A Machine Learning Approach to Model the Received Signal in Molecular Communications

TL;DR

This paper employs artificial neural networks to accurately model the received signal in diffusion-based molecular communication channels with spherical transmitters, addressing an open modeling challenge.

Contribution

It introduces a neural network-based model for the received signal in spherical transmitter molecular communication channels, improving upon previous point transmitter assumptions.

Findings

Neural network effectively models the received signal.

Model applicable to spherical transmitters.

Enhances understanding of diffusion-based channels.

Abstract

A molecular communication channel is determined by the received signal. Received signal models form the basis for studies focused on modulation, receiver design, capacity, and coding depend on the received signal models. Therefore, it is crucial to model the number of received molecules until time $t$ analytically. Modeling the diffusion-based molecular communication channel with the first-hitting process is an open issue for a spherical transmitter. In this paper, we utilize the artificial neural networks technique to model the received signal for a spherical transmitter and a perfectly absorbing receiver (i.e., first hitting process). The proposed technique may be utilized in other studies that assume a spherical transmitter instead of a point transmitter.