2,674 research outputs found
The MIMO Iterative Waterfilling Algorithm
This paper considers the non-cooperative maximization of mutual information
in the vector Gaussian interference channel in a fully distributed fashion via
game theory. This problem has been widely studied in a number of works during
the past decade for frequency-selective channels, and recently for the more
general MIMO case, for which the state-of-the art results are valid only for
nonsingular square channel matrices. Surprisingly, these results do not hold
true when the channel matrices are rectangular and/or rank deficient matrices.
The goal of this paper is to provide a complete characterization of the MIMO
game for arbitrary channel matrices, in terms of conditions guaranteeing both
the uniqueness of the Nash equilibrium and the convergence of asynchronous
distributed iterative waterfilling algorithms. Our analysis hinges on new
technical intermediate results, such as a new expression for the MIMO
waterfilling projection valid (also) for singular matrices, a mean-value
theorem for complex matrix-valued functions, and a general contraction theorem
for the multiuser MIMO watefilling mapping valid for arbitrary channel
matrices. The quite surprising result is that uniqueness/convergence conditions
in the case of tall (possibly singular) channel matrices are more restrictive
than those required in the case of (full rank) fat channel matrices. We also
propose a modified game and algorithm with milder conditions for the uniqueness
of the equilibrium and convergence, and virtually the same performance (in
terms of Nash equilibria) of the original game.Comment: IEEE Transactions on Signal Processing (accepted
Improving Macrocell - Small Cell Coexistence through Adaptive Interference Draining
The deployment of underlay small base stations (SBSs) is expected to
significantly boost the spectrum efficiency and the coverage of next-generation
cellular networks. However, the coexistence of SBSs underlaid to an existing
macro-cellular network faces important challenges, notably in terms of spectrum
sharing and interference management. In this paper, we propose a novel
game-theoretic model that enables the SBSs to optimize their transmission rates
by making decisions on the resource occupation jointly in the frequency and
spatial domains. This procedure, known as interference draining, is performed
among cooperative SBSs and allows to drastically reduce the interference
experienced by both macro- and small cell users. At the macrocell side, we
consider a modified water-filling policy for the power allocation that allows
each macrocell user (MUE) to focus the transmissions on the degrees of freedom
over which the MUE experiences the best channel and interference conditions.
This approach not only represents an effective way to decrease the received
interference at the MUEs but also grants the SBSs tier additional transmission
opportunities and allows for a more agile interference management. Simulation
results show that the proposed approach yields significant gains at both
macrocell and small cell tiers, in terms of average achievable rate per user,
reaching up to 37%, relative to the non-cooperative case, for a network with
150 MUEs and 200 SBSs
Energy-Efficient Resource Allocation in Multiuser MIMO Systems: A Game-Theoretic Framework
This paper focuses on the cross-layer issue of resource allocation for energy
efficiency in the uplink of a multiuser MIMO wireless communication system.
Assuming that all of the transmitters and the uplink receiver are equipped with
multiple antennas, the situation considered is that in which each terminal is
allowed to vary its transmit power, beamforming vector, and uplink receiver in
order to maximize its own utility, which is defined as the ratio of data
throughput to transmit power; the case in which non-linear interference
cancellation is used at the receiver is also investigated. Applying a
game-theoretic formulation, several non-cooperative games for utility
maximization are thus formulated, and their performance is compared in terms of
achieved average utility, achieved average SINR and average transmit power at
the Nash equilibrium. Numerical results show that the use of the proposed
cross-layer resource allocation policies brings remarkable advantages to the
network performance.Comment: Proceedings of the 16th European Signal Processing Conference,
Lausanne, Switzerland, August 25-29, 200
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
Power Allocation Games in Wireless Networks of Multi-antenna Terminals
We consider wireless networks that can be modeled by multiple access channels
in which all the terminals are equipped with multiple antennas. The propagation
model used to account for the effects of transmit and receive antenna
correlations is the unitary-invariant-unitary model, which is one of the most
general models available in the literature. In this context, we introduce and
analyze two resource allocation games. In both games, the mobile stations
selfishly choose their power allocation policies in order to maximize their
individual uplink transmission rates; in particular they can ignore some
specified centralized policies. In the first game considered, the base station
implements successive interference cancellation (SIC) and each mobile station
chooses his best space-time power allocation scheme; here, a coordination
mechanism is used to indicate to the users the order in which the receiver
applies SIC. In the second framework, the base station is assumed to implement
single-user decoding. For these two games a thorough analysis of the Nash
equilibrium is provided: the existence and uniqueness issues are addressed; the
corresponding power allocation policies are determined by exploiting random
matrix theory; the sum-rate efficiency of the equilibrium is studied
analytically in the low and high signal-to-noise ratio regimes and by
simulations in more typical scenarios. Simulations show that, in particular,
the sum-rate efficiency is high for the type of systems investigated and the
performance loss due to the use of the proposed suboptimum coordination
mechanism is very small
Jamming Games in the MIMO Wiretap Channel With an Active Eavesdropper
This paper investigates reliable and covert transmission strategies in a
multiple-input multiple-output (MIMO) wiretap channel with a transmitter,
receiver and an adversarial wiretapper, each equipped with multiple antennas.
In a departure from existing work, the wiretapper possesses a novel capability
to act either as a passive eavesdropper or as an active jammer, under a
half-duplex constraint. The transmitter therefore faces a choice between
allocating all of its power for data, or broadcasting artificial interference
along with the information signal in an attempt to jam the eavesdropper
(assuming its instantaneous channel state is unknown). To examine the resulting
trade-offs for the legitimate transmitter and the adversary, we model their
interactions as a two-person zero-sum game with the ergodic MIMO secrecy rate
as the payoff function. We first examine conditions for the existence of
pure-strategy Nash equilibria (NE) and the structure of mixed-strategy NE for
the strategic form of the game.We then derive equilibrium strategies for the
extensive form of the game where players move sequentially under scenarios of
perfect and imperfect information. Finally, numerical simulations are presented
to examine the equilibrium outcomes of the various scenarios considered.Comment: 27 pages, 8 figures. To appear, IEEE Transactions on Signal
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