5,338 research outputs found
Robust game-theoretic algorithms for distributed resource allocation in wireless communications
The predominant game-theoretic solutions for distributed rate-maximization algorithms in Gaussian interference channels through optimal power control require perfect channel knowledge, which is not possible in practice due to various reasons, such as estimation errors, feedback quantization and latency between channel estimation and signal transmission. This thesis therefore aims at addressing this issue through the design and analysis of robust gametheoretic algorithms for rate-maximization in Gaussian interference channels in the presence of bounded channel uncertainty. A robust rate-maximization game is formulated for the single-antenna frequency-selective Gaussian interference channel under bounded channel uncertainty. The robust-optimization equilibrium solution for this game is independent of the probability distribution of the channel uncertainty. The existence and uniqueness of the equilibrium are studied and sufficient conditions for the uniqueness of the equilibrium are provided. Distributed algorithms to compute the equilibrium solution are presented and shown to have guaranteed asymptotic convergence when the game has a unique equilibrium. The sum-rate and the price of anarchy at the equilibrium of this game are analyzed for the two-user scenario and shown to improve with increase in channel uncertainty under certain conditions. These results indicate that the robust solution moves closer to a frequency division multiple access (FDMA) solution when uncertainty increases. This leads to a higher sum-rate and a lower price of anarchy for systems where FDMA is globally optimal. A robust rate-maximization game for multi-antenna Gaussian interference channels in the presence of channel uncertainty is also developed along similar principles. It is shown that this robust game is equivalent to the nominal game with modified channel matrices. The robust-optimization equilibrium for this game and a distributed algorithm for its computation are presented and characterized. Sufficient conditions for the uniqueness of the equilibrium and asymptotic convergence of the algorithm are presented. Numerical simulations are used to confirm the behaviour of these algorithms. The analytical and numerical results of this thesis indicate that channel uncertainty is not necessarily detrimental, but can indeed result in improvement of performance of networks in particular situations, where the Nash equilibrium solution is quite inefficient and channel uncertainty leads to reduced greediness of users.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Distributed stochastic optimization via matrix exponential learning
In this paper, we investigate a distributed learning scheme for a broad class
of stochastic optimization problems and games that arise in signal processing
and wireless communications. The proposed algorithm relies on the method of
matrix exponential learning (MXL) and only requires locally computable gradient
observations that are possibly imperfect and/or obsolete. To analyze it, we
introduce the notion of a stable Nash equilibrium and we show that the
algorithm is globally convergent to such equilibria - or locally convergent
when an equilibrium is only locally stable. We also derive an explicit linear
bound for the algorithm's convergence speed, which remains valid under
measurement errors and uncertainty of arbitrarily high variance. To validate
our theoretical analysis, we test the algorithm in realistic
multi-carrier/multiple-antenna wireless scenarios where several users seek to
maximize their energy efficiency. Our results show that learning allows users
to attain a net increase between 100% and 500% in energy efficiency, even under
very high uncertainty.Comment: 31 pages, 3 figure
Defending Elections Against Malicious Spread of Misinformation
The integrity of democratic elections depends on voters' access to accurate
information. However, modern media environments, which are dominated by social
media, provide malicious actors with unprecedented ability to manipulate
elections via misinformation, such as fake news. We study a zero-sum game
between an attacker, who attempts to subvert an election by propagating a fake
new story or other misinformation over a set of advertising channels, and a
defender who attempts to limit the attacker's impact. Computing an equilibrium
in this game is challenging as even the pure strategy sets of players are
exponential. Nevertheless, we give provable polynomial-time approximation
algorithms for computing the defender's minimax optimal strategy across a range
of settings, encompassing different population structures as well as models of
the information available to each player. Experimental results confirm that our
algorithms provide near-optimal defender strategies and showcase variations in
the difficulty of defending elections depending on the resources and knowledge
available to the defender.Comment: Full version of paper accepted to AAAI 201
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
Robust Spectrum Sharing via Worst Case Approach
This paper considers non-cooperative and fully-distributed power-allocation
for secondary-users (SUs) in spectrum-sharing environments when
normalized-interference to each secondary-user 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 set, we show that the power allocation
problem for each SU 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 the social utility may be higher at a RNE as compared to that at the
corresponding NE, and demonstrate that this is caused by SUs' orthogonal
utilization of bandwidth for increasing the social utility. Simulations confirm
our analysis
A stochastic approximation algorithm for stochastic semidefinite programming
Motivated by applications to multi-antenna wireless networks, we propose a
distributed and asynchronous algorithm for stochastic semidefinite programming.
This algorithm is a stochastic approximation of a continous- time matrix
exponential scheme regularized by the addition of an entropy-like term to the
problem's objective function. We show that the resulting algorithm converges
almost surely to an -approximation of the optimal solution
requiring only an unbiased estimate of the gradient of the problem's stochastic
objective. When applied to throughput maximization in wireless multiple-input
and multiple-output (MIMO) systems, the proposed algorithm retains its
convergence properties under a wide array of mobility impediments such as user
update asynchronicities, random delays and/or ergodically changing channels.
Our theoretical analysis is complemented by extensive numerical simulations
which illustrate the robustness and scalability of the proposed method in
realistic network conditions.Comment: 25 pages, 4 figure
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