tracking Assets in One or More Optical Domains

    公开(公告)号:US20170300794A1

    公开(公告)日:2017-10-19

    申请号:US15130239

    申请日:2016-04-15

    CPC classification number: G02B27/32 G06Q10/0833

    Abstract: In one implementation, a method of tracking assets includes obtaining a first image in a first optical domain, where the first optical domain is characterized by a first frequency range. The method also includes detecting a tracking apparatus (e.g., a tag) within the first image in the first optical domain, where a first feature of the tracking apparatus serves as a beacon enabling optical discrimination of the tracking apparatus in the first frequency range. The method further includes determining information associated with the tracking apparatus based on the arrangement of a second feature of the tracking apparatus provided to convey a data set associated with the tracking apparatus.

    Learning-based wireless transmission parameter adaptation based on client activity detection

    公开(公告)号:US11070301B2

    公开(公告)日:2021-07-20

    申请号:US16292998

    申请日:2019-03-05

    Abstract: An access point (AP) is configured to transmit packets to a client device over a communication channel. The AP determines a motion indictor indicative of motion of the client device based on a sequence of channel state information measurements, and measures a signal-to-noise ratio (SNR). The AP selects a transmission parameter among candidate transmission parameters using a learning-based algorithm based on observation parameters including the motion indicator, the SNR, and a device identifier for the client device. The AP employs the transmission parameter to transmit packets to the client device, and measures a transmission performance associated with the transmission parameter based on the transmitted packets. The AP updates the learning-based algorithm based on the observation parameters and the transmission performance for a next pass through the selecting, the employing, and the measuring.

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