Score-based Source Separation with Applications to Digital Communication Signals
This work addresses the challenge of recovering encoded bits from radio-frequency mixtures, offering a significant improvement for communication systems, though it builds on prior diffusion models.
The paper tackles the problem of separating superimposed sources in digital communication signals using a diffusion-based generative model, achieving a 95% reduction in bit error rate (BER) compared to existing methods.
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 $α$-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