12 citations · 35 across the 6 of their papers we have counts for
6 papers
Advancing AI Challenges for the United States Department of the Air Force
Christian Prothmann, Vijay Gadepally, Jeremy Kepner +35
The DAF-MIT AI Accelerator is a collaboration between the United States Department of the Air Force (DAF) and the Massachusetts Institute of Technology (MIT). This program pioneers…
RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge
Alejandro Lancho, Amir Weiss, Gary C. F. Lee +4
We address the critical problem of interference rejection in radio-frequency (RF) signals using a data-driven approach that leverages deep-learning methods. A primary contribution…
Score-based Source Separation with Applications to Digital Communication Signals
Tejas Jayashankar, Gary C. F. Lee, Alejandro Lancho +3
We propose a new method for separating superimposed sources using diffusion-based generative models. Our method relies only on separately trained statistical priors of independent…
On Neural Architectures for Deep Learning-based Source Separation of Co-Channel OFDM Signals
Gary C. F. Lee, Amir Weiss, Alejandro Lancho +2
We study the single-channel source separation problem involving orthogonal frequency-division multiplexing (OFDM) signals, which are ubiquitous in many modern-day digital communica…
Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals
Alejandro Lancho, Amir Weiss, Gary C. F. Lee +4
We study the potential of data-driven deep learning methods for separation of two communication signals from an observation of their mixture. In particular, we assume knowledge on…
Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation
Gary C. F. Lee, Amir Weiss, Alejandro Lancho +4
We study the problem of single-channel source separation (SCSS), and focus on cyclostationary signals, which are particularly suitable in a variety of application domains. Unlike c…