Asynchronous Iterative Waterfilling for Gaussian Frequency-Selective Interference Channels

TL;DR

This paper introduces an asynchronous iterative waterfilling algorithm for Gaussian frequency-selective interference channels, enabling decentralized power allocation that converges to a unique Nash equilibrium without user cooperation.

Contribution

It proposes a novel distributed asynchronous algorithm for power control in interference channels, generalizing existing waterfilling methods and providing convergence guarantees.

Findings

Algorithm converges to the unique Nash equilibrium.

Unified framework encompasses known iterative waterfilling algorithms.

Conditions for global convergence are established.

Abstract

This paper considers the maximization of information rates for the Gaussian frequency-selective interference channel, subject to power and spectral mask constraints on each link. To derive decentralized solutions that do not require any cooperation among the users, the optimization problem is formulated as a static noncooperative game of complete information. To achieve the so-called Nash equilibria of the game, we propose a new distributed algorithm called asynchronous iterative waterfilling algorithm. In this algorithm, the users update their power spectral density in a completely distributed and asynchronous way: some users may update their power allocation more frequently than others and they may even use outdated measurements of the received interference. The proposed algorithm represents a unified framework that encompasses and generalizes all known iterative waterfilling algorithms, e.g., sequential and simultaneous versions. The main result of the paper consists of a unified set of conditions that guarantee the global converge of the proposed algorithm to the (unique) Nash equilibrium of the game.