13,070 research outputs found
Deep Reinforcement Learning for Resource Management in Network Slicing
Network slicing is born as an emerging business to operators, by allowing
them to sell the customized slices to various tenants at different prices. In
order to provide better-performing and cost-efficient services, network slicing
involves challenging technical issues and urgently looks forward to intelligent
innovations to make the resource management consistent with users' activities
per slice. In that regard, deep reinforcement learning (DRL), which focuses on
how to interact with the environment by trying alternative actions and
reinforcing the tendency actions producing more rewarding consequences, is
assumed to be a promising solution. In this paper, after briefly reviewing the
fundamental concepts of DRL, we investigate the application of DRL in solving
some typical resource management for network slicing scenarios, which include
radio resource slicing and priority-based core network slicing, and demonstrate
the advantage of DRL over several competing schemes through extensive
simulations. Finally, we also discuss the possible challenges to apply DRL in
network slicing from a general perspective.Comment: The manuscript has been accepted by IEEE Access in Nov. 201
A Comprehensive Survey of Potential Game Approaches to Wireless Networks
Potential games form a class of non-cooperative games where unilateral
improvement dynamics are guaranteed to converge in many practical cases. The
potential game approach has been applied to a wide range of wireless network
problems, particularly to a variety of channel assignment problems. In this
paper, the properties of potential games are introduced, and games in wireless
networks that have been proven to be potential games are comprehensively
discussed.Comment: 44 pages, 6 figures, to appear in IEICE Transactions on
Communications, vol. E98-B, no. 9, Sept. 201
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
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