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!