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

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

doi:10.1109/BlackSeaCom.2017.8277667

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

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

School of Integrated Technology

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

14 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 language	English
Title of host publication	2017 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-5
Number of pages	5
ISBN (Electronic)	9781509050499
DOIs	https://doi.org/10.1109/BlackSeaCom.2017.8277667
Publication status	Published - 2018 Jan 31
Event	2017 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2017 - Istanbul, Turkey Duration: 2017 Jun 5 → 2017 Jun 8

Publication series

Name	2017 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2017
Volume	2018-January

Other

Other	2017 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2017
Country/Territory	Turkey
City	Istanbul
Period	17/6/5 → 17/6/8

Bibliographical note

Funding Information:
This research has been supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the “ICT Consilience Creative Program” (IITP-R0346-16-1008) supervised by the IITP (Institute for Information & Communications Technology Promotion) and by the Basic Science Research Program (2014R1A1A1002186) funded by the MSIP, Korea, through the National Research Foundation of Korea.

Publisher Copyright:
© 2017 IEEE.

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Signal Processing

Access to Document

10.1109/BlackSeaCom.2017.8277667

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 et al. / 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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title = "A machine learning approach to model the received signal in molecular communications",

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.",

author = "Yilmaz, {H. Birkan} and Changmin Lee and Cho, {Yae Jee} and Chae, {Chan Byoung}",

note = "Funding Information: This research has been supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the “ICT Consilience Creative Program” (IITP-R0346-16-1008) supervised by the IITP (Institute for Information & Communications Technology Promotion) and by the Basic Science Research Program (2014R1A1A1002186) funded by the MSIP, Korea, through the National Research Foundation of Korea. Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2017 ; Conference date: 05-06-2017 Through 08-06-2017",

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Yilmaz, HB, Lee, C, Cho, YJ & Chae, CB 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 et al.
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 proceeding › Conference contribution

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AU - Cho, Yae Jee

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N1 - Funding Information: This research has been supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the “ICT Consilience Creative Program” (IITP-R0346-16-1008) supervised by the IITP (Institute for Information & Communications Technology Promotion) and by the Basic Science Research Program (2014R1A1A1002186) funded by the MSIP, Korea, through the National Research Foundation of Korea. Publisher Copyright: © 2017 IEEE.

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N2 - 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.

AB - 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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Y2 - 5 June 2017 through 8 June 2017

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Yilmaz HB, Lee C, Cho YJ, Chae CB. 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). doi: 10.1109/BlackSeaCom.2017.8277667

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

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