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arXiv · 1704.00870

Machine Learning based Channel Modeling for Molecular MIMO Communications

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

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Changmin Lee, H. Birkan Yilmaz, Chan-Byoung Chae, Nariman Farsad, Andrea Goldsmith. 2017-04-04. Machine Learning based Channel Modeling for Molecular MIMO Communications. https://arxiv.org/abs/1704.00870

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