ICASSP 2024: Data-Driven Signal Separation in Radio Spectrum

ICASSP 2024 Challenge: Data-Driven Signal Separation in Radio Spectrum

Join “ICASSP 2024: Data-Driven Signal Separation in Radio Spectrum” until Dec 1, 2023.

Discover why this challenge matters:

In the world of Machine Learning (ML) and Artificial Intelligence (AI), challenges like MNIST, ImageNet, VAST, and HPC have driven remarkable advancements.

However, radio-frequency (RF) signal processing, including detection, identification, and geolocation, has received less attention. That’s where the RFChallenge at MIT comes in.

As part of the USAF-MIT AI ACCELERATOR, we aim to inspire the RF community to create AI-driven solutions. Let’s explore contemporary ideas and novel ML techniques to enhance spectral awareness and tackle interference rejection challenges.

Learn more about this exciting challenge here: https://rfchallenge.mit.edu/icassp24-single-channel/

This work was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 101024432.