Machine learning based channel modeling for molecular MIMO communications

Changmin Lee; H. Birkan Yilmaz; Chan Byoung Chae; Nariman Farsad; Andrea Goldsmith

doi:10.1109/SPAWC.2017.8227765

Machine learning based channel modeling for molecular MIMO communications

Changmin Lee, H. Birkan Yilmaz, Chan Byoung Chae, Nariman Farsad, Andrea Goldsmith

School of Integrated Technology

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

45 Citations (Scopus)

Abstract

In diffusion-based molecular communication, information particles locomote via a diffusion process, characterized by random movement and heavy tail distribution for the random arrival time. As a result, the molecular communication shows lower transmission rates than the traditional communication. To compensate for such low rates, researchers have recently proposed the molecular multiple-input multiple-output (MIMO) technique. Although channel models exist for single-input single-output (SISO) systems for some simple environments, extending the results to multiple molecular emitters complicates the modeling process. In this paper, we introduce a novel machine learning technique for modeling the molecular MIMO channel and confirm the effectiveness via extensive numerical studies.

Original language	English
Title of host publication	18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-5
Number of pages	5
ISBN (Electronic)	9781509030088
DOIs	https://doi.org/10.1109/SPAWC.2017.8227765
Publication status	Published - 2017 Dec 19
Event	18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017 - Sapporo, Japan Duration: 2017 Jul 3 → 2017 Jul 6

Publication series

Name	IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume	2017-July

Other

Other	18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017
Country/Territory	Japan
City	Sapporo
Period	17/7/3 → 17/7/6

Bibliographical note

Funding Information:
This work was supported by the MSIP/IITP, under the ”ICT Consilience Creative Program” (IITP-2017-2017-0-01015) and by the Basic Science Research Program (2017R1A1A1A05001439) through the NRF of Korea.

Publisher Copyright:
© 2017 IEEE.

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering
Computer Science Applications
Information Systems

Access to Document

10.1109/SPAWC.2017.8227765

Cite this

Lee, C., Yilmaz, H. B., Chae, C. B., Farsad, N., & Goldsmith, A. (2017). Machine learning based channel modeling for molecular MIMO communications. In 18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017 (pp. 1-5). [8227765] (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC; Vol. 2017-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SPAWC.2017.8227765

Lee, Changmin ; Yilmaz, H. Birkan ; Chae, Chan Byoung et al. / Machine learning based channel modeling for molecular MIMO communications. 18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1-5 (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC).

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title = "Machine learning based channel modeling for molecular MIMO communications",

abstract = "In diffusion-based molecular communication, information particles locomote via a diffusion process, characterized by random movement and heavy tail distribution for the random arrival time. As a result, the molecular communication shows lower transmission rates than the traditional communication. To compensate for such low rates, researchers have recently proposed the molecular multiple-input multiple-output (MIMO) technique. Although channel models exist for single-input single-output (SISO) systems for some simple environments, extending the results to multiple molecular emitters complicates the modeling process. In this paper, we introduce a novel machine learning technique for modeling the molecular MIMO channel and confirm the effectiveness via extensive numerical studies.",

author = "Changmin Lee and Yilmaz, {H. Birkan} and Chae, {Chan Byoung} and Nariman Farsad and Andrea Goldsmith",

note = "Funding Information: This work was supported by the MSIP/IITP, under the ”ICT Consilience Creative Program” (IITP-2017-2017-0-01015) and by the Basic Science Research Program (2017R1A1A1A05001439) through the NRF of Korea. Publisher Copyright: {\textcopyright} 2017 IEEE.; 18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017 ; Conference date: 03-07-2017 Through 06-07-2017",

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doi = "10.1109/SPAWC.2017.8227765",

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Lee, C, Yilmaz, HB, Chae, CB, Farsad, N & Goldsmith, A 2017, Machine learning based channel modeling for molecular MIMO communications. in 18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017., 8227765, IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC, vol. 2017-July, Institute of Electrical and Electronics Engineers Inc., pp. 1-5, 18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017, Sapporo, Japan, 17/7/3. https://doi.org/10.1109/SPAWC.2017.8227765

Machine learning based channel modeling for molecular MIMO communications. / Lee, Changmin; Yilmaz, H. Birkan; Chae, Chan Byoung et al.
18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-5 8227765 (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC; Vol. 2017-July).

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

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N1 - Funding Information: This work was supported by the MSIP/IITP, under the ”ICT Consilience Creative Program” (IITP-2017-2017-0-01015) and by the Basic Science Research Program (2017R1A1A1A05001439) through the NRF of Korea. Publisher Copyright: © 2017 IEEE.

PY - 2017/12/19

Y1 - 2017/12/19

N2 - In diffusion-based molecular communication, information particles locomote via a diffusion process, characterized by random movement and heavy tail distribution for the random arrival time. As a result, the molecular communication shows lower transmission rates than the traditional communication. To compensate for such low rates, researchers have recently proposed the molecular multiple-input multiple-output (MIMO) technique. Although channel models exist for single-input single-output (SISO) systems for some simple environments, extending the results to multiple molecular emitters complicates the modeling process. In this paper, we introduce a novel machine learning technique for modeling the molecular MIMO channel and confirm the effectiveness via extensive numerical studies.

AB - In diffusion-based molecular communication, information particles locomote via a diffusion process, characterized by random movement and heavy tail distribution for the random arrival time. As a result, the molecular communication shows lower transmission rates than the traditional communication. To compensate for such low rates, researchers have recently proposed the molecular multiple-input multiple-output (MIMO) technique. Although channel models exist for single-input single-output (SISO) systems for some simple environments, extending the results to multiple molecular emitters complicates the modeling process. In this paper, we introduce a novel machine learning technique for modeling the molecular MIMO channel and confirm the effectiveness via extensive numerical studies.

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Lee C, Yilmaz HB, Chae CB, Farsad N, Goldsmith A. Machine learning based channel modeling for molecular MIMO communications. In 18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1-5. 8227765. (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC). doi: 10.1109/SPAWC.2017.8227765

Machine learning based channel modeling for molecular MIMO communications

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