Stochastic Chemical Reaction Networks for MAP Detection in Cellular Receivers

Heinlein B, Brand L, Egan M, Schäfer M, Schober R, Lotter S (2023)

Publication Type: Conference contribution

Publication year: 2023

Publisher: Association for Computing Machinery, Inc

Pages Range: 65-71

Conference Proceedings Title: NanoCom 2023 - Proceedings of the 10th ACM International Conference on Nanoscale Computing and Communication

Event location: Coventry GB

ISBN: 9798400700347

DOI: 10.1145/3576781.3608709

Abstract

In order to fully exploit the potential of molecular communication (MC) for intra-body communication, practically implementable cellular receivers are an important long-term goal. A variety of receiver architectures based on chemical reaction networks (CRNs) and gene-regulatory networks (GRNs) has been introduced in the literature, because cells use these concepts to perform computations in nature. However, practical feasibility is still limited by stochastic fluctuations of chemical reactions and long computation times in GRNs. Therefore, in this paper, we propose two receiver designs based on stochastic CRNs, i.e., CRNs that perform computations by exploiting the intrinsic fluctuations of chemical reactions with very low molecule counts. The first CRN builds on a recent result from chemistry that showed how Boltzmann machines (BMs), a commonly used machine learning model, can be implemented with CRNs. We show that BMs with optimal parameter values and their CRN implementations can act as maximum-a-posteriori (MAP) detectors. Furthermore, we show that BMs can be efficiently trained from simulation data to achieve close-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 pilot symbols even within the cell and thus adapt to changing channel conditions. We extend the literature by showing that practical robust detectors can achieve close-to-MAP performance even without explicit channel knowledge.

Authors with CRIS profile

Lukas Brand Lehrstuhl für Digitale Übertragung Maximilian Schäfer Lehrstuhl für Digitale Übertragung Robert Schober Lehrstuhl für Digitale Übertragung Sebastian Lotter Lehrstuhl für Digitale Übertragung

Involved external institutions

Institut National des Sciences Appliquées de Lyon (INSA Lyon) FR France (FR)

How to cite

APA:

Heinlein, B., Brand, L., Egan, M., Schäfer, M., Schober, R., & Lotter, S. (2023). Stochastic Chemical Reaction Networks for MAP Detection in Cellular Receivers. In NanoCom 2023 - Proceedings of the 10th ACM International Conference on Nanoscale Computing and Communication (pp. 65-71). Coventry, GB: Association for Computing Machinery, Inc.

MLA:

Heinlein, Bastian, et al. "Stochastic Chemical Reaction Networks for MAP Detection in Cellular Receivers." Proceedings of the 10th ACM International Conference on Nanoscale Computing and Communication, NANOCOM 2023, Coventry Association for Computing Machinery, Inc, 2023. 65-71.

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