An overview of the transmission capacity of wireless networks

TL;DR

This paper reviews the transmission capacity framework for decentralized wireless networks, providing analytical tools and bounds to quantify single-hop rates and network performance under various channel conditions.

Contribution

It unifies recent research on transmission capacity, extending analysis to include fading channels and applications to network design strategies.

Findings

Derived tight bounds on transmission capacity with path loss only.

Provided analytical approximations for channels with fading and shadowing.

Applied results to optimize scheduling, power control, and antenna deployment.

Abstract

This paper surveys and unifies a number of recent contributions that have collectively developed a metric for decentralized wireless network analysis known as transmission capacity. Although it is notoriously difficult to derive general end-to-end capacity results for multi-terminal or \adhoc networks, the transmission capacity (TC) framework allows for quantification of achievable single-hop rates by focusing on a simplified physical/MAC-layer model. By using stochastic geometry to quantify the multi-user interference in the network, the relationship between the optimal spatial density and success probability of transmissions in the network can be determined, and expressed -- often fairly simply -- in terms of the key network parameters. The basic model and analytical tools are first discussed and applied to a simple network with path loss only and we present tight upper and lower bounds on transmission capacity (via lower and upper bounds on outage probability). We then introduce random channels (fading/shadowing) and give TC and outage approximations for an arbitrary channel distribution, as well as exact results for the special cases of Rayleigh and Nakagami fading. We then apply these results to show how TC can be used to better understand scheduling, power control, and the deployment of multiple antennas in a decentralized network. The paper closes by discussing shortcomings in the model as well as future research directions.