Fundamentals of Clustered Molecular Nanonetworks

TL;DR

This paper develops a novel spatial model for clustered molecular nanonetworks, analyzing interference, error probability, and performance, with applications in biological communication systems.

Contribution

It introduces a new Poisson cluster process model for nano-machines and derives distance distributions and interference analysis for 3D molecular communication networks.

Findings

Derived new distance distributions for nano-machines in 3D space.

Characterized interference and its Laplace transform in clustered nanonetworks.

Analyzed error probability and impact of parameters on network performance.

Abstract

We present a comprehensive approach to the modeling, performance analysis, and design of clustered molecular 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. To facilitate rigorous analysis, we first develop a novel spatial model for this setup by modeling nano-machines as a Poisson cluster process with the fusion centers forming its parent point process. For this setup, we first 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. Using this, total interference from previous symbols and different clusters is characterized and its expected value and Laplace transform are obtained. The error probability of a simple detector suitable for biological applications is analyzed, and approximate and upper-bound results are provided. The impact of different parameters on the performance is also investigated.