@inproceedings{17483d7f854041f49a1d9d6c0b2c4add,
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.",
keywords = "Channel model, Molecular MIMO, Molecular communication, Random movement",
author = "Changmin Lee and Yilmaz, {H. Birkan} and Chae, {Chan Byoung} and Nariman Farsad and Andrea Goldsmith",
note = "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",
year = "2017",
month = dec,
day = "19",
doi = "10.1109/SPAWC.2017.8227765",
language = "English (US)",
series = "IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--5",
booktitle = "18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017",
address = "United States",
}