289 research outputs found
Coalitional Game Theoretic Approach for Cooperative Transmission in Vehicular Networks
Cooperative transmission in vehicular networks is studied by using
coalitional game and pricing in this paper. There are several vehicles and
roadside units (RSUs) in the networks. Each vehicle has a desire to transmit
with a certain probability, which represents its data burtiness. The RSUs can
enhance the vehicles' transmissions by cooperatively relaying the vehicles'
data. We consider two kinds of cooperations: cooperation among the vehicles and
cooperation between the vehicle and RSU. First, vehicles cooperate to avoid
interfering transmissions by scheduling the transmissions of the vehicles in
each coalition. Second, a RSU can join some coalition to cooperate the
transmissions of the vehicles in that coalition. Moreover, due to the mobility
of the vehicles, we introduce the notion of encounter between the vehicle and
RSU to indicate the availability of the relay in space. To stimulate the RSU's
cooperative relaying for the vehicles, the pricing mechanism is applied. A
non-transferable utility (NTU) game is developed to analyze the behaviors of
the vehicles and RSUs. The stability of the formulated game is studied.
Finally, we present and discuss the numerical results for the 2-vehicle and
2-RSU scenario, and the numerical results verify the theoretical analysis.Comment: accepted by IEEE ICC'1
A Cooperative Bayesian Nonparametric Framework for Primary User Activity Monitoring in Cognitive Radio Network
This paper introduces a novel approach that enables a number of cognitive
radio devices that are observing the availability pattern of a number of
primary users(PUs), to cooperate and use \emph{Bayesian nonparametric}
techniques to estimate the distributions of the PUs' activity pattern, assumed
to be completely unknown. In the proposed model, each cognitive node may have
its own individual view on each PU's distribution, and, hence, seeks to find
partners having a correlated perception. To address this problem, a coalitional
game is formulated between the cognitive devices and an algorithm for
cooperative coalition formation is proposed. It is shown that the proposed
coalition formation algorithm allows the cognitive nodes that are experiencing
a similar behavior from some PUs to self-organize into disjoint, independent
coalitions. Inside each coalition, the cooperative cognitive nodes use a
combination of Bayesian nonparametric models such as the Dirichlet process and
statistical goodness of fit techniques in order to improve the accuracy of the
estimated PUs' activity distributions. Simulation results show that the
proposed algorithm significantly improves the estimates of the PUs'
distributions and yields a performance advantage, in terms of reduction of the
average achieved Kullback-Leibler distance between the real and the estimated
distributions, reaching up to 36.5% relative the non-cooperative estimates. The
results also show that the proposed algorithm enables the cognitive nodes to
adapt their cooperative decisions when the actual PUs' distributions change due
to, for example, PU mobility.Comment: IEEE Journal on Selected Areas in Communications (JSAC), to appear,
201
Game theory for collaboration in future networks
Cooperative strategies have the great potential of improving network performance and spectrum utilization in future networking environments. This new paradigm in terms of network management, however, requires a novel design and analysis framework targeting a highly flexible networking solution with a distributed architecture. Game Theory is very suitable for this task, since it is a comprehensive mathematical tool for modeling the highly complex interactions among distributed and intelligent decision makers. In this way, the more convenient management policies for the diverse players (e.g. content providers, cloud providers, home providers, brokers, network providers or users) should be found to optimize the performance of the overall network infrastructure. The authors discuss in this chapter several Game Theory models/concepts that are highly relevant for enabling collaboration among the diverse players, using different ways to incentivize it, namely through pricing or reputation. In addition, the authors highlight several related open problems, such as the lack of proper models for dynamic and incomplete information games in this area.info:eu-repo/semantics/acceptedVersio
Hedonic Coalition Formation for Distributed Task Allocation among Wireless Agents
Autonomous wireless agents such as unmanned aerial vehicles or mobile base
stations present a great potential for deployment in next-generation wireless
networks. While current literature has been mainly focused on the use of agents
within robotics or software applications, we propose a novel usage model for
self-organizing agents suited to wireless networks. In the proposed model, a
number of agents are required to collect data from several arbitrarily located
tasks. Each task represents a queue of packets that require collection and
subsequent wireless transmission by the agents to a central receiver. The
problem is modeled as a hedonic coalition formation game between the agents and
the tasks that interact in order to form disjoint coalitions. Each formed
coalition is modeled as a polling system consisting of a number of agents which
move between the different tasks present in the coalition, collect and transmit
the packets. Within each coalition, some agents can also take the role of a
relay for improving the packet success rate of the transmission. The proposed
algorithm allows the tasks and the agents to take distributed decisions to join
or leave a coalition, based on the achieved benefit in terms of effective
throughput, and the cost in terms of delay. As a result of these decisions, the
agents and tasks structure themselves into independent disjoint coalitions
which constitute a Nash-stable network partition. Moreover, the proposed
algorithm allows the agents and tasks to adapt the topology to environmental
changes such as the arrival/removal of tasks or the mobility of the tasks.
Simulation results show how the proposed algorithm improves the performance, in
terms of average player (agent or task) payoff, of at least 30.26% (for a
network of 5 agents with up to 25 tasks) relatively to a scheme that allocates
nearby tasks equally among agents.Comment: to appear, IEEE Transactions on Mobile Computin
Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory
Recently, there has been a growing trend toward ap-plying game theory (GT) to various engineering fields in order to solve optimization problems with different competing entities/con-tributors/players. Researches in the fourth generation (4G) wireless network field also exploited this advanced theory to overcome long term evolution (LTE) challenges such as resource allocation, which is one of the most important research topics. In fact, an efficient de-sign of resource allocation schemes is the key to higher performance. However, the standard does not specify the optimization approach to execute the radio resource management and therefore it was left open for studies. This paper presents a survey of the existing game theory based solution for 4G-LTE radio resource allocation problem and its optimization
Game theory for cooperation in multi-access edge computing
Cooperative strategies amongst network players can improve network performance and spectrum utilization in future networking environments. Game Theory is very suitable for these emerging scenarios, since it models high-complex interactions among distributed decision makers. It also finds the more convenient management policies for the diverse players (e.g., content providers, cloud providers, edge providers, brokers, network providers, or users). These management policies optimize the performance of the overall network infrastructure with a fair utilization of their resources. This chapter discusses relevant theoretical models that enable cooperation amongst the players in distinct ways through, namely, pricing or reputation. In addition, the authors highlight open problems, such as the lack of proper models for dynamic and incomplete information scenarios. These upcoming scenarios are associated to computing and storage at the network edge, as well as, the deployment of large-scale IoT systems. The chapter finalizes by discussing a business model for future networks.info:eu-repo/semantics/acceptedVersio
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