]. M. Conti and S. Giordano, Mobile ad hoc networking: milestones, challenges, and new research directions, IEEE Communications Magazine, vol.52, issue.1, pp.85-96, 2014.
DOI : 10.1109/MCOM.2014.6710069

C. R. Lin and M. Gerla, Adaptive clustering for mobile wireless networks, IEEE Journal on Selected Areas in Communications, vol.15, issue.7, pp.1265-1275, 1997.
DOI : 10.1109/49.622910

A. Asadi, V. Sciancalepore, and V. Mancuso, On the efficient utilization of radio resources in extremely dense wireless networks, IEEE Communications Magazine, vol.53, issue.1, pp.126-132, 2015.
DOI : 10.1109/MCOM.2015.7010525

J. Sucec and I. Marsic, Hierarchical routing overhead in mobile ad hoc networks, IEEE Transactions on Mobile Computing, vol.3, issue.1, pp.46-56, 2004.
DOI : 10.1109/TMC.2004.1261816

R. Massin, C. J. Martret, and P. Ciblat, A Coalition Formation Game for Distributed Node Clustering in Mobile Ad Hoc Networks, IEEE Transactions on Wireless Communications, vol.16, issue.6, pp.3940-3952, 2017.
DOI : 10.1109/TWC.2017.2690419

Y. Babichenko, Completely uncoupled dynamics and Nash equilibria, Games and Economic Behavior, vol.76, issue.1, pp.1-14, 2012.
DOI : 10.1016/j.geb.2012.06.004
URL : http://ratio.huji.ac.il/sites/default/files/publications/dp529.pdf

H. Tembine, Distributed strategic learning for wireless engineers, 2012.
DOI : 10.1201/b11896
URL : https://hal.archives-ouvertes.fr/hal-00752209

S. Lasaulce and H. Tembine, Game theory and learning for wireless networks: fundamentals and applications, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00661625

S. Hart and A. Mas, Uncoupled Dynamics Do Not Lead to Nash Equilibrium, American Economic Review, vol.93, issue.5, pp.1830-1836, 2003.
DOI : 10.1257/000282803322655581

B. S. Pradelski and H. P. Young, Learning efficient Nash equilibria in distributed systems, Games and Economic Behavior, vol.75, issue.2, pp.882-897, 2012.
DOI : 10.1016/j.geb.2012.02.017

W. Wang, A. Kwasinski, D. Niyato, and Z. Han, A Survey on Applications of Model-Free Strategy Learning in Cognitive Wireless Networks, IEEE Communications Surveys & Tutorials, vol.18, issue.3, pp.1717-1757, 2016.
DOI : 10.1109/COMST.2016.2539923

J. R. Marden, H. P. Young, and L. Y. Pao, Achieving Pareto Optimality Through Distributed Learning, SIAM Journal on Control and Optimization, vol.52, issue.5, pp.2753-2770, 2014.
DOI : 10.1137/110850694

H. Young, Learning by trial and error, Games and Economic Behavior, vol.65, issue.2, pp.626-643, 2009.
DOI : 10.1016/j.geb.2008.02.011

M. Bennis and D. Niyato, A Q-learning based approach to interference avoidance in self-organized femtocell networks, 2010 IEEE Globecom Workshops, pp.706-710, 2010.
DOI : 10.1109/GLOCOMW.2010.5700414

A. Galindo-serrano and L. Giupponi, Distributed Q-Learning for Interference Control in OFDMA-Based Femtocell Networks, 2010 IEEE 71st Vehicular Technology Conference, pp.1-5, 2010.
DOI : 10.1109/VETECS.2010.5493950

F. Wilhelmi, B. Bellalta, C. Cano, and A. Jonsson, Implications of Decentralized Q-learning Resource Allocation in Wireless Networks ArXiv e-prints, 2017.

R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, IEEE Transactions on Neural Networks, vol.9, issue.5, 1998.
DOI : 10.1109/TNN.1998.712192

M. A. Thathachar and P. S. Sastry, Varieties of learning automata: an overview, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.32, issue.6, pp.711-722, 2002.
DOI : 10.1109/TSMCB.2002.1049606

K. S. Narendra and M. A. Thathachar, Learning automata: an introduction. Courier Corporation, 2012.

D. J. Leith and P. Clifford, A Self-Managed Distributed Channel Selection Algorithm for WLANs, 2006 4th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, pp.1-9, 2006.
DOI : 10.1109/WIOPT.2006.1666484

Y. Xu, J. Wang, Q. Wu, A. Anpalagan, and Y. D. Yao, Opportunistic Spectrum Access in Unknown Dynamic Environment: A Game-Theoretic Stochastic Learning Solution, IEEE Transactions on Wireless Communications, vol.11, issue.4, pp.1380-1391, 2012.
DOI : 10.1109/TWC.2012.020812.110025

Q. Wu, Y. Xu, J. Wang, L. Shen, J. Zheng et al., Distributed channel selection in time-varying radio environment: Interference mitigation game with uncoupled stochastic learning, IEEE Transactions on Vehicular Technology, vol.62, issue.9, pp.4524-4538, 2013.

P. S. Sastry, V. V. Phansalkar, and M. A. Thathachar, Decentralized learning of Nash equilibria in multi-person stochastic games with incomplete information, IEEE Transactions on Systems, Man, and Cybernetics, vol.24, issue.5, pp.769-777, 1994.
DOI : 10.1109/21.293490

M. Benaïm, Dynamics of stochastic approximation algorithms, pp.1-68, 1999.
DOI : 10.1007/978-1-4757-1947-5

M. Bennis, S. M. Perlaza, P. Blasco, Z. Han, and H. V. Poor, Self-Organization in Small Cell Networks: A Reinforcement Learning Approach, IEEE Transactions on Wireless Communications, vol.12, issue.7, pp.3202-3212, 2013.
DOI : 10.1109/TWC.2013.060513.120959
URL : https://hal.archives-ouvertes.fr/hal-01281016

S. M. Perlaza, H. Tembine, and S. Lasaulce, How can ignorant but patient cognitive terminals learn their strategy and utility?, 2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp.1-5, 2010.
DOI : 10.1109/SPAWC.2010.5670983
URL : https://hal.archives-ouvertes.fr/hal-00556154

K. A. Chaitanya, V. Sharma, and U. Mukherji, Distributed learning of equilibria for a stochastic game on interference channels, 2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp.650-654, 2015.

J. Zheng, Y. Cai, Y. Xu, and A. Anpalagan, Distributed Channel Selection for Interference Mitigation in Dynamic Environment: A Game-Theoretic Stochastic Learning Solution, IEEE Transactions on Vehicular Technology, vol.63, issue.9, pp.4757-4762, 2014.
DOI : 10.1109/TVT.2014.2311496

S. Hart and A. Mas, A Simple Adaptive Procedure Leading to Correlated Equilibrium, Econometrica, vol.68, issue.5, pp.1127-1150, 2000.
DOI : 10.1111/1468-0262.00153

L. Rose, S. Lasaulce, S. M. Perlaza, and M. Debbah, Learning equilibria with partial information in decentralized wireless networks, IEEE Communications Magazine, vol.49, issue.8, pp.136-142, 2011.
DOI : 10.1109/MCOM.2011.5978427
URL : https://hal.archives-ouvertes.fr/hal-00647634

L. Rose, S. M. Perlaza, C. J. Le, M. Martret, and . Debbah, Self-Organization in Decentralized Networks: A Trial and Error Learning Approach, IEEE Transactions on Wireless Communications, vol.13, issue.1, pp.268-279, 2014.
DOI : 10.1109/TWC.2013.112613.130405
URL : https://hal.archives-ouvertes.fr/hal-00927764

L. Rose, S. M. Perlaza, C. J. Martret, and M. Debbah, Achieving Pareto optimal equilibria in energy efficient clustered ad hoc networks, 2013 IEEE International Conference on Communications (ICC), pp.1491-1495, 2013.
DOI : 10.1109/ICC.2013.6654723
URL : https://hal.archives-ouvertes.fr/hal-00923168

M. Sheng, C. Xu, X. Wang, Y. Zhang, W. Han et al., Utility-Based Resource Allocation for Multi-Channel Decentralized Networks, IEEE Transactions on Communications, vol.62, issue.10, pp.3610-3620, 2014.
DOI : 10.1109/TCOMM.2014.2357028

C. Ramesh, M. Schmitt, and J. Lygeros, Distributed learning in the presence of disturbances, 2016 European Control Conference (ECC), pp.257-262, 2016.
DOI : 10.1109/ECC.2016.7810295

L. Rose, Decisional process for ad hoc networks
URL : https://hal.archives-ouvertes.fr/tel-01079805

J. Nash, Non-cooperative games, Annals of mathematics, pp.286-295, 1951.

D. Fudenberg and D. K. Levine, The theory of learning in games, 1998.

H. P. Young, The Evolution of Conventions, Econometrica, vol.61, issue.1, pp.57-84, 1993.
DOI : 10.2307/2951778

L. Rose, S. M. Perlaza, M. Debbah, C. J. Le, and . Martret, Distributed power allocation with SINR constraints using trial and error learning, 2012 IEEE Wireless Communications and Networking Conference (WCNC), pp.1835-1840, 2012.
DOI : 10.1109/WCNC.2012.6214083
URL : https://hal.archives-ouvertes.fr/hal-00770611

R. Menon, A. B. Mackenzie, R. M. Buehrer, and J. H. Reed, WSN15-4: A Game-Theoretic Framework for Interference Avoidance in Ad hoc Networks, IEEE Globecom 2006, pp.1-6, 2006.
DOI : 10.1109/GLOCOM.2006.979

A. Menon and J. S. Baras, Convergence guarantees for a decentralized algorithm achieving pareto optimality, 2013 American Control Conference, pp.1932-1937, 2013.
DOI : 10.1109/ACC.2013.6580118

J. Gaveau, C. J. Le, M. Martret, and . Assaad, Performance analysis of trial and error algorithms, 1711.

M. Simsek, M. Bennis, and A. Czylwik, Dynamic Inter-Cell Interference Coordination in HetNets: A reinforcement learning approach, 2012 IEEE Global Communications Conference (GLOBECOM), pp.5446-5450, 2012.
DOI : 10.1109/GLOCOM.2012.6503987

J. G. Kemeny, Generalization of a fundamental matrix, Linear Algebra and its Applications, pp.193-206, 1981.
DOI : 10.1016/0024-3795(81)90020-3
URL : https://doi.org/10.1016/0024-3795(81)90020-3

J. G. Kemeny and J. L. Snell, Finite Markov chains. van Nostrand Princeton NJ, vol.356, 1960.

L. Comtet, Partitions of Integers, Advanced Combinatorics, pp.94-126, 1974.
DOI : 10.1007/978-94-010-2196-8_2

D. E. Knuth, Generating all combinations and partitions, The Art of Computer Programming, 2005.

A. Agresti and B. A. , Approximate is better than " exact " for interval estimation of binomial proportions, The American Statistician, vol.52, issue.2, pp.119-126, 1998.

E. Cameron, On the Estimation of Confidence Intervals for Binomial Population Proportions in Astronomy: The Simplicity and Superiority of the Bayesian Approach, Publications of the Astronomical Society of Australia, pp.128-139, 2011.
DOI : 10.1086/433169

M. Abramowitz and I. A. Stegun, Handbook of mathematical functions: with formulas, graphs, and mathematical tables, Courier Corporation, vol.55, 1964.

A. Gelman, J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari et al., Bayesian data analysis

K. Butler and M. Stephens, The distribution of a sum of binomial random variables, DTIC Document, Tech. Rep, 1993.
DOI : 10.21236/ADA266969

S. Ross and B. Chaib-draa, Satisfaction Equilibrium: Achieving Cooperation in Incomplete Information Games, Conference of the Canadian Society for Computational Studies of Intelligence, pp.61-72, 2006.
DOI : 10.1007/11766247_6

S. M. Perlaza, H. Tembine, S. Lasaulce, and M. Debbah, Quality-Of-Service Provisioning in Decentralized Networks: A Satisfaction Equilibrium Approach, IEEE Journal of Selected Topics in Signal Processing, vol.6, issue.2, pp.104-116, 2012.
DOI : 10.1109/JSTSP.2011.2180507
URL : https://hal.archives-ouvertes.fr/hal-00769411

J. Proakis, Digital Communications, ser. McGraw-Hill series in electrical and computer engineering: Communications and signal processing

. Mcgraw-hill, Available: https://books.google.fr/books?id= aUp2QgAACAAJ, 2001.

Y. W. Blankenship, P. J. Sartori, B. K. Classon, V. Desai, and K. L. Baum, Link error prediction methods for multicarrier systems, IEEE 60th Vehicular Technology Conference, 2004. VTC2004-Fall. 2004, pp.4175-4179, 2004.
DOI : 10.1109/VETECF.2004.1404865

M. Pauli, U. Wachsmann, and S. S. Tsai, Quality determination for a wireless communications link, p.183, 2007.

S. N. Donthi and N. B. Mehta, An Accurate Model for EESM and its Application to Analysis of CQI Feedback Schemes and Scheduling in LTE, IEEE Transactions on Wireless Communications, vol.10, issue.10, pp.3436-3448, 2011.
DOI : 10.1109/TWC.2011.081011.102247

M. B. Hcine and R. Bouallegue, Analytical Downlink Effective SINR Evaluation in LTE Networks, 2015 IEEE 29th International Conference on Advanced Information Networking and Applications Workshops, pp.376-381, 2015.
DOI : 10.1109/WAINA.2015.51

J. Fan, Q. Yin, G. Y. Li, B. Peng, and X. Zhu, MCS Selection for Throughput Improvement in Downlink LTE Systems, 2011 Proceedings of 20th International Conference on Computer Communications and Networks (ICCCN), pp.1-5, 2011.
DOI : 10.1109/ICCCN.2011.6005743

H. Song, R. Kwan, and J. Zhang, Approximations of EESM Effective SNR Distribution, IEEE Transactions on Communications, vol.59, issue.2, pp.603-612, 2011.
DOI : 10.1109/TCOMM.2011.011811.100056

J. Francis and N. B. Mehta, EESM-Based Link Adaptation in Point-to-Point and Multi-Cell OFDM Systems: Modeling and Analysis, IEEE Transactions on Wireless Communications, vol.13, issue.1, pp.407-417, 2014.
DOI : 10.1109/TWC.2013.112613.130716

J. Gaveau, C. J. Le, M. Martret, and . Assaad, Grouping of subcarriers and effective SNR statistics in wideband OFDM systems using EESM, 2017 IEEE 13th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp.1-7, 2017.
DOI : 10.1109/WiMOB.2017.8115800
URL : https://hal.archives-ouvertes.fr/hal-01578885

T. Rappaport, Wireless Communications: Principles and Practice, 1996.

A. M. Cipriano, R. Visoz, and T. Salzer, Calibration Issues of PHY Layer Abstractions for Wireless Broadband Systems, 2008 IEEE 68th Vehicular Technology Conference, pp.1-5, 2008.
DOI : 10.1109/VETECF.2008.403

, Radio transmission and reception, 3rd Generation Partnership Project (3GPP), TS 45.005, 2008.

Z. Wang and G. B. Giannakis, A simple and general parameterization quantifying performance in fading channels, IEEE Transactions on Communications, vol.51, issue.8, pp.1389-1398, 2003.
DOI : 10.1109/TCOMM.2003.815053

T. W. Anderson and D. A. Darling, A Test of Goodness of Fit, Journal of the American Statistical Association, vol.49, issue.268, pp.765-769, 1954.
DOI : 10.1214/aoms/1177729080

, 3GPP TSG RAN multiplexing and channel coding (FDD) (release 1999), 3GPP, vol.212, pp.2002-2011

P. Brémaud, Markov chains: Gibbs fields, Monte Carlo simulation, and queues, 1999.
DOI : 10.1007/978-1-4757-3124-8

V. Aalo and C. Chayawan, Outage probability of cellular radio systems using maximal ratio combining in Rayleigh fading channel with multiple interferers, Electronics Letters, vol.36, issue.15, pp.1314-1315, 2000.
DOI : 10.1049/el:20000910

, Immeuble Discovery Route de l'Orme aux Merisiers RD 128, 91190.