761 research outputs found
A Hierarchical Game with Strategy Evolution for Mobile Sponsored Content and Service Markets
In sponsored content and service markets, the content and service providers
are able to subsidize their target mobile users through directly paying the
mobile network operator, to lower the price of the data/service access charged
by the network operator to the mobile users. The sponsoring mechanism leads to
a surge in mobile data and service demand, which in return compensates for the
sponsoring cost and benefits the content/service providers. In this paper, we
study the interactions among the three parties in the market, namely, the
mobile users, the content/service providers and the network operator, as a
two-level game with multiple Stackelberg (i.e., leader) players. Our study is
featured by the consideration of global network effects owning to consumers'
grouping. Since the mobile users may have bounded rationality, we model the
service-selection process among them as an evolutionary-population follower
sub-game. Meanwhile, we model the pricing-then-sponsoring process between the
content/service providers and the network operator as a non-cooperative
equilibrium searching problem. By investigating the structure of the proposed
game, we reveal a few important properties regarding the equilibrium existence,
and propose a distributed, projection-based algorithm for iterative equilibrium
searching. Simulation results validate the convergence of the proposed
algorithm, and demonstrate how sponsoring helps improve both the providers'
profits and the users' experience
Spectrum Trading: An Abstracted Bibliography
This document contains a bibliographic list of major papers on spectrum
trading and their abstracts. The aim of the list is to offer researchers
entering this field a fast panorama of the current literature. The list is
continually updated on the webpage
\url{http://www.disp.uniroma2.it/users/naldi/Ricspt.html}. Omissions and papers
suggested for inclusion may be pointed out to the authors through e-mail
(\textit{[email protected]})
Leveraging information in vehicular parking games
Our paper approaches the parking assistance service in urban environments as
an instance of service provision in non-cooperative network environments. We
propose normative abstractions for the way drivers pursue parking space and the
way they respond to partial or complete information for parking demand and
supply as well as specific pricing policies on public and private parking
facilities. The drivers are viewed as strategic agents who make rational
decisions attempting to minimize the cost of the acquired parking spot. We
formulate the resulting games as resource selection games and derive their
equilibria under different expressions of uncertainty about the overall parking
demand. The efficiency of the equilibrium states is compared against the
optimal assignment that could be determined by a centralized entity and
conditions are derived for minimizing the related price of anarchy value. Our
results provide useful hints for the pricing and practical management of
on-street and private parking resources. More importantly, they exemplify
counterintuitive less-is-more effects about the way information availability
modulates the service cost, which underpin general competitive service
provision settings and contribute to the better understanding of effective
information mechanisms
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Channel access optimization with adaptive congestion pricing for cognitive vehicular networks: an evolutionary game approach
Cognitive radio-enabled vehicular nodes as unlicensed users can competitively and opportunistically access the radio spectrum provided by a licensed provider and simultaneously use a dedicated channel for vehicular communications. In such cognitive vehicular networks, channel access optimization plays a key role in making the most of the spectrum resources. In this paper, we present the competition among self-interest-driven vehicular nodes as an evolutionary game and study fundamental properties of the Nash equilibrium and the evolutionary stability. To deal with the inefficiency of the Nash equilibrium, we design a delayed pricing mechanism and propose a discretized replicator dynamics with this pricing mechanism. The strategy adaptation and the channel pricing can be performed in an asynchronous manner, such that vehicular users can obtain the knowledge of the channel prices prior to actually making access decisions. We prove that the Nash equilibrium of the proposed evolutionary dynamics is evolutionary stable and coincides with the social optimum. Besides, performance comparison is also carried out in different environments to demonstrate the effectiveness and advantages of our method over the distributed multi-agent reinforcement learning scheme in current literature in terms of the system convergence, stability and adaptability
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