Molecular Index Modulation using Convolutional Neural Networks

TL;DR

This paper introduces a CNN-based method for molecular communication systems that effectively reduces interference effects, outperforming traditional index modulation and maximum likelihood approaches.

Contribution

A novel CNN architecture tailored for molecular MISO systems is proposed to mitigate interference, demonstrating superior performance over existing methods.

Findings

CNN-based approach outperforms index modulation

Proposed method reduces inter-symbol and inter-link interference

Improved detection accuracy in molecular communication systems

Abstract

As the potential of molecular communication via diffusion (MCvD) systems at nano-scale communication increases, designing molecular schemes robust to the inevitable effects of molecular interference has become of vital importance. There are numerous molecular approaches in literature aiming to mitigate the effects of interference, namely inter-symbol interference. Moreover, for molecular multiple-input-multiple-output systems, interference among antennas, namely inter-link interference, becomes of significance. Inspired by the state-of-the-art performances of machine learning algorithms on making decisions, we propose a novel approach of a convolutional neural network (CNN)-based architecture. The proposed approach is for a uniquely-designed molecular multiple-input-single-output topology in order to alleviate the damaging effects of molecular interference. In this study, we compare the performance of the proposed network with that of an index modulation approach and a symbol-by-symbol maximum likelihood estimation and show that the proposed method yields better performance.