  In this article we obtain large deviation asymptotics for supercritical
communication networks modelled as signal-interference-noise ratio networks. To
do this, we define the empirical power measure and the empirical connectivity
measure, and prove joint large deviation principles(LDPs) for the two empirical
measures on two different scales i.e. $\lambda$ and $\lambda^2 a_{\lambda},$
where $\lambda$ is the intensity measure of the poisson point process (PPP)
which defines the SINR random network.Using this joint LDPs we prove an
asymptotic equipartition property for the stochastic telecommunication Networks
modelled as the SINR networks. Further, we prove a Local large deviation
principle(LLDP) for the SINR Network. From the LLDP we prove the a large
deviation principle, and a classical MacMillian Theorem for the stochastic SNIR
network processes. Note, for tupical empirical connectivity measure,
$q\pi\otimes\pi,$ we can deduce from the LLDP a bound on the cardinality of the
space of SINR networks to be approximately equal to $\displaystyle e^{\lambda^2
a_{\lambda}\|q\pi\otimes\pi\|H\big(q\pi\otimes\pi/\|q\pi\otimes\pi\|\big)},$
where the connectivity probability of the network, $Q^{z^\lambda} ,$ satisfies
$ a_{\lambda}^{-1}Q^{z^\lambda} \to q.$ Observe, the LDP for the empirical
measures of the stochastic SINR network were obtained on spaces of measures
equipped with the $\tau-$ topology, and the LLDPs were obtained in the space of
SINR network process without any topological restrictions.
