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. 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 technique for modeling the molecular MIMO channel and confirm the effectiveness via numerical studies.
@article{arxiv.1704.00870,
title = {Machine Learning based Channel Modeling for Molecular MIMO Communications},
author = {Changmin Lee and H. Birkan Yilmaz and Chan-Byoung Chae and Nariman Farsad and Andrea Goldsmith},
journal= {arXiv preprint arXiv:1704.00870},
year = {2017}
}