Interference-Aware Molecular Detector Design for Clustered Bio-Nanonetworks

We present a comprehensive approach to the modeling and design of clustered molecular bio-nanonetworks in which nano-machines of different clusters release an appropriate number of molecules to transmit their sensed information to their respective fusion centers. The fusion centers decode this information by counting the number of molecules received in the given time slot. Owing to the propagation properties of the biological media, this setup suffers from both inter- and intra-cluster interference that needs to be carefully modeled. We first develop a novel spatial model for this setup by modeling nanomachines as a Poisson cluster process with the fusion centers forming its parent point process. For this setup, we then derive a new set of distance distributions in the three-dimensional space, resulting in a remarkably simple result for the special case of the Thomas cluster process. Accordingly, total interference from previous symbols and different clusters is characterized and its expected value is obtained. Then, using the expected value, a simple detector suitable for biological applications is proposed. The impact of different parameters on the performance of the detector is also investigated.