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.
|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.|
|Number of pages||5|
|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
|Name||IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC|
|Other||18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017|
|Period||17/7/3 → 17/7/6|
Bibliographical noteFunding 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.
© 2017 IEEE.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Computer Science Applications
- Information Systems