This talk will give a survey of recent results on the interplay between stochastic geometry and information theory.
First, we will show that stochastic geometry provides a natural way of defining and computing macroscopic properties of classical channels of network information theory. These macroscopic properties are obtained by some averaging over all node patterns found in a large random network of the Euclidean plane. We will discuss the implications of this spatial averaging viewpoint to wireless network design.
Secondly, we will revisit some of the most basic capacity and error exponent questions of information theory in terms of random geometric objects living in Euclidean spaces with dimensions tending to infinity. This approach allows one to use the theory of large deviations to evaluate random coding error exponents in channels with additive stationary and ergodic noise.