The spatial structure observed in communication networks is usually far from being regular. The network geometry and its structural fluctuations are critical parameters that greatly influence the performance of random networks. Specifically, since the interference and the signal strength at a receiver critically depends on the distribution of the interfering transmitters, mathematical techniques are needed to explicitly model the node distribution.

As a consequence, stochastic geometry and random geometric graph theory have emerged as essential tools to model and quantify interference, connectivity, coverage, as well as outage probability and throughput in large wireless networks. These techniques – including point process theory and percolation theory – were instrumental in recent breakthroughs and have shed light to the fundamental limits of wireless networks.

This page links to material that summarizes the main developments in stochastic geometry and random geometric graphs for the analysis and design of wireless networks. The new analytical tools from these fields and the key results and insights that were derived in the last decade are presented. Our hope is that this web page can serve to the community as a repository and resource for researchers and scientists in the field.