TY - GEN
T1 - Machine learning based channel modeling for molecular MIMO communications
AU - Lee, Changmin
AU - Yilmaz, H. Birkan
AU - Chae, Chan Byoung
AU - Farsad, Nariman
AU - Goldsmith, Andrea
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/19
Y1 - 2017/12/19
N2 - 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.
AB - 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.
KW - Channel model
KW - Molecular communication
KW - Molecular MIMO
KW - Random movement
UR - https://www.scopus.com/pages/publications/85044222769
U2 - 10.1109/SPAWC.2017.8227765
DO - 10.1109/SPAWC.2017.8227765
M3 - Conference contribution
AN - SCOPUS:85044222769
T3 - IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
SP - 1
EP - 5
BT - 18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017
Y2 - 3 July 2017 through 6 July 2017
ER -