Stochastic Geometry and Dense Wireless Networks Francois Baccelli University of Texas at Austin athematics and Electrical and Computer Engineering Stochastic geometry provides a natural way of averaging out the quantitative characteristics of any network information theoretic channel over all potential geometrical patterns or channel gains present in e.g. a stationary Poisson point process. The talk will survey recent scaling laws obtained by this approach on several network information theoretic channels, when the density of the point process tends to infinity. This approach allows one to predict the asymptotic behavior of spectral efficiency in large wireless networks under densification assumptions.