Integrating Deep Learning Algorithms to Enable Real-Time Spectrum-Driven Decision-Making

ECE Assistant Professor Francesco Restuccia and William L. Smith Professor Tommaso Melodia were awarded a patent for “Real-time cognitive wireless networking through deep learning in transmission and reception communication paths.”

Abstract Source: USPTO

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.

Related Faculty: Francesco Restuccia, Tommaso Melodia

Related Departments:Electrical & Computer Engineering