  The performance of wireless networks is fundamentally limited by the
aggregate interference, which depends on the spatial distributions of the
interferers, channel conditions, and user traffic patterns (or queueing
dynamics). These factors usually exhibit spatial and temporal correlations and
thus make the performance of large-scale networks environment-dependent (i.e.,
dependent on network topology, locations of the blockages, etc.). The
correlation can be exploited in protocol designs (e.g., spectrum-, load-,
location-, energy-aware resource allocations) to provide efficient wireless
services. For this, accurate system-level performance characterization and
evaluation with spatio-temporal correlation are required. In this context,
stochastic geometry models and random graph techniques have been used to
develop analytical frameworks to capture the spatio-temporal interference
correlation in large-scale wireless networks. The objective of this article is
to provide a tutorial on the stochastic geometry analysis of large-scale
wireless networks that captures the spatio-temporal interference correlation
(and hence the signal-to-interference ratio (SIR) correlation). We first
discuss the importance of spatio-temporal performance analysis, different
parameters affecting the spatio-temporal correlation in the SIR, and the
different performance metrics for spatio-temporal analysis. Then we describe
the methodologies to characterize spatio-temporal SIR correlations for
different network configurations (independent, attractive, repulsive
configurations), shadowing scenarios, user locations, queueing behavior,
relaying, retransmission, and mobility. We conclude by outlining future
research directions in the context of spatio-temporal analysis of emerging
wireless communications scenarios.
