Closing the Implementation Gap in MC: Fully Chemical Synchronization and Detection for Cellular Receivers

TL;DR

This work tackles the practical challenge of implementing cellular receivers for molecular communication by introducing a fully chemical RX architecture built on CRNs. It compares two detection paradigms: an offline-trained Boltzmann-machine (BM) detector and an online pilot-symbol–based adaptive detector, each realized entirely with chemical reactions and accessible to intracellular execution. A novel timing mechanism synchronizes transmitter and receiver and coordinates detection, training, and cleanup, enabling end-to-end symbol processing within single cells. Through theoretical analysis and extensive stochastic simulations, the authors show that the BM detector can closely approach MAP performance offline, while the adaptive detector effectively learns and tracks optimal thresholds in time-varying channels, all within a lightweight chemical implementation. The results demonstrate the feasibility and practical potential of biocompatible, fully chemical nano-scale receivers for IoBNT applications, and outline clear paths for further optimization and integration with downstream cellular processes.

Abstract

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).

Figures (11)

• Figure 1: System Model. External signals synchronize the transmission and reception of data and, optionally, pilot symbols. The RX has an internal timing mechanism to coordinate the detection and training process prompted by the respective external signals.
• Figure 2: Data and Pilot Mode. A: *PMES and *DMES switch the TX (RX) into the pilot mode and the data mode for the transmission (reception) of pilot and data symbols, respectively. B: If a detector does not use pilot symbols, only data symbols are transmitted.
• Figure 3: Main functionalities of the RX components.STES ends the previous reset phase, starts the stopwatch, and updates the stored value of the pilot symbol. Once the stopwatch has run long enough, the detection phase starts and the symbol value is estimated. If the training mechanism is active, it updates $n_{\mathrm{W}}$ if necessary. Eventually, the reset phase starts and the RX is reset. The letters on the individual blocks indicate where the chemical implementation is shown in Figure \ref{['fig:system_overview']}.
• Figure 4: Overview of the adaptive RX. A: Stopwatch. B: Switches to define computation phases (cf. Figure \ref{['fig:chemical_switch']}). C: PMES and DMES prompt the transition between pilot and data phases. D: Detection mechanism. E: Production of the helper molecules required for training. F: Training process. G: Chemical flip-flop storing the true value of pilot symbols (cf. Figure \ref{['fig:chemical_flipflop']}). The notation of the chemical reactions in this figure slightly extends \ref{['eq:system_model:reaction_external_catalyst']} such that all types of catalysts (external and non-external) are here depicted by their corresponding symbols above or below the arrows, e.g., the upper reaction in block E is equivalent to $\mathrm{P^{pilot}} \xrightarrow{\mathrm{ST-ES}\,k_{\mathrm{H}}} \mathrm{H} + \mathrm{P^{pilot}}$. The symbol $\varnothing$ denotes any chemical species that is abundant and of no further relevance to the considered CRN.
• Figure 5: Chemical Switch. The reactions in the upper box implement a bi-stable behavior that can be altered by the $\mathrm{T^{ON}}$ molecules via the reactions in the lower box scirep_approx_majority.

Theorems & Definitions (7)

• Definition 1: MAP Property
• Theorem 1
• proof
• Theorem 2
• proof
• Theorem 3
• proof