Machine learning based channel modeling for molecular MIMO communications

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

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

22 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 languageEnglish
Title of host publication18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781509030088
DOIs
Publication statusPublished - 2017 Dec 19
Event18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017 - Sapporo, Japan
Duration: 2017 Jul 32017 Jul 6

Publication series

NameIEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume2017-July

Other

Other18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017
CountryJapan
CitySapporo
Period17/7/317/7/6

All Science Journal Classification (ASJC) codes

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

Fingerprint Dive into the research topics of 'Machine learning based channel modeling for molecular MIMO communications'. Together they form a unique fingerprint.

  • 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