To address the challenges listed above, our group is currently working on the following topics:
- Receiver Modeling: The common mechanism of cells for learning about changes in their environment is via sensing of the local concentration of molecules with the help of receptors covering their surface. In particular, molecules that are in the vicinity of the cell surface can reversibly bind to the receptors and activate them. Upon activation of a receptor, a chain of chemical reactions, the so-called signaling pathways, will be triggered inside the cell which ultimately changes the behaviour of the cell. Motivated by this natural mechanism, we modelled the receiver as a sphere covered with a finite number of receptors. Then, we studied and modelled the chemical reactions happening on the surface of the receiver for activation of each receptor.
- Reducing Interference with Enzymes: Enzymes are reaction catalysts that do not degrade when they react. Deploying enzymes to react with information molecules in the propagation environment enables the transmitter to transmit data (by emitting molecules) more frequently and with less risk of interference. There is also less interference from neighboring communication links. These gains can be achieved with no additional complexity at the sender or receiver, which is a very useful benefit for the case of individual nanomachines with limited computational capabilities.
- Scalability of Reaction-Diffusion Systems: The simulation of individual molecules requires more computational resources as more molecules are needed in the environment. By using dimensional analysis, smaller environments can be simulated and the results extrapolated to larger systems as needed.
- Optimal Receiver Design: Even though the computational capabilities of nanomachines are limited, it is of interest to derive the optimal receiver to act as a benchmark for comparing more practical methods. It is especially of interest if the error probability of a simple receiver design can approach that of the optimal receiver.
- Improving Range with Relays: The propagation time of molecules increases with the square of distance. Furthermore, the number of molecules that will eventually reach a receiver decreases with distance. A network may include a receiver that is very far away and communicating with that receiver using a single transmitter may be impractical. Molecular relays could be used to help information reach the receiver much faster and more reliably by amplifying the signal from the initial transmitter.
- Distance Estimation: The throughput between a transmitter and receiver is highly dependent on the physical distance between them (as well as the presence of flow). The knowledge of this distance at both the transmitter and receiver can help tune the communication scheme. The distance can be measured if the transmitter emits training pulses to the receiver. Specifically, the receiver could measure how long the signal takes to dissipate. The roles of transmitter and receiver can be exchanged so that the original transmitter also learns this distance.
- Channel Estimation: Knowledge of the channel state information (CSI) is needed for the design of detection and equalization schemes. Hence, we developed a training-based channel estimation framework for MC systems which aims at estimating the channel impulse response (CIR) based on the observed number of molecules at the receiver due to emission of a sequence of known numbers of molecules by the transmitter.
- Non-Coherent Detection: Acquisition of the CSI in MC might be a challenging task for nano-receivers with limited computational capability particularly when the MC channel changes rapidly. Therefore, we study non-coherent multiple-symbol detection schemes which directly detect the data symbols based on the received observations and without spending any resources on CSI acquisition.