@inproceedings{67404397122e4ad5a75d3f27a37100c5,
title = "The Data-Driven Radio Frequency Signal Separation Challenge",
abstract = "The radio-frequency (RF) signal separation challenge involves recovering a signal-of-interest (SOI) from a super-imposed co-channel interference signal. The SOI is a digital communication waveform of known modulation, pulse-shape, timing, etc. The interferer is unknown and must be learned from data. Submissions featured a blend of signal processing strategies, leveraging RF-specific domain knowledge and novel neural network architectures with careful hyperparameter selection/optimization. The resulting solutions establish new benchmarks for data-driven RF modeling and interference cancellation.",
keywords = "Source separation, interference rejection, machine learning, wireless communication",
author = "Tejas Jayashankar and Binoy Kurien and Alejandro Lancho and Lee, {Gary C.F.} and Yury Polyanskiy and Amir Weiss and Wornell, {Gregory W.}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 49th IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW 2024 ; Conference date: 14-04-2024 Through 19-04-2024",
year = "2024",
doi = "10.1109/icasspw62465.2024.10627554",
language = "الإنجليزيّة",
series = "2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW 2024 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "53--54",
booktitle = "2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW 2024 - Proceedings",
address = "الولايات المتّحدة",
}