ViterbiNet: Symbol Detection Using a Deep Learning Based Viterbi Algorithm

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Symbol detection plays an important role in the implementation of digital receivers. One of the most common symbol detection schemes is the Viterbi algorithm, which is capable of achieving the minimal probability of error under a broad range of channels encountered in practice. The Viterbi algorithm is based on channel state information (CSI); it requires that the receiver knows exactly (or approximately) the statistical relationship between the channel input and output. In some cases, such knowledge may be unavailable or very difficult to obtain. In this work, we propose ViterbiNet, which is a data-driven symbol detector obtained by converting the Viterbi algorithm into a system utilizing deep neural networks (DNNs). The resulting detector thus operates without CSI. We identify the specific parts of the Viterbi algorithm that are model-based, and design the DNN to implement those computations. Our numerical evaluations demonstrate that the performance of ViterbiNet, which is ignorant of the CSI, approaches that of the optimal CSI-based Viterbi algorithm. Moreover, unlike conventional Viterbi detection, ViterbiNet is robust to model uncertainty. Our results demonstrate the conceptual benefit of designing DNN-based communication systems to implement established algorithms.

Original languageEnglish (US)
Title of host publication2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538665282
DOIs
StatePublished - Jul 2019
Externally publishedYes
Event20th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019 - Cannes, France
Duration: Jul 2 2019Jul 5 2019

Publication series

NameIEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume2019-July

Conference

Conference20th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019
CountryFrance
CityCannes
Period7/2/197/5/19

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Information Systems

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