  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.
