Machine Learning for Accelerated IBFD Tuning

A network device includes a transceiver configured to concurrently transmit signals and receive signals within a single frequency band resulting in radio-frequency signal interference. The device includes an analog canceler configured to mitigate the signal interference. The device includes a neural network that receives data that describes characteristics of the signal interference and provides coefficients for the analog canceler as outputs. The neural network-generated coefficients are applied to the analog canceler which uses them to cancel the signal interference.

Researchers

Kenneth Kolodziej / Bradley Perry / Aidan Cookson

Departments: Lincoln Laboratory
Technology Areas: Artificial Intelligence (AI) and Machine Learning (ML) / Communication Systems: Wireless / Computer Science: Networking & Signals

  • methods and apparatus for analog canceler tuning using neural networks
    United States of America | Granted | 11,626,966

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