# A Novel Experimental Platform for In-Vessel Multi-Chemical Molecular Communications

## TL;DR

This paper introduces an inexpensive, versatile multi-chemical experimental platform for molecular communication, demonstrating enhanced detection algorithms and significantly improved data rates over previous systems.

## Contribution

The work presents a new multi-chemical platform and machine learning-based detection algorithms, enabling higher data rates and better performance in molecular communication systems.

## Key findings

- Bit error rate improved by an order of magnitude.
- Data rate increased by an order of magnitude.
- Platform can simulate diverse environments.

## Abstract

This work presents a new multi-chemical experimental platform for molecular communication where the transmitter can release different chemicals. This platform is designed to be inexpensive and accessible, and it can be expanded to simulate different environments including the cardiovascular system and complex network of pipes in industrial complexes and city infrastructures. To demonstrate the capabilities of the platform, we implement a time-slotted binary communication system where a bit-0 is represented by an acid pulse, a bit-1 by a base pulse, and information is carried via pH signals. The channel model for this system, which is nonlinear and has long memories, is unknown. Therefore, we devise novel detection algorithms that use techniques from machine learning and deep learning to train a maximum-likelihood detector. Using these algorithms the bit error rate improves by an order of magnitude relative to the approach used in previous works. Moreover, our system achieves a data rate that is an order of magnitude higher than any of the previous molecular communication platforms.