تقرير
Score-based Source Separation with Applications to Digital Communication Signals
| العنوان: | Score-based Source Separation with Applications to Digital Communication Signals |
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| المؤلفون: | Jayashankar, Tejas, Lee, Gary C. F., Lancho, Alejandro, Weiss, Amir, Polyanskiy, Yury, Wornell, Gregory W. |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing |
| الوصف: | 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 Comment: 34 pages, 18 figures, for associated project webpage see https://alpha-rgs.github.io |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2306.14411 |
| رقم الأكسشن: | edsarx.2306.14411 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |