Introduction

Abstract

The prevalence of high-performance mobile devices such as smartphones and tablets has brought fundamental changes to existing wireless networks. The growth of bandwidth-intensive multimedia and location-based mobile services has exponentially increased the network congestion and the demand for the wireless spectrum. The high computational complexity and communication overhead associated with conventional centralized resource management methods will no longer be suitable to capture the scale of tomorrow’s wireless networks. As a result, resource management in next-generation networks is shifting from conventional solutions that rely on centralized optimization to self-organizing solutions in which intelligence is distributed across the network devices and nodes. The goal of this book is to demonstrate the effectiveness of matching theory, a Nobel-prize winning operational research framework, for solving the wireless resource allocation problems in a distributed manner. Matching theory has already been widely used to solve challenging economic problems. More recently, matching theory has been shown to have a promising potential for modeling and analyzing wireless resource allocation problems due to three reasons: (1) it offers suitable models that can inherently capture various wireless communication features; (2) the ability to use notions, such as preference relations, that can interpret complex system requirements; (3) it provides low-complexity and near-optimal matching algorithms while guaranteeing the system stability. In order to understand the potential of matching theory for wireless resource management, in this chapter, we start by providing an overview on the recent changes in the wireless landscape which, in turn, motivate the need for distributed, matching-based resource management solutions.