  In the context of the Internet of Bio-Nano Things (IoBNT), nano-devices are
envisioned to perform complex tasks collaboratively, i.e., by communicating
with each other. One candidate for the implementation of such devices are
engineered cells due to their inherent biocompatibility. However, because each
engineered cell has only little computational capabilities, transmitter and
receiver (RX) functionalities can afford only limited complexity. In this
paper, we propose a simple, yet modular, architecture for a cellular RX that is
capable of processing a stream of observed symbols using chemical reaction
networks. Furthermore, we propose two specific detector implementations for the
RX. The first detector is based on a machine learning model that is trained
offline, i.e., before the cellular RX is deployed. The second detector utilizes
pilot symbol-based training and is therefore able to continuously adapt to
changing channel conditions online, i.e., after deployment. To coordinate the
different chemical processing steps involved in symbol detection, the proposed
cellular RX leverages an internal chemical timer. Furthermore, the RX is
synchronized with the transmitter via external, i.e., extracellular, signals.
Finally, the proposed architecture is validated using theoretical analysis and
stochastic simulations. The presented results confirm the feasibility of both
proposed implementations and reveal that the proposed online learning-based RX
is able to perform reliable detection even in initially unknown or slowly
changing channels. By its modular design and exclusively chemical
implementation, the proposed RX contributes towards the realization of
versatile and biocompatible nano-scale communication networks for IoBNT
applications narrowing the existing implementation gap in cellular molecular
communication (MC).
