Olfaction-inspired MCs: Molecule Mixture Shift Keying and Cross-Reactive Receptor Arrays

TL;DR

This paper introduces a novel molecular communication system inspired by animal olfaction, utilizing cross-reactive receptor arrays to enable efficient multi-user communication with reduced hardware complexity.

Contribution

It proposes a new olfaction-inspired MC system design using cross-reactive receptors and develops algorithms for molecule allocation and mixture recovery.

Findings

End-to-end MC channel model based on olfaction properties

Algorithms for molecule allocation and mixture optimization

Convex compressive sensing approach for molecule mixture recovery

Abstract

In this paper, we propose a novel concept for engineered molecular communication (MC) systems inspired by animal olfaction. We focus on a multi-user scenario where several transmitters wish to communicate with a central receiver. We assume that each transmitter employs a unique mixture of different types of signaling molecules to represent its message and the receiver is equipped with an array comprising $R$ different types of receptors in order to detect the emitted molecule mixtures. The design of an MC system based on \textit{orthogonal} molecule-receptor pairs implies that the hardware complexity of the receiver linearly scales with the number of signaling molecule types $Q$ (i.e., $R=Q$). Natural olfaction systems avoid such high complexity by employing arrays of \textit{cross-reactive} receptors, where each type of molecule activates multiple types of receptors and each type of receptor is predominantly activated by multiple types of molecules albeit with different activation strengths. For instance, the human olfactory system is believed to discriminate several thousands of chemicals using only a few hundred receptor types, i.e., $Q\gg R$. Motivated by this observation, we first develop an end-to-end MC channel model that accounts for the key properties of olfaction. Subsequently, we present the proposed transmitter and receiver designs. In particular, given a set of signaling molecules, we develop algorithms that allocate molecules to different transmitters and optimize the mixture alphabet for communication. Moreover, we formulate the molecule mixture recovery as a convex compressive sensing problem which can be efficiently solved via available numerical solvers.