33,137 research outputs found
MIMO-OFDM Based Energy Harvesting Cooperative Communications Using Coalitional Game Algorithm
This document is the Accepted Manuscript version. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, we consider the problem of cooperative communication between relays and base station in an advanced MIMO-OFDM framework, under the assumption that the relays are supplied by electric power drawn from energy harvesting (EH) sources. In particular, we focus on the relay selection, with the goal to guarantee the required performance in terms of capacity. In order to maximize the data throughput under the EH constraint, we model the transmission scheme as a non-transferable coalition formation game, with characteristic function based on an approximated capacity expression. Then, we introduce a powerful mathematical tool inherent to coalitional game theory, namely: the Shapley value (Sv) to provide a reliable solution concept to the game. The selected relays will form a virtual dynamically-configuredMIMO network that is able to transmit data to destination using efficient space-time coding techniques. Numerical results, obtained by simulating the EH-powered cooperativeMIMO-OFDMtransmission with Algebraic Space-Time Coding (ASTC), prove that the proposed coalitional game-based relay selection allows to achieve performance very close to that obtained by the same system operated by guaranteed power supply. The proposed methodology is finally compared with some recent related state-of-the-art techniques showing clear advantages in terms of link performance and goodput.Peer reviewe
Cooperation under Interval Uncertainty
Classification: JEL code C71Cooperative game theory;Interval uncertainty;Core;Value;Balancedness
Cooperation driven by mutations in multi-person Prisoner's Dilemma
The n-person Prisoner's Dilemma is a widely used model for populations where
individuals interact in groups. The evolutionary stability of populations has
been analysed in the literature for the case where mutations in the population
may be considered as isolated events. For this case, and assuming simple
trigger strategies and many iterations per game, we analyse the rate of
convergence to the evolutionarily stable populations. We find that for some
values of the payoff parameters of the Prisoner's Dilemma this rate is so low
that the assumption, that mutations in the population are infrequent on that
timescale, is unreasonable. Furthermore, the problem is compounded as the group
size is increased. In order to address this issue, we derive a deterministic
approximation of the evolutionary dynamics with explicit, stochastic mutation
processes, valid when the population size is large. We then analyse how the
evolutionary dynamics depends on the following factors: mutation rate, group
size, the value of the payoff parameters, and the structure of the initial
population. In order to carry out the simulations for groups of more than just
a few individuals, we derive an efficient way of calculating the fitness
values. We find that when the mutation rate per individual and generation is
very low, the dynamics is characterised by populations which are evolutionarily
stable. As the mutation rate is increased, other fixed points with a higher
degree of cooperation become stable. For some values of the payoff parameters,
the system is characterised by (apparently) stable limit cycles dominated by
cooperative behaviour. The parameter regions corresponding to high degree of
cooperation grow in size with the mutation rate, and in number with the group
size.Comment: 22 pages, 7 figures. Accepted for publication in Journal of
Theoretical Biolog
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
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