An Overview of Local Capacity in Wireless Networks

TL;DR

This paper introduces the local capacity metric for wireless ad hoc networks, compares various medium access schemes, and finds grid patterns are optimal while practical schemes nearly achieve this capacity.

Contribution

It proposes a local capacity framework for evaluating medium access schemes and analyzes their performance, identifying grid patterns as optimal and practical schemes as nearly optimal.

Findings

Grid pattern schemes maximize local capacity.

ALOHA scheme achieves at most half the optimal capacity.

Node coloring and carrier sense schemes are nearly optimal.

Abstract

This article introduces a metric for performance evaluation of medium access schemes in wireless ad hoc networks known as local capacity. Although deriving the end-to-end capacity of wireless ad hoc networks is a difficult problem, the local capacity framework allows us to quantify the average information rate received by a receiver node randomly located in the network. In this article, the basic network model and analytical tools are first discussed and applied to a simple network to derive the local capacity of various medium access schemes. Our goal is to identify the most optimal scheme and also to see how does it compare with more practical medium access schemes. We analyzed grid pattern schemes where simultaneous transmitters are positioned in a regular grid pattern, ALOHA schemes where simultaneous transmitters are dispatched according to a uniform Poisson distribution and exclusion schemes where simultaneous transmitters are dispatched according to an exclusion rule such as node coloring and carrier sense schemes. Our analysis shows that local capacity is optimal when simultaneous transmitters are positioned in a grid pattern based on equilateral triangles and our results show that this optimal local capacity is at most double the local capacity of ALOHA based scheme. Our results also show that node coloring and carrier sense schemes approach the optimal local capacity by an almost negligible difference. At the end, we also discuss the shortcomings in our model as well as future research directions.