10,288 research outputs found
On the Two-user Multi-carrier Joint Channel Selection and Power Control Game
In this paper, we propose a hierarchical game approach to model the energy
efficiency maximization problem where transmitters individually choose their
channel assignment and power control. We conduct a thorough analysis of the
existence, uniqueness and characterization of the Stackelberg equilibrium.
Interestingly, we formally show that a spectrum orthogonalization naturally
occurs when users decide sequentially about their transmitting carriers and
powers, delivering a binary channel assignment. Both analytical and simulation
results are provided for assessing and improving the performances in terms of
energy efficiency and spectrum utilization between the simultaneous-move game
(with synchronous decision makers), the social welfare (in a centralized
manner) and the proposed Stackelberg (hierarchical) game. For the first time,
we provide tight closed-form bounds on the spectral efficiency of such a model,
including correlation across carriers and users. We show that the spectrum
orthogonalization capability induced by the proposed hierarchical game model
enables the wireless network to achieve the spectral efficiency improvement
while still enjoying a high energy efficiency.Comment: 31 pages, 13 figures, accepted in IEEE Transactions on Communication
Joint Channel Selection and Power Control in Infrastructureless Wireless Networks: A Multi-Player Multi-Armed Bandit Framework
This paper deals with the problem of efficient resource allocation in dynamic
infrastructureless wireless networks. Assuming a reactive interference-limited
scenario, each transmitter is allowed to select one frequency channel (from a
common pool) together with a power level at each transmission trial; hence, for
all transmitters, not only the fading gain, but also the number of interfering
transmissions and their transmit powers are varying over time. Due to the
absence of a central controller and time-varying network characteristics, it is
highly inefficient for transmitters to acquire global channel and network
knowledge. Therefore a reasonable assumption is that transmitters have no
knowledge of fading gains, interference, and network topology. Each
transmitting node selfishly aims at maximizing its average reward (or
minimizing its average cost), which is a function of the action of that
specific transmitter as well as those of all other transmitters. This scenario
is modeled as a multi-player multi-armed adversarial bandit game, in which
multiple players receive an a priori unknown reward with an arbitrarily
time-varying distribution by sequentially pulling an arm, selected from a known
and finite set of arms. Since players do not know the arm with the highest
average reward in advance, they attempt to minimize their so-called regret,
determined by the set of players' actions, while attempting to achieve
equilibrium in some sense. To this end, we design in this paper two joint power
level and channel selection strategies. We prove that the gap between the
average reward achieved by our approaches and that based on the best fixed
strategy converges to zero asymptotically. Moreover, the empirical joint
frequencies of the game converge to the set of correlated equilibria. We
further characterize this set for two special cases of our designed game
Channel Selection for Network-assisted D2D Communication via No-Regret Bandit Learning with Calibrated Forecasting
We consider the distributed channel selection problem in the context of
device-to-device (D2D) communication as an underlay to a cellular network.
Underlaid D2D users communicate directly by utilizing the cellular spectrum but
their decisions are not governed by any centralized controller. Selfish D2D
users that compete for access to the resources construct a distributed system,
where the transmission performance depends on channel availability and quality.
This information, however, is difficult to acquire. Moreover, the adverse
effects of D2D users on cellular transmissions should be minimized. In order to
overcome these limitations, we propose a network-assisted distributed channel
selection approach in which D2D users are only allowed to use vacant cellular
channels. This scenario is modeled as a multi-player multi-armed bandit game
with side information, for which a distributed algorithmic solution is
proposed. The solution is a combination of no-regret learning and calibrated
forecasting, and can be applied to a broad class of multi-player stochastic
learning problems, in addition to the formulated channel selection problem.
Analytically, it is established that this approach not only yields vanishing
regret (in comparison to the global optimal solution), but also guarantees that
the empirical joint frequencies of the game converge to the set of correlated
equilibria.Comment: 31 pages (one column), 9 figure
Non-atomic Games for Multi-User Systems
In this contribution, the performance of a multi-user system is analyzed in
the context of frequency selective fading channels. Using game theoretic tools,
a useful framework is provided in order to determine the optimal power
allocation when users know only their own channel (while perfect channel state
information is assumed at the base station). We consider the realistic case of
frequency selective channels for uplink CDMA. This scenario illustrates the
case of decentralized schemes, where limited information on the network is
available at the terminal. Various receivers are considered, namely the Matched
filter, the MMSE filter and the optimum filter. The goal of this paper is to
derive simple expressions for the non-cooperative Nash equilibrium as the
number of mobiles becomes large and the spreading length increases. To that end
two asymptotic methodologies are combined. The first is asymptotic random
matrix theory which allows us to obtain explicit expressions of the impact of
all other mobiles on any given tagged mobile. The second is the theory of
non-atomic games which computes good approximations of the Nash equilibrium as
the number of mobiles grows.Comment: 17 pages, 4 figures, submitted to IEEE JSAC Special Issue on ``Game
Theory in Communication Systems'
"To sense" or "not to sense" in energy-efficient power control games
A network of cognitive transmitters is considered. Each transmitter has to
decide his power control policy in order to maximize energy-efficiency of his
transmission. For this, a transmitter has two actions to take. He has to decide
whether to sense the power levels of the others or not (which corresponds to a
finite sensing game), and to choose his transmit power level for each block
(which corresponds to a compact power control game). The sensing game is shown
to be a weighted potential game and its set of correlated equilibria is
studied. Interestingly, it is shown that the general hybrid game where each
transmitter can jointly choose the hybrid pair of actions (to sense or not to
sense, transmit power level) leads to an outcome which is worse than the one
obtained by playing the sensing game first, and then playing the power control
game. This is an interesting Braess-type paradox to be aware of for
energy-efficient power control in cognitive networks.Comment: Proc. of the 2nd International Conference on Game Theory for Network
(GAMENETS), 201
Resource Allocation in a MAC with and without security via Game Theoretic Learning
In this paper a -user fading multiple access channel with and without
security constraints is studied. First we consider a F-MAC without the security
constraints. Under the assumption of individual CSI of users, we propose the
problem of power allocation as a stochastic game when the receiver sends an ACK
or a NACK depending on whether it was able to decode the message or not. We
have used Multiplicative weight no-regret algorithm to obtain a Coarse
Correlated Equilibrium (CCE). Then we consider the case when the users can
decode ACK/NACK of each other. In this scenario we provide an algorithm to
maximize the weighted sum-utility of all the users and obtain a Pareto optimal
point. PP is socially optimal but may be unfair to individual users. Next we
consider the case where the users can cooperate with each other so as to
disagree with the policy which will be unfair to individual user. We then
obtain a Nash bargaining solution, which in addition to being Pareto optimal,
is also fair to each user.
Next we study a -user fading multiple access wiretap Channel with CSI of
Eve available to the users. We use the previous algorithms to obtain a CCE, PP
and a NBS.
Next we consider the case where each user does not know the CSI of Eve but
only its distribution. In that case we use secrecy outage as the criterion for
the receiver to send an ACK or a NACK. Here also we use the previous algorithms
to obtain a CCE, PP or a NBS. Finally we show that our algorithms can be
extended to the case where a user can transmit at different rates. At the end
we provide a few examples to compute different solutions and compare them under
different CSI scenarios.Comment: 27 pages, 12 figures. Part of the paper was presented in 2016 IEEE
Information theory and applicaitons (ITA) Workshop, San Diego, USA in Feb.
2016. Submitted to journa
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