Score-based Source Separation with Applications to Digital Communication Signals

Tejas Jayashankar, Gary C.F. Lee, Alejandro Lancho, Amir Weiss, Yury Polyanskiy, Gregory Wornell

Keywords: Diffusion models, score-based models, source separation, digital communications, maximum a posteriori (MAP) estimation, alpha-posterior, Gaussian smoothing, score distillation sampling, radio frequency systems, interference mitigation

TL;DR: We propose alpha-RGS, a novel score/diffusion-based source separation method leveraging generalized Bayes’ and randomized Gaussian smoothing, focusing on sources with underlying discrete nature, with applications to digital communication systems.

Abstract: 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 sources to establish a new objective function guided by $\textit{maximum a posteriori}$ estimation with an $\textit{$\alpha$-posterior}$, across multiple levels of Gaussian smoothing. Motivated by applications in radio-frequency (RF) systems, we are interested in sources with underlying discrete nature and the recovery of encoded bits from a signal of interest, as measured by the bit error rate (BER). Experimental results with RF mixtures demonstrate that our method results in a BER reduction of 95\% over classical and existing learning-based methods. Our analysis demonstrates that our proposed method yields solutions that asymptotically approach the modes of an underlying discrete distribution. Furthermore, our method can be viewed as a multi-source extension to the recently proposed score distillation sampling scheme, shedding additional light on its use beyond conditional sampling. The project webpage is available at https://alpha-rgs.github.io.

Submission Number: 9904