New Patent for “Real-time cognitive wireless networking through deep learning in transmission and reception communication paths”

Professors Francesco Restuccia and Tommaso Melodia were recently awarded a patent for their work on “Real-time cognitive wireless networking through deep learning in transmission and reception communication paths.”

Their definitions describe the equipment and methods required to implement spectrum-driven embedded wireless networks utilizing deep learning in real time.

Patent Abstract

“Apparatuses and methods for real-time spectrum-driven embedded wireless networking through deep learning are provided. Radio frequency, optical, or acoustic communication apparatus include a programmable logic system having a front-end configuration core, a learning core, and a learning actuation core. The learning core includes a deep learning neural network that receives and processes input in-phase/quadrature (I/Q) input samples through the neural network layers to extract RF, optical, or acoustic spectrum information. A processing system having a learning controller module controls operations of the learning core and the learning actuation core. The processing system and the programmable logic system are operable to configure one or more communication and networking parameters for transmission via the transceiver in response to extracted spectrum information.”

Source: uspto.gov

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