A machine learning approach to model the received signal in molecular communications

H. Birkan Yilmaz, Changmin Lee, Yae Jee Cho, Chan-Byoung Chae

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

4 Citations (Scopus)

Abstract

A molecular communication channel is determined by the received signal, which forms the basis for studies that are focusing on modulation, receiver design, capacity, and coding. Therefore, it is crucial to model the number of received molecules until time t. Received signal is modeled analytically when the transmitter is a point and the receiver is an absorbing sphere. Modeling the diffusion-based molecular communication channel with the first-hitting process (i.e., with an absorbing receiver) is an open issue when the transmitter is a reflecting spherical body. In this paper, we utilize the artificial neural networks technique to model the received signal for a spherical transmitter and a perfectly absorbing receiver (i.e., first-hitting process). The proposed technique may be utilized in other studies that assume a spherical transmitter instead of a point transmitter.

Original languageEnglish
Title of host publication2017 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781509050499
DOIs
Publication statusPublished - 2018 Jan 31
Event2017 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2017 - Istanbul, Turkey
Duration: 2017 Jun 52017 Jun 8

Publication series

Name2017 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2017
Volume2018-January

Other

Other2017 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2017
CountryTurkey
CityIstanbul
Period17/6/517/6/8

Fingerprint

Learning systems
Transmitters
Communication
Modulation
Neural networks
Molecules

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Signal Processing

Cite this

Yilmaz, H. B., Lee, C., Cho, Y. J., & Chae, C-B. (2018). A machine learning approach to model the received signal in molecular communications. In 2017 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2017 (pp. 1-5). (2017 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BlackSeaCom.2017.8277667
Yilmaz, H. Birkan ; Lee, Changmin ; Cho, Yae Jee ; Chae, Chan-Byoung. / A machine learning approach to model the received signal in molecular communications. 2017 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2017. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-5 (2017 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2017).
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abstract = "A molecular communication channel is determined by the received signal, which forms the basis for studies that are focusing on modulation, receiver design, capacity, and coding. Therefore, it is crucial to model the number of received molecules until time t. Received signal is modeled analytically when the transmitter is a point and the receiver is an absorbing sphere. Modeling the diffusion-based molecular communication channel with the first-hitting process (i.e., with an absorbing receiver) is an open issue when the transmitter is a reflecting spherical body. In this paper, we utilize the artificial neural networks technique to model the received signal for a spherical transmitter and a perfectly absorbing receiver (i.e., first-hitting process). The proposed technique may be utilized in other studies that assume a spherical transmitter instead of a point transmitter.",
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Yilmaz, HB, Lee, C, Cho, YJ & Chae, C-B 2018, A machine learning approach to model the received signal in molecular communications. in 2017 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2017. 2017 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-5, 2017 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2017, Istanbul, Turkey, 17/6/5. https://doi.org/10.1109/BlackSeaCom.2017.8277667

A machine learning approach to model the received signal in molecular communications. / Yilmaz, H. Birkan; Lee, Changmin; Cho, Yae Jee; Chae, Chan-Byoung.

2017 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-5 (2017 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2017; Vol. 2018-January).

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

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Yilmaz HB, Lee C, Cho YJ, Chae C-B. A machine learning approach to model the received signal in molecular communications. In 2017 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-5. (2017 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2017). https://doi.org/10.1109/BlackSeaCom.2017.8277667