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.Comment: Accepted for publicatio