Decentralized cross-tier interference mitigation in cognitive femtocell networks

IEEE International Conference on Communications

Volumn , Issue , 2011, Pages

  Bennis, M ^a   Perlaza, S M ^b  

^a UNIVERSITY OF OULU   (Finland)

^b ORANGE LABS   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE COMMUNICATIONS; FEMTOCELL; INTERFERENCE MITIGATION; MACRO CELLS; NUMERICAL RESULTS; POWER LEVELS; SIGNAL TO INTERFERENCE PLUS NOISE RATIO; STOCHASTIC APPROXIMATIONS;

ALGORITHMS; APPROXIMATION THEORY; BASE STATIONS; FREQUENCY BANDS; GAME THEORY; MOBILE TELECOMMUNICATION SYSTEMS;

PROBABILITY DISTRIBUTIONS;

EID: 80052138497     PISSN: 05361486     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/icc.2011.5962649     Document Type: Conference Paper

References (14)

1. V. Chandrasekhar and J. G. Andrews, "Femtocell networks: A survey," IEEE Communication Magazine, 46(9): 59-67, September 2008.
2. D. Niyato and E. Hossain, "Competitive spectrum sharing in cognitive radio networks: A dynamic game approach," IEEE Transactions on Wireless Communications, vol. 7, no. 7, 2651-2660, July 2008.
3. D. Niyato and E. Hossain, "Dynamic of network selection in heterogeneous wireless networks: an evolutionary game approach," IEEE Transactions on Vehicular Communications, vol. 58, no. 4, 2651-2660, May 2009.
4. D. Fudenberg and D. K. Levine, "The Theory of Learning in Games," The MIT Press, Cambridge, MA, 1998.
5. H. Li, "Multi-agent Q-Learning of Channel Selection in Multi-user Cognitive Radio Systems: A Two by Two Case," IEEE Conference on System, Man and Cybernetics (SMC), 2009.
6. M. Bennis and D. Niyato, "A Q-learning based approach to interference avoidance in self-organized femtocell networks," IEEE FEMnet (colocated with IEEE GLOBECOM), Miami, USA, Dec. 2010.
7. M. Nazir, M. Bennis, K. Ghaboosi, A. Mackenzie and M. Latva-aho, "Learning based mechanisms for interference mitigation in self-organized femtocell networks," IEEE ASILOMAR, Pacific Grove, USA, Nov. 2010.
8. Y. M. Kaniovski and H. P. Young, "Learning dynamics in games with stochastic perturbations," Games and Economic Behavior, vol. 11, no. 2, pp. 330-363, Nov. 1995.
9. S. M. Perlaza, H. Tembine and S. Lasaulce, "How can ignorant but patient cognitive terminals learn their strategy and utility?," in proc. of the IEEE Intl. Workshop on Signal Processing Advances for Wireless Communications (SPAWC), Marrakesh, Morroco, June 2010.
10. S. M. Perlaza, H. Tembine, S. Lasaulce and M. Debbah, "Radio resource sharing in decentralized wireless networks: a dynamical system approach," submitted to IEEE Transaction in Signal Processing, 2010.
11. J. F. Nash, "Equilibrium points in n-person games," National Academy of Sciences of the United States of America, 1950.
12. V. S. Borkar, "Stochastic Approximation: A Dynamical System Viewpoint," Cambridge University Press, 2008.
13. D. McFadden, "Quantal choice analysis: a survey," Annals of Economic and Social Measurement, vol. 5, no. 4, pp. 363-390, June 1976.
14. R. D. Mckelvey and T. Palfrey, "Quantal response equilibria for normal form games," Games and Economic Behavior, vol. 10, no. 1, pp. 6-38, 1995.