Inference in Turbulent Molecular Information Channels Using Support Vector Machine

Abstract

Inference of transmitter side information is essential to communication. In Molecular Communication (MC), whilst the Bayesian inference of mass diffusion channel parameters is well established, turbulent diffusion (TD) channels are not well-understood. Cascading vortices rapidly transform transmitted momentum (molecular information puffs) into heat, which raises the challenge of receiver inferring transmitter information. Our initial results found that in TD channels, inferring transmitted molecular concentration or timing is challenging. As such, we were motivated to infer transmitter velocity from a flexible receiver sample area. In this paper, we consider an unbounded scenario of Molecular Communication via Turbulent Diffusion (MCvTD) where a transmitter injects several molecular puffs with different velocities. We first developed a time difference concentration (TDC) method based on large-scale support vector machine (SVM) to distinguish the injection velocities. To trade-off the prediction accuracy and number of receiver spatial samples, we propose the stepwise maximum variance (SMV) algorithm to select the limited dominant receiver sampling locations. The overall performance can achieve 100% accuracy in transmitter velocity information recovery, with excellent error vs. receiver size trade-off (e.g., 5% error for 74% area reduction). The research results indicate that velocity modulation at transmitter and TDC with SVM receiver should be used in MCvTD channels.