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Most work on wireless network throughput ignores the temporal correlation inherent to wireless channels, due to trouble with tractability. In order to better capture the temporal variations of wireless network throughput, this paper introduces the metric of Ergodic Spatial Throughput (EST), which includes spatial and temporal ergodicity. All transmitters in the network form a stationary Poisson point process and all channels are modeled by a finite state Markov chain. The bounds on EST are characterized, and their scaling behaviors for a sparse and dense network are discussed. From these results, the authors show that the EST can be characterized by the inner product of the channel state vector and the invariant probability vector of the Markov chain.
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