  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than others and they may even use outdated measurements of the received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.
