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

3 Scopus citations

Abstract

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
Pages571-575
Number of pages5
DOIs
StatePublished - 11 Jul 2021
Event21st IEEE Statistical Signal Processing Workshop, SSP 2021 - Virtual, Rio de Janeiro, Brazil
Duration: 11 Jul 202114 Jul 2021

Conference

Conference21st IEEE Statistical Signal Processing Workshop, SSP 2021
Country/TerritoryBrazil
CityVirtual, Rio de Janeiro
Period11/07/2114/07/21

ASJC Scopus subject areas

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

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