Machine Learning based Channel Modeling for Molecular MIMO Communications

TL;DR

This paper introduces a novel modeling technique for molecular MIMO channels in diffusion-based molecular communication, addressing the complexity of multiple emitters and demonstrating its effectiveness through numerical validation.

Contribution

It presents a new approach to model molecular MIMO channels, extending existing SISO models to more complex multi-emitter environments.

Findings

The proposed model accurately predicts molecular MIMO channel behavior.

Numerical studies confirm the effectiveness of the modeling approach.

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.