4,233 research outputs found
Let Cognitive Radios Imitate: Imitation-based Spectrum Access for Cognitive Radio Networks
In this paper, we tackle the problem of opportunistic spectrum access in
large-scale cognitive radio networks, where the unlicensed Secondary Users (SU)
access the frequency channels partially occupied by the licensed Primary Users
(PU). Each channel is characterized by an availability probability unknown to
the SUs. We apply evolutionary game theory to model the spectrum access problem
and develop distributed spectrum access policies based on imitation, a behavior
rule widely applied in human societies consisting of imitating successful
behavior. We first develop two imitation-based spectrum access policies based
on the basic Proportional Imitation (PI) rule and the more advanced Double
Imitation (DI) rule given that a SU can imitate any other SUs. We then adapt
the proposed policies to a more practical scenario where a SU can only imitate
the other SUs operating on the same channel. A systematic theoretical analysis
is presented for both scenarios on the induced imitation dynamics and the
convergence properties of the proposed policies to an imitation-stable
equilibrium, which is also the -optimum of the system. Simple,
natural and incentive-compatible, the proposed imitation-based spectrum access
policies can be implemented distributedly based on solely local interactions
and thus is especially suited in decentralized adaptive learning environments
as cognitive radio networks
Boltzmann meets Nash: Energy-efficient routing in optical networks under uncertainty
Motivated by the massive deployment of power-hungry data centers for service
provisioning, we examine the problem of routing in optical networks with the
aim of minimizing traffic-driven power consumption. To tackle this issue,
routing must take into account energy efficiency as well as capacity
considerations; moreover, in rapidly-varying network environments, this must be
accomplished in a real-time, distributed manner that remains robust in the
presence of random disturbances and noise. In view of this, we derive a pricing
scheme whose Nash equilibria coincide with the network's socially optimum
states, and we propose a distributed learning method based on the Boltzmann
distribution of statistical mechanics. Using tools from stochastic calculus, we
show that the resulting Boltzmann routing scheme exhibits remarkable
convergence properties under uncertainty: specifically, the long-term average
of the network's power consumption converges within of its
minimum value in time which is at most ,
irrespective of the fluctuations' magnitude; additionally, if the network
admits a strict, non-mixing optimum state, the algorithm converges to it -
again, no matter the noise level. Our analysis is supplemented by extensive
numerical simulations which show that Boltzmann routing can lead to a
significant decrease in power consumption over basic, shortest-path routing
schemes in realistic network conditions.Comment: 24 pages, 4 figure
The projection dynamic, the replicator dynamic and the geometry of population games
Every population game defines a vector field on the set of strategy distributions X. The
projection dynamic maps each population game to a new vector field: namely, the one closest
to the payoff vector field among those that never point outward from X. We investigate the
geometric underpinnings of the projection dynamic, describe its basic game-theoretic properties,
and establish a number of close connections between the projection dynamic and the replicator
dynamic
A game theoretic approach to a peer-to-peer cloud storage model
Classical cloud storage based on external data providers has been recognized
to suffer from a number of drawbacks. This is due to its inherent centralized
architecture which makes it vulnerable to external attacks, malware, technical
failures, as well to the large premium charged for business purposes. In this
paper, we propose an alternative distributed peer-to-peer cloud storage model
which is based on the observation that the users themselves often have
available storage capabilities to be offered in principle to other users. Our
set-up is that of a network of users connected through a graph, each of them
being at the same time a source of data to be stored externally and a possible
storage resource. We cast the peer-to-peer storage model to a Potential Game
and we propose an original decentralized algorithm which makes units interact,
cooperate, and store a complete back up of their data on their connected
neighbors. We present theoretical results on the algorithm as well a good
number of simulations which validate our approach.Comment: 10 page
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