Meta Distributions—Part 1: Definition and Examples

Abstract

Meta distributions (MDs) have emerged as a powerful tool in the analysis of wireless networks. Compared to standard distributions, they enable a clean separation of the different sources of randomness, resulting in sharper, more refined results. In particular, they capture the disparity of the performances of individual links or users. In this first part of a two-letter series, we start from first principles and give the formal definition of MDs and present several simple yet illustrative examples. Part 2 explores the properties of the MD in more depth and offers multiple interpretations and applications.

Topics & Concepts