  In this work, we study integrated sensing and communication (ISAC) networks
with the aim of effectively balancing sensing and communication (S&C)
performance at the network level. Focusing on monostatic sensing, the tool of
stochastic geometry is exploited to capture the S&C performance, which
facilitates us to illuminate key cooperative dependencies in the ISAC network
and optimize key network-level parameters. Based on the derived tractable
expression of area spectral efficiency (ASE), we formulate the optimization
problem to maximize the network performance from the view point of two joint
S&C metrics. Towards this end, we further jointly optimize the cooperative BS
cluster sizes for S&C and the serving/probing numbers of users/targets to
achieve a flexible tradeoff between S&C at the network level. It is verified
that interference nulling can effectively improve the average data rate and
radar information rate. Surprisingly, the optimal communication tradeoff for
the case of the ASE maximization tends to employ all spacial resources towards
multiplexing and diversity gain, without interference nulling. By contrast, for
the sensing objectives, resource allocation tends to eliminate certain
interference especially when the antenna resources are sufficient, because the
inter-cell interference becomes a more dominant factor affecting sensing
performance. Furthermore, we prove that the ratio of the optimal number of
users and the number of transmit antennas is a constant value when the
communication performance is optimal. Simulation results demonstrate that the
proposed cooperative ISAC scheme achieves a substantial gain in S&C performance
at the network level.
