Game Dynamics and Cost of Learning in Heterogeneous 4G Networks

In this paper, we study game dynamics and learning schemes for heterogeneous 4G networks. We introduce a novel learning scheme called cost-to-learn that incorporates the cost to switch, the switching delay, and the cost of changing to a new action and, captures the realistic behavior of the users th...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE journal on selected areas in communications Ročník 30; číslo 1; s. 198 - 213
Hlavní autori: Khan, M. A., Tembine, H., Vasilakos, A. V.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.01.2012
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Predmet:
Computer Science
Computer simulation
cost of learning
Dynamics
Estimation
Game dynamics
Games
heterogeneous 4G networks
Heuristic algorithms
Learning
Nash equilibrium
Networking and Internet Architecture
Networks
Noise measurement
Robustness
strategic learning
Strategy
Studies
Switching theory
Wireless communication
ISSN:0733-8716, 1558-0008
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:In this paper, we study game dynamics and learning schemes for heterogeneous 4G networks. We introduce a novel learning scheme called cost-to-learn that incorporates the cost to switch, the switching delay, and the cost of changing to a new action and, captures the realistic behavior of the users that we have experimented on OPNET simulations. Considering a dynamic and uncertain environment where the users and operators have only a numerical value of their own payoffs as information, we construct various heterogeneous combined fully distributed payoff and strategy reinforcement learning (CODIPAS-RL): the users try to learn their own optimal payoff and their optimal strategy simultaneously. We establish the asymptotic pseudo-trajectories as solution of differential equations. Using evolutionary game dynamics, we prove the convergence and stability properties in specific classes of dynamic robust games. We provide various numerical examples and OPNET simulations in the context network selection in wireless local area networks (WLAN) and Long Term Evolution (LTE).
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Article-2
ObjectType-Feature-1
content type line 23
ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2012.120118