Data-Driven Symbol Detection Via Model-Based Machine Learning

Nariman Farsad, Nir Shlezinger, Andrea J Goldsmith, Yonina C Eldar

Research output: Contribution to conferencePaperpeer-review

15 Scopus citations


We present a data-driven framework to symbol detection design that combines machine learning (ML) and model-based algorithms. The resulting data-driven receivers are most suitable for systems where the underlying channel models are poorly understood, highly complex, or do not well-capture the underlying physics. Our approach is unique in that it only replaces the channel-model-based computations with dedicated neural networks that can be trained from a small amount of data, while keeping the general algorithm intact. Our results demonstrate that these techniques can yield performance close to that of model-based algorithms with perfect model knowledge without knowing the exact channel model or state.

Original languageEnglish
Number of pages5
StatePublished - 11 Jul 2021
Event21st IEEE Statistical Signal Processing Workshop, SSP 2021 - Virtual, Rio de Janeiro, Brazil
Duration: 11 Jul 202114 Jul 2021


Conference21st IEEE Statistical Signal Processing Workshop, SSP 2021
CityVirtual, Rio de Janeiro

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications


Dive into the research topics of 'Data-Driven Symbol Detection Via Model-Based Machine Learning'. Together they form a unique fingerprint.

Cite this