Data-Driven Signal Separation in Radio Spectrum: ICASSP 2024

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Data-Driven Signal Separation in Radio Spectrum: ICASSP 2024


This challenge will require developing an engine for signal separation  of radio-frequency (RF) waveforms. At inference time, a superposition of a signal of interest (SOI) and an interfering signal will be fed to the engine, which should recover the SOI by performing a sophisticated interference cancellation. SOI is a digital communication signal whose complete description is available (modulation, pulse-shape, timing, frequency, etc). However, the structure of the interference will need to be learned from data. We expect successful contributions to adapt existing machine learning (ML) methods and/or propose new ones from the areas of generative modeling, variational auto-encoders, U-Nets and others.

Visit the Challenge website for details and more information!


Technical Committee: Machine Learning for Signal Processing, Signal Processing for Communications and Networking

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