  Most work on wireless network throughput ignores the temporal correlation
inherent to wireless channels because it degrades tractability. To better model
and quantify the temporal variations of wireless network throughput, this paper
introduces a metric termed ergodic transmission capacity (ETC), which includes
spatial and temporal ergodicity. All transmitters in the network form a
homogeneous Poisson point process and all channels are modeled by a finite
state Markov chain. The bounds on outage probability and ETC are characterized,
and their scaling behaviors for a sparse and dense network are discussed. From
these results, we show that the ETC can be characterized by the inner product
of the channel-state related vector and the invariant probability vector of the
Markov chain. This indicates that channel-aware opportunistic transmission does
not always increase ETC. Finally, we look at outage probability with
interference management from a stochastic geometry point of view. The improved
bounds on outage probability and ETC due to interference management are
characterized and they provide some useful insights on how to effectively
manage interference in sparse and dense networks.
