Frequency-Domain Detection for Molecular Communications

TL;DR

This paper introduces a frequency-domain detection method for molecular communication receivers based on bioFET sensors, exploiting ligand-receptor binding rate differences to improve decoding accuracy amid molecular interference.

Contribution

The study proposes a novel frequency-domain detection technique for bioFET-based molecular communication receivers, addressing molecular cross-talk issues and deriving analytical error probability expressions.

Findings

FDD outperforms traditional TDD in noisy interference conditions.

Analytical BEP expressions validate FDD's effectiveness.

Applicable to various biosensor-based MC receivers.

Abstract

Molecular Communications (MC) is a bio-inspired communication paradigm which uses molecules as information carriers, thereby requiring unconventional transmitter/receiver architectures and modulation/detection techniques. Practical MC receivers (MC-Rxs) can be implemented based on field-effect transistor biosensor (bioFET) architectures, where surface receptors reversibly react with ligands, whose concentration encodes the information. The time-varying concentration of ligand-bound receptors is then translated into electrical signals via field-effect, which is used to decode the transmitted information. However, ligand-receptor interactions do not provide an ideal molecular selectivity, as similar types of ligands, i.e., interferers, co-existing in the MC channel can interact with the same type of receptors, resulting in cross-talk. Overcoming this molecular cross-talk with time-domain samples of the Rx's electrical output is not always attainable, especially when Rx has no knowledge of the interferer statistics or it operates near saturation. In this study, we propose a frequency-domain detection (FDD) technique for bioFET-based MC-Rxs, which exploits the difference in binding reaction rates of different types of ligands, reflected to the noise spectrum of the ligand-receptor binding fluctuations. We analytically derive the bit error probability (BEP) of the FDD technique, and demonstrate its effectiveness in decoding transmitted concentration signals under stochastic molecular interference, in comparison to a widely-used time-domain detection (TDD) technique. The proposed FDD method can be applied to any biosensor-based MC-Rxs, which employ receptor molecules as the channel-Rx interface.