1 code implementation • NeurIPS 2023 • Tejas Jayashankar, Gary C. F. Lee, Alejandro Lancho, Amir Weiss, Yury Polyanskiy, Gregory W. Wornell
We propose a new method for separating superimposed sources using diffusion-based generative models.
no code implementations • 14 May 2023 • Amir Weiss, Alejandro Lancho, Yuheng Bu, Gregory W. Wornell
A bilateral (i. e., upper and lower) bound on the mean-square error under a general model mismatch is developed.
1 code implementation • 11 Mar 2023 • Gary C. F. Lee, Amir Weiss, Alejandro Lancho, Yury Polyanskiy, Gregory W. Wornell
We study the single-channel source separation problem involving orthogonal frequency-division multiplexing (OFDM) signals, which are ubiquitous in many modern-day digital communication systems.
1 code implementation • 11 Sep 2022 • Alejandro Lancho, Amir Weiss, Gary C. F. Lee, Jennifer Tang, Yuheng Bu, Yury Polyanskiy, Gregory W. Wornell
We study the potential of data-driven deep learning methods for separation of two communication signals from an observation of their mixture.
1 code implementation • 22 Aug 2022 • Gary C. F. Lee, Amir Weiss, Alejandro Lancho, Jennifer Tang, Yuheng Bu, Yury Polyanskiy, Gregory W. Wornell
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.