Neural Network Detectors for Molecular Communication Systems

Nariman Farsad, Andrea Goldsmith

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

6 Scopus citations

Abstract

We consider molecular communication systems and show it is possible to train detectors without any knowledge of the underlying channel models. In particular, we demonstrate that a technique we previously developed, which is called sliding bidirectional recurrent neural network (SBRNN), performs well for a wide range of channel states when it is trained using a dataset that contains many sample transmissions under various channel conditions. We also demonstrate that the bit error rate (BER) performance of the proposed SBRNN detector is better than that of a Viterbi detector (VD) with imperfect channel state information (CSI) and it is computationally efficient.

Original languageEnglish (US)
Title of host publication2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781538635124
DOIs
StatePublished - Aug 24 2018
Externally publishedYes
Event19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018 - Kalamata, Greece
Duration: Jun 25 2018Jun 28 2018

Publication series

NameIEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume2018-June

Conference

Conference19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018
CountryGreece
CityKalamata
Period6/25/186/28/18

All Science Journal Classification (ASJC) codes

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

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