  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.
