Game Theory for Signal Processing in Networks

TL;DR

This tutorial introduces fundamental game theory concepts and explores their recent applications in signal processing, emphasizing strategic and coalition games, and their links to distributed optimization and learning algorithms.

Contribution

It consolidates key game-theoretic notions and tools relevant to signal processing, providing detailed descriptions and illustrative examples for the first time in a single comprehensive paper.

Findings

Clarifies the differences between strategic-form and coalition-form games.

Highlights the connections between game theory and distributed optimization.

Provides practical examples for applying game-theoretic tools in signal processing.

Abstract

In this tutorial, the basics of game theory are introduced along with an overview of its most recent and emerging applications in signal processing. One of the main features of this contribution is to gather in a single paper some fundamental game-theoretic notions and tools which, over the past few years, have become widely spread over a large number of papers. In particular, both strategic-form and coalition-form games are described in details while the key connections and differences between them are outlined. Moreover, a particular attention is also devoted to clarify the connections between strategic-form games and distributed optimization and learning algorithms. Beyond an introduction to the basic concepts and main solution approaches, several carefully designed examples are provided to allow a better understanding of how to apply the described tools.