3,844 research outputs found
Closed form solutions for symmetric water filling games
We study power control in optimization and game frameworks. In the
optimization framework there is a single decision maker who assigns network
resources and in the game framework users share the network resources according
to Nash equilibrium. The solution of these problems is based on so-called
water-filling technique, which in turn uses bisection method for solution of
non-linear equations for Lagrange multiplies. Here we provide a closed form
solution to the water-filling problem, which allows us to solve it in a finite
number of operations. Also, we produce a closed form solution for the Nash
equilibrium in symmetric Gaussian interference game with an arbitrary number of
users. Even though the game is symmetric, there is an intrinsic hierarchical
structure induced by the quantity of the resources available to the users. We
use this hierarchical structure to perform a successive reduction of the game.
In addition, to its mathematical beauty, the explicit solution allows one to
study limiting cases when the crosstalk coefficient is either small or large.
We provide an alternative simple proof of the convergence of the Iterative
Water Filling Algorithm. Furthermore, it turns out that the convergence of
Iterative Water Filling Algorithm slows down when the crosstalk coefficient is
large. Using the closed form solution, we can avoid this problem. Finally, we
compare the non-cooperative approach with the cooperative approach and show
that the non-cooperative approach results in a more fair resource distribution
Closed form solutions for symmetric water filling games
We study power control in optimization and game frameworks. In the optimization framework there is a single decision maker who assigns network resources and in the game framework users share the network resources according to Nash equilibrium. The solution of these problems is based on so-called water-filling technique, which in turn uses bisection method for solution of non-linear equations for Lagrange multiplies. Here we provide a closed form solution to the water-filling problem, which allows us to solve it in a finite number of operations. Also, we produce a closed form solution for the Nash equilibrium in symmetric Gaussian interference game with an arbitrary number of users. Even though the game is symmetric, there is an intrinsic hierarchical structure induced by the quantity of the resources available to the users. We use this hierarchical structure to perform a successive reduction of the game. In addition, to its mathematical beauty, the explicit solution allows one to study limiting cases when the crosstalk coefficient is either small or large. We provide an alternative simple proof of the convergence of the Iterative Water Filling Algorithm. Furthermore, it turns out that the convergence of Iterative Water Filling Algorithm slows down when the crosstalk coefficient is large. Using the closed form solution, we can avoid this problem. Finally, we compare the non-cooperative approach with the cooperative approach and show that the non-cooperative approach results in a more fair resource distribution
Worst-Case Robust Distributed Power Allocation in Shared Unlicensed Spectrum
This paper considers non-cooperative and fully-distributed power-allocation
for selfish transmitter-receiver pairs in shared unlicensed spectrum when
normalized-interference to each receiver is uncertain. We model each uncertain
parameter by the sum of its nominal (estimated) value and a bounded additive
error in a convex set, and show that the allocated power always converges to
its equilibrium, called robust Nash equilibrium (RNE). In the case of a bounded
and symmetric uncertainty region, we show that the power allocation problem for
each user is simplified, and can be solved in a distributed manner. We derive
the conditions for RNE's uniqueness and for convergence of the distributed
algorithm; and show that the total throughput (social utility) is less than
that at NE when RNE is unique. We also show that for multiple RNEs, the social
utility may be higher at a RNE as compared to that at the corresponding NE, and
demonstrate that this is caused by users' orthogonal utilization of bandwidth
at RNE. Simulations confirm our analysis
Competitive Spectrum Management with Incomplete Information
This paper studies an interference interaction (game) between selfish and
independent wireless communication systems in the same frequency band. Each
system (player) has incomplete information about the other player's channel
conditions. A trivial Nash equilibrium point in this game is where players
mutually full spread (FS) their transmit spectrum and interfere with each
other. This point may lead to poor spectrum utilization from a global network
point of view and even for each user individually.
In this paper, we provide a closed form expression for a non pure-FS
epsilon-Nash equilibrium point; i.e., an equilibrium point where players choose
FDM for some channel realizations and FS for the others. We show that operating
in this non pure-FS epsilon-Nash equilibrium point increases each user's
throughput and therefore improves the spectrum utilization, and demonstrate
that this performance gain can be substantial. Finally, important insights are
provided into the behaviour of selfish and rational wireless users as a
function of the channel parameters such as fading probabilities, the
interference-to-signal ratio
Coalitional Games for Transmitter Cooperation in MIMO Multiple Access Channels
Cooperation between nodes sharing a wireless channel is becoming increasingly
necessary to achieve performance goals in a wireless network. The problem of
determining the feasibility and stability of cooperation between rational nodes
in a wireless network is of great importance in understanding cooperative
behavior. This paper addresses the stability of the grand coalition of
transmitters signaling over a multiple access channel using the framework of
cooperative game theory. The external interference experienced by each TX is
represented accurately by modeling the cooperation game between the TXs in
\emph{partition form}. Single user decoding and successive interference
cancelling strategies are examined at the receiver. In the absence of
coordination costs, the grand coalition is shown to be \emph{sum-rate optimal}
for both strategies. Transmitter cooperation is \emph{stable}, if and only if
the core of the game (the set of all divisions of grand coalition utility such
that no coalition deviates) is nonempty. Determining the stability of
cooperation is a co-NP-complete problem in general. For a single user decoding
receiver, transmitter cooperation is shown to be \emph{stable} at both high and
low SNRs, while for an interference cancelling receiver with a fixed decoding
order, cooperation is stable only at low SNRs and unstable at high SNR. When
time sharing is allowed between decoding orders, it is shown using an
approximate lower bound to the utility function that TX cooperation is also
stable at high SNRs. Thus, this paper demonstrates that ideal zero cost TX
cooperation over a MAC is stable and improves achievable rates for each
individual user.Comment: in review for publication in IEEE Transactions on Signal Processin
Real and Complex Monotone Communication Games
Noncooperative game-theoretic tools have been increasingly used to study many
important resource allocation problems in communications, networking, smart
grids, and portfolio optimization. In this paper, we consider a general class
of convex Nash Equilibrium Problems (NEPs), where each player aims to solve an
arbitrary smooth convex optimization problem. Differently from most of current
works, we do not assume any specific structure for the players' problems, and
we allow the optimization variables of the players to be matrices in the
complex domain. Our main contribution is the design of a novel class of
distributed (asynchronous) best-response- algorithms suitable for solving the
proposed NEPs, even in the presence of multiple solutions. The new methods,
whose convergence analysis is based on Variational Inequality (VI) techniques,
can select, among all the equilibria of a game, those that optimize a given
performance criterion, at the cost of limited signaling among the players. This
is a major departure from existing best-response algorithms, whose convergence
conditions imply the uniqueness of the NE. Some of our results hinge on the use
of VI problems directly in the complex domain; the study of these new kind of
VIs also represents a noteworthy innovative contribution. We then apply the
developed methods to solve some new generalizations of SISO and MIMO games in
cognitive radios and femtocell systems, showing a considerable performance
improvement over classical pure noncooperative schemes.Comment: to appear on IEEE Transactions in Information Theor
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