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...
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| Veröffentlicht in: | IEEE journal on selected areas in communications Jg. 30; H. 1; S. 198 - 213 |
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| Sprache: | Englisch |
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IEEE
01.01.2012
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers |
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| ISSN: | 0733-8716, 1558-0008 |
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| Abstract | 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). |
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| AbstractList | 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). |
| Author | Tembine, H. Vasilakos, A. V. Khan, M. A. |
| Author_xml | – sequence: 1 givenname: M. A. surname: Khan fullname: Khan, M. A. email: manzoor-ahmed.khan@dai-labor.de organization: Tech. Univ. Berlin, Berlin, Germany – sequence: 2 givenname: H. surname: Tembine fullname: Tembine, H. email: tembine@ieee.org organization: Ecole Super. d'Electricite, Supelec, France – sequence: 3 givenname: A. V. surname: Vasilakos fullname: Vasilakos, A. V. email: vasilako@ath.forthnet.gr organization: Univ. of Western Macedonia, Kozani, Greece |
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| SubjectTerms | 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 |
| Title | Game Dynamics and Cost of Learning in Heterogeneous 4G Networks |
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