Score-based Source Separation with Applications to Digital Communication Signals

• URL: http://arxiv.org/abs/2306.14411v3
• Date: Wed, 17 Jan 2024 14:55:36 GMT
• Title: Score-based Source Separation with Applications to Digital Communication Signals
• Authors: Tejas Jayashankar, Gary C.F. Lee, Alejandro Lancho, Amir Weiss, Yury Polyanskiy, Gregory W. Wornell
• Abstract summary: We propose a new method for separating superimposed sources using diffusion-based generative models. Motivated by applications in radio-frequency (RF) systems, we are interested in sources with underlying discrete nature. Our method can be viewed as a multi-source extension to the recently proposed score distillation sampling scheme.
• Score: 72.6570125649502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 maximum a posteriori estimation with an $\alpha$-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

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