In a diffusion-based molecular communication network, transmitters and receivers communicate by using signalling molecules (or ligands) in a fluid medium. This paper proposes a novel modulation mechanism for molecular communication called Reaction Shift Keying (RSK). In RSK, the transmitter uses different chemical reactions to generate different time-varying functions of concentration of signalling molecules to represent different transmission symbols . We consider the problem of demodulating the RSK symbols assuming that the transmitter and receiver are synchronised. We assume the receiver consists of receptors and signalling molecules may react with these receptors to form ligand-receptor complexes.  We derive an optimal RSK demodulator using the continuous history of the number of complexes at the receiver as the input to the demodulator. We do that by first deriving a communication model which includes the chemical reactions in the transmitter, diffusion in the transmission medium and the ligand-receptor process in the receiver. This model, which takes the form of a continuous-time Markov process, captures the noise in the receiver signal due to the stochastic nature of chemical reactions and diffusion. We then adopt a maximum  posterior framework and use Bayesian filtering to derive the optimal demodulator for RSK signals . We use numerical examples to illustrate the properties of the RSK demodulator.
In a diffusion-based molecular communication network, transmitters and receivers communicate by using signalling molecules (or ligands) in a fluid medium. This paper assumes that the transmitter uses different chemical reactions to generate different emission patterns of signalling molecules to represent different transmission symbols , and the receiver consists of receptors . When the signalling molecules arrive at the receiver, they may react with the receptors to form ligand-receptor complexes. Our goal is to study the demodulation in this setup assuming that the transmitter and receiver are synchronised. We derive an optimal demodulator using the continuous history of the number of complexes at the receiver as the input to the demodulator. We do that by first deriving a communication model which includes the chemical reactions in the transmitter, diffusion in the transmission medium and the ligand-receptor process in the receiver. This model, which takes the form of a continuous-time Markov process, captures the noise in the receiver signal due to the stochastic nature of chemical reactions and diffusion. We then adopt a maximum a posterior framework and use Bayesian filtering to derive the optimal demodulator . We use numerical examples to illustrate the properties of this optimal demodulator.
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