The Meta Distribution of the SIR in Poisson Bipolar and Cellular Networks

TL;DR

This paper introduces the meta distribution of the SIR in Poisson bipolar and cellular networks, providing detailed insights into individual link success probabilities beyond average metrics, with analytical bounds, exact expressions, and practical implications.

Contribution

It develops the concept of the meta distribution of SIR, offering a more detailed analysis of link success probabilities in stochastic network models, including bounds and approximations.

Findings

Meta distribution offers fine-grained link success probability insights.

In bipolar networks, success probability becomes uniform as p→0 with constant density.

In cellular networks, success probability variance remains non-zero as p→0.

Abstract

The calculation of the SIR distribution at the typical receiver (or, equivalently, the success probability of transmissions over the typical link) in Poisson bipolar and cellular networks with Rayleigh fading is relatively straightforward, but it only provides limited information on the success probabilities of the individual links. This paper introduces the notion of the meta distribution of the SIR, which is the distribution of the conditional success probability $P$ given the point process, and provides bounds, an exact analytical expression, and a simple approximation for it. The meta distribution provides fine-grained information on the SIR and answers questions such as "What fraction of users in a Poisson cellular network achieve 90% link reliability if the required SIR is 5 dB?". Interestingly, in the bipolar model, if the transmit probability $p$ is reduced while increasing the network density $\lambda$ such that the density of concurrent transmitters $\lambda p$ stays constant as $p\to 0$, $P$ degenerates to a constant, i.e., all links have exactly the same success probability in the limit, which is the one of the typical link. In contrast, in the cellular case, if the interfering base stations are active independently with probability $p$, the variance of $P$ approaches a non-zero constant when $p$ is reduced to $0$ while keeping the mean success probability constant.