Maximum Likelihood Detection With Ligand Receptors for Diffusion-Based Molecular Communications in Internet of Bio-Nano Things

Molecular Communication (MC) is a bio-inspired communication technique that uses molecules as a method of information transfer among nanoscale devices. MC receiver is an essential component having profound impact on the communication system performance. However, the interaction of the receiver with information bearing molecules has been usually oversimplified in modeling the reception process and developing signal detection techniques. In this paper, we focus on the signal detection problem of MC receivers employing receptor molecules to infer the transmitted messages encoded into the concentration of molecules, i.e., ligands. Exploiting the observable characteristics of ligand-receptor binding reaction, we first introduce a Maximum Likelihood (ML) detection method based on instantaneous receptor occupation ratio, as aligned with the current MC literature. Then, we propose a novel ML detection technique, which exploits the amount of time the receptors stay unbound in an observation time window. A comprehensive analysis is carried out to compare the performance of the detectors in terms of bit error probability. In evaluating the detection performance, emphasis is given to the receptor saturation problem resulting from the accumulation of messenger molecules at the receiver as a consequence of intersymbol interference. The results reveal that detection based on receptor unbound time is quite reliable even in saturation, whereas the reliability of detection based on receptor occupation ratio substantially decreases as the receiver gets saturated. Finally, we also discuss the potential methods of implementing the detectors.

Improving the Capacity of Molecular Communication Using Enzymatic Reaction Cycles

This paper considers the capacity of a diffusion-based molecular communication link assuming the receiver uses chemical reactions. The key contribution is we show that enzymatic reaction cycles, which is a class of chemical reactions commonly found in cells consisting of a forward and a backward enzymatic reaction, can improve the capacity of the communication link. The technical difficulty in analyzing enzymatic reaction cycles is that their reaction rates are nonlinear. We deal with this by assuming that the amount of certain chemicals in the enzymatic reaction cycle is large. In order to simplify the problem further, we use singular perturbation to study a particular operating regime of the enzymatic reaction cycles. This allows us to derive a closed-form expression of the channel gain. This expression suggests that we can improve the channel gain by increasing the total amount of substrate in the enzymatic reaction cycle. By using numerical calculations, we show that the effect of the enzymatic reaction cycle is to increase the channel gain and to reduce the noise, which results in a better signal-to-noise ratio and in turn a higher communication capacity. Furthermore, we show that we can increase the capacity by increasing the total amount of substrate in the enzymatic reaction cycle.