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In order to fully exploit the potential of molecular communication (MC) forintra-body communication, practically implementable cellular receivers are animportant long-term goal. A variety of receiver architectures based on chemicalreaction networks (CRNs) and gene-regulatory networks (GRNs) has beenintroduced in the literature, because cells use these concepts to performcomputations in nature. However, practical feasibility is still limited bystochastic fluctuations of chemical reactions and long computation times inGRNs. Therefore, in this paper, we propose two receiver designs based onstochastic CRNs, i.e., CRNs that perform computations by exploiting theintrinsic fluctuations of chemical reactions with very low molecule counts. Thefirst CRN builds on a recent result from chemistry that showed how Boltzmannmachines (BMs), a commonly used machine learning model, can be implemented withCRNs. We show that BMs with optimal parameter values and their CRNimplementations can act as maximum-a-posteriori (MAP) detectors. Furthermore,we show that BMs can be efficiently trained from simulation data to achieveclose-to-MAP performance. While this approach yields a fixed CRN once deployed,our second approach based on a manually designed CRN can be trained with pilotsymbols even within the cell and thus adapt to changing channel conditions. Weextend the literature by showing that practical robust detectors can achieveclose-to-MAP performance even without explicit channel knowledge.

Stochastic Chemical Reaction Networks for MAP Detection in Cellular Receivers