NDLI: Bayesian equilibria in slow fading OFDM systems with partial channel state information

Content Provider	IEEE Xplore Digital Library
Author	Akbarzadeh, S. Cottatellucci, L. Bonnet, C.
Copyright Year	2010
Description	Author affiliation: Eurecom, 2229 Route des Cretes, 06904 Sophia Antipolis Cedex, France (Akbarzadeh, S.; Cottatellucci, L.; Bonnet, C.)
Abstract	We consider a slow frequency selective fading multiple access channel (MAC) where 2 independent transmitters are simultaneously communicating with a receiver using orthogonal frequency division multiplexing (OFDM) over N subcarriers. Each transmitter has partial knowledge of the channel state. In such a context, the system is inherently impaired by a nonzero outage probability. We propose a low complexity distributed algorithm for joint rate and power allocation aiming at maximizing the individual throughput, defined as the successfully-received-information rate, under a power constraint. As well known, the problem at hand is non-convex with exponential complexity in the number of transmitters and subcarriers. Inspired by effective almost optimum recent results using the duality principle, we propose a low complexity distributed algorithm based on Bayesian games and duality. We show that the Bayesian game boils down to a two-level game, referred to as per-subcarrier game and global game. The per-subcarrier game reduces to the solution of linear system of equations while the global game boils down to the solution of several constrained submodular games. The provided algorithm determines all the possible Nash equilibria of the game, if they exist.
Starting Page	1
Ending Page	16
File Size	266557
Page Count	16
File Format	PDF
e-ISBN	9781905824182
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-16
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	IIMC
Subject Keyword	Transmitters OFDM Bayesian methods Games Nash equilibrium Bayesian Games Complexity theory Resource management Optimization
Content Type	Text
Resource Type	Article

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