1,817 research outputs found
Applications of Repeated Games in Wireless Networks: A Survey
A repeated game is an effective tool to model interactions and conflicts for
players aiming to achieve their objectives in a long-term basis. Contrary to
static noncooperative games that model an interaction among players in only one
period, in repeated games, interactions of players repeat for multiple periods;
and thus the players become aware of other players' past behaviors and their
future benefits, and will adapt their behavior accordingly. In wireless
networks, conflicts among wireless nodes can lead to selfish behaviors,
resulting in poor network performances and detrimental individual payoffs. In
this paper, we survey the applications of repeated games in different wireless
networks. The main goal is to demonstrate the use of repeated games to
encourage wireless nodes to cooperate, thereby improving network performances
and avoiding network disruption due to selfish behaviors. Furthermore, various
problems in wireless networks and variations of repeated game models together
with the corresponding solutions are discussed in this survey. Finally, we
outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference
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
Design of Two-Level Incentive Mechanisms for Hierarchical Federated Learning
Hierarchical Federated Learning (HFL) is a distributed machine learning
paradigm tailored for multi-tiered computation architectures, which supports
massive access of devices' models simultaneously. To enable efficient HFL, it
is crucial to design suitable incentive mechanisms to ensure that devices
actively participate in local training. However, there are few studies on
incentive mechanism design for HFL. In this paper, we design two-level
incentive mechanisms for the HFL with a two-tiered computing structure to
encourage the participation of entities in each tier in the HFL training. In
the lower-level game, we propose a coalition formation game to joint optimize
the edge association and bandwidth allocation problem, and obtain efficient
coalition partitions by the proposed preference rule, which can be proven to be
stable by exact potential game. In the upper-level game, we design the
Stackelberg game algorithm, which not only determines the optimal number of
edge aggregations for edge servers to maximize their utility, but also optimize
the unit reward provided for the edge aggregation performance to ensure the
interests of cloud servers. Furthermore, numerical results indicate that the
proposed algorithms can achieve better performance than the benchmark schemes
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