Improving energy efficiency in Green femtocell networks: A hierarchical reinforcement learning framework

IEEE International Conference on Communications

Volumn , Issue , 2013, Pages 2241-2245

  Chen, Xianfu ^a   Zhang, Honggang ^b,c   Chen, Tao ^a   Lasanen, Mika ^a  

^a VTT TECHNICAL RESEARCH CENTRE OF FINLAND   (Finland)

^b UNIVERSITÉ EUROPÉENNE DE BRETAGNE   (France)

^c ZHEJIANG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ENERGY EFFICIENCY; LEARNING ALGORITHMS; REINFORCEMENT LEARNING;

DYNAMIC STRATEGIES; FEMTOCELL NETWORKS; HIERARCHICAL REINFORCEMENT LEARNING; LEARNING PROCEDURES; NUMERICAL RESULTS; STACKELBERG GAMES; STOCHASTIC LEARNING; TRANSMISSION POWER;

FEMTOCELL;

EID: 84891347946     PISSN: 15503607     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICC.2013.6654861     Document Type: Conference Paper

References (13)

1. T. Chen, Y. Yang, H. Zhang, H. Kim, and K. Horneman, Network energy saving technologies for green wireless access networks, IEEE Wireless Commun., vol. 18, no. 5, pp. 30-38, Oct. 2011.
2. V. Chandrasekhar, J. Andrews, and A. Gatherer, Femtocell networks: A survey, IEEE Commun. Mag., vol. 46, no. 9, pp. 59-67, Sep. 2008.
3. R. Xie, F. R. Yu, and H. Ji, Energy-efficient spectrum sharing and power allocation in cognitive radio femtocell networks, in Proc. INFOCOM, Orlando, Florida USA, Mar. 2012.
4. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press, 1998.
5. L. Giupponi, A. M. Galindo-Serrano, and M. Dohle, From cognition to docition: The teaching radio paradigm for distributed & autonomous deployments, Comput. Commun., vol. 33, no. 17, pp. 2015-2020, Nov. 2010.
6. M. Bennis, S. Guruacharya, and D. Niyato, Distributed learning strategies for interference mitigation in femtocell networks, in Proc. GLOBECOM, Houston, Texas, USA, Dec. 2011.
7. C. Long, Q. Zhang, B. Li, H. Yang, and X. Guan, Non-cooperative power control for wireless ad hoc networks with repeated games, IEEE J. Sel. Areas Commun., vol. 25, no. 6, pp. 1101-1112, Aug. 2007.
8. Y. Vorobeychik and S. Singh, Computing Stackelberg equilibria in discounted stochastic games, in Proc. AAAI, Toronto, Canada, Jul. 2012.
9. D. Fudenberg and J. Tirole, Game Theory. Cambridge, MA: MIT Press, 1992.
10. C. J. C. H. Watkins and P. Dayan, -learning, Mach. Learn., vol. 8, no. 3-4, pp. 279-292, 1992.
11. A. Kianercy and A. Galstyan, Dynamics of Boltzmann -learning in two-player two-action games, Phys. Rev. E, vol. 85, no. 4, pp. 041145, Apr. 2012.
12. P. S. Sastry, V. V. Phansalkar, and M. A. L. Thathachar, Decentralized learning of Nash equilibria in multi-person stochastic games with incomplete information, IEEE Trans. Syst., Man, Cybern., vol. 24, no. 5, pp. 769-777, May 1994.
13. A. Jean-Marie and M. Tidball, Adapting behaviors through a learning process, J. Econ. Behav. Organ., vol. 60, no. 3, pp. 399-422, Jul. 2006.