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US Patent 11005697 Orthogonal frequency-division multiplexing equalization using deep neural network

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Is a
Patent
Patent
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Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
110056970
Patent Inventor Names
Robert Edward Liston0
John George Apostolopoulos0
Date of Patent
May 11, 2021
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Patent Application Number
165589420
Date Filed
September 3, 2019
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Patent Citations
‌
US Patent 10217346 Presence detection with neural networks
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US Patent 10305766 Coexistence-insensitive presence detection
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US Patent 10217047 Learning and deployment of adaptive wireless communications
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US Patent 10305553 Encoding and decoding of information for wireless transmission using multi-antenna transceivers
Patent Citations Received
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US Patent 11940889 Combined TDECQ measurement and transmitter tuning using machine learning
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US Patent 11907090 Machine learning for taps to accelerate TDECQ and other measurements
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US Patent 11923896 Optical transceiver tuning using machine learning
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US Patent 11923895 Optical transmitter tuning using machine learning and reference parameters
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Patent Primary Examiner
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Gbemileke J Onamuti
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Patent 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.

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