Neural Network Detectors for Molecular Communication Systems

Nariman Farsad; Andrea Goldsmith

doi:10.1109/SPAWC.2018.8445926

Neural Network Detectors for Molecular Communication Systems

Nariman Farsad, Andrea Goldsmith

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 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 language	English (US)
Title of host publication	2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	9781538635124
DOIs	https://doi.org/10.1109/SPAWC.2018.8445926
State	Published - Aug 24 2018
Externally published	Yes
Event	19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018 - Kalamata, Greece Duration: Jun 25 2018 → Jun 28 2018

Publication series

Name	IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume	2018-June

Conference

Conference	19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018
Country/Territory	Greece
City	Kalamata
Period	6/25/18 → 6/28/18

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering
Computer Science Applications
Information Systems

Access to Document

10.1109/SPAWC.2018.8445926

Cite this

Farsad, N., & Goldsmith, A. (2018). Neural Network Detectors for Molecular Communication Systems. In 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018 Article 8445926 (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC; Vol. 2018-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SPAWC.2018.8445926

@inproceedings{7ff7091a9ebe4578bff94fdc10c8628f,

title = "Neural Network Detectors for Molecular Communication Systems",

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.",

author = "Nariman Farsad and Andrea Goldsmith",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018 ; Conference date: 25-06-2018 Through 28-06-2018",

year = "2018",

month = aug,

day = "24",

doi = "10.1109/SPAWC.2018.8445926",

language = "English (US)",

isbn = "9781538635124",

series = "IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018",

address = "United States",

}

Farsad, N & Goldsmith, A 2018, Neural Network Detectors for Molecular Communication Systems. in 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018., 8445926, IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC, vol. 2018-June, Institute of Electrical and Electronics Engineers Inc., 19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018, Kalamata, Greece, 6/25/18. https://doi.org/10.1109/SPAWC.2018.8445926

Neural Network Detectors for Molecular Communication Systems. / Farsad, Nariman; Goldsmith, Andrea.
2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8445926 (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC; Vol. 2018-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Neural Network Detectors for Molecular Communication Systems

AU - Farsad, Nariman

AU - Goldsmith, Andrea

PY - 2018/8/24

Y1 - 2018/8/24

N2 - 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.

AB - 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.

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U2 - 10.1109/SPAWC.2018.8445926

DO - 10.1109/SPAWC.2018.8445926

M3 - Conference contribution

AN - SCOPUS:85053464407

SN - 9781538635124

T3 - IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC

BT - 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018

Y2 - 25 June 2018 through 28 June 2018

ER -

Farsad N, Goldsmith A. Neural Network Detectors for Molecular Communication Systems. In 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8445926. (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC). doi: 10.1109/SPAWC.2018.8445926

Neural Network Detectors for Molecular Communication Systems

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All Science Journal Classification (ASJC) codes

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