# Maximum Likelihood Detection for Cooperative Molecular Communication

## TL;DR

This paper introduces maximum likelihood detection methods for cooperative molecular communication systems, analyzing their performance trade-offs and demonstrating their effectiveness through theoretical derivations and simulations.

## Contribution

It proposes three ML detection variants for cooperative MC, deriving error probabilities and comparing their performance with simpler methods.

## Key findings

- ML detection variants outperform simpler cooperative methods
- Majority rule detection performs comparably to ML under noisy reporting
- Closed-form error probability derived for one detection variant

## Abstract

In this paper, symbol-by-symbol maximum likelihood (ML) detection is proposed for a cooperative diffusion-based molecular communication (MC) system. In this system, a fusion center (FC) chooses the transmitter's symbol that is more likely, given the likelihood of the observations from multiple receivers (RXs). We propose three different ML detection variants according to different constraints on the information available to the FC, which enables us to demonstrate trade-offs in their performance versus the information available. The system error probability for one variant is derived in closed form. Numerical and simulation results show that the ML detection variants provide lower bounds on the error performance of the simpler cooperative variants and demonstrate that majority rule detection has performance comparable to ML detection when the reporting is noisy.