  Molecular Communications (MC) is a bio-inspired communication paradigm that
uses molecules as information carriers, requiring unconventional transceivers
and modulation/detection techniques. Practical MC receivers (MC-Rxs) can be
implemented using field-effect transistor biosensor (bioFET) architectures,
where surface receptors reversibly react with ligands. The time-varying
concentration of ligand-bound receptors is 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 ligand types, i.e., interferers, co-existing in the MC channel, can
interact with the same type of receptors. Overcoming this molecular cross-talk
in the time domain can be challenging, especially when Rx has no knowledge of
the interferer statistics or operates near saturation. Therefore, we propose a
frequency-domain detection (FDD) technique for bioFET-based MC-Rxs that
exploits the difference in binding reaction rates of different ligand types
reflected in the power spectrum of the ligand-receptor binding noise. We derive
the bit error probability (BEP) of the FDD technique and demonstrate its
effectiveness in decoding transmitted concentration signals under stochastic
molecular interference compared to a widely used time-domain detection (TDD)
technique. We then verified the analytical performance bounds of the FDD
through a particle-based spatial stochastic simulator simulating reactions on
the MC-Rx in microfluidic channels.
