CNN-Based Detection of Mixed-Molecule Concentrations in Molecular Communication

TL;DR

This paper introduces a CNN-based method for detecting multiple molecule concentrations in molecular communication systems, demonstrating effective decoding even under noise and desynchronization conditions.

Contribution

It develops a fractal CNN model trained on combined experimental and noise-augmented simulated data for robust multi-molecule detection in MC.

Findings

Noise-augmented simulated data matches experimental accuracy

Effective detection in BCSK and QCSK scenarios

Robust performance with desynchronized transmitters

Abstract

Molecular communication (MC) is a promising paradigm for applications where traditional electromagnetic communications are impractical. However, decoding chemical signals, especially in multi-transmitter systems, remains a key challenge due to interference and complex propagation dynamics. In this paper, we develop a one-dimensional fractal convolutional neural network (fCNN) to detect the concentrations of multiple types of molecules based on the absorbance spectra measured at a receiver. Our model is trained by both experimental and simulated datasets, with the latter enhanced by noise modeling to mimic real-world measurements. We demonstrate that a noiseaugmented simulated dataset can effectively be a substitute for experimental data, achieving similar decoding accuracy. Our approach successfully detects bit sequences in both binary and quadruple concentration shift keying (BCSK and QCSK) scenarios, even when transmitters are desynchronized, highlighting the potential of machine learning for robust MC signal detection.