K. V. Schinasi, Spectrum management: Better knowledge needed to take advantage of technologies that may improve spectrum efficiency, United States General Accounting Office, Tech. Rep, 2004.

J. Mitola, I. , and G. Q. Maguire-jr, Cognitive radio: making software radios more personal, IEEE Personal Communications, vol.6, issue.4, pp.13-18, 1999.
DOI : 10.1109/98.788210

Q. Zhao and B. M. Sadler, A Survey of Dynamic Spectrum Access, IEEE Signal Processing Magazine, vol.24, issue.3, pp.79-89, 2007.
DOI : 10.1109/MSP.2007.361604

S. Haykin, Cognitive radio: brain-empowered wireless communications, IEEE Journal on Selected Areas in Communications, vol.23, issue.2, pp.201-220, 2005.
DOI : 10.1109/JSAC.2004.839380

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.112.8409

A. Goldsmith, S. A. Jafar, I. Maric, and S. Srinivasa, Breaking Spectrum Gridlock With Cognitive Radios: An Information Theoretic Perspective, Proc. IEEE, pp.894-914, 2009.
DOI : 10.1109/JPROC.2009.2015717

G. J. Foschini and M. J. Gans, On limits of wireless communications in a fading environment when using multiple antennas, Wireless Personal Communications, vol.6, issue.3, pp.311-335, 1998.
DOI : 10.1023/A:1008889222784

I. E. Telatar, Capacity of Multi-antenna Gaussian Channels, European Transactions on Telecommunications, vol.45, issue.6, pp.585-596, 1999.
DOI : 10.1002/ett.4460100604

Y. J. Zhang and M. A. So, Optimal Spectrum Sharing in MIMO Cognitive Radio Networks via Semidefinite Programming, IEEE Journal on Selected Areas in Communications, vol.29, issue.2
DOI : 10.1109/JSAC.2011.110209

G. Scutari and D. P. Palomar, MIMO Cognitive Radio: A Game Theoretical Approach, IEEE Transactions on Signal Processing, vol.58, issue.2, pp.761-780, 2010.
DOI : 10.1109/TSP.2009.2032039

J. Wang, G. Scutari, and D. P. Palomar, Robust MIMO Cognitive Radio Via Game Theory, IEEE Transactions on Signal Processing, vol.59, issue.3, pp.1183-1201, 2011.
DOI : 10.1109/TSP.2010.2092773

N. Nie and C. Comaniciu, Adaptive Channel Allocation Spectrum Etiquette for Cognitive Radio Networks, DySPAN '05: Proceedings of the 2005 IEEE Symposium on Dynamic Spectrum Access Networks, pp.269-278, 2005.
DOI : 10.1007/s11036-006-0049-y

A. Anandkumar, N. Michael, A. K. Tang, and A. Swami, Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret, IEEE Journal on Selected Areas in Communications, vol.29, issue.4, pp.731-745, 2011.
DOI : 10.1109/JSAC.2011.110406

H. Li, Multi-agent Q-learning of channel selection in multi-user cognitive radio systems: A two by two case, 2009 IEEE International Conference on Systems, Man and Cybernetics, pp.1893-1898, 2009.
DOI : 10.1109/ICSMC.2009.5346172

Y. Gai, B. Krishnamachari, and R. Jain, Learning Multiuser Channel Allocations in Cognitive Radio Networks: A Combinatorial Multi-Armed Bandit Formulation, 2010 IEEE Symposium on New Frontiers in Dynamic Spectrum (DySPAN), 2010.
DOI : 10.1109/DYSPAN.2010.5457857

J. Hannan, Approximation to Bayes risk in repeated play Volume III, ser, Contributions to the Theory of Games, pp.97-139, 1957.

S. Shalev-shwartz, Online Learning and Online Convex Optimization, Machine Learning, pp.107-194, 2011.
DOI : 10.1561/2200000018

V. G. Vovk, AGGREGATING STRATEGIES, COLT '90: Proceedings of the 3rd Workshop on Computational Learning Theory, pp.371-383, 1990.
DOI : 10.1016/B978-1-55860-146-8.50032-1

N. Littlestone and M. K. Warmuth, The Weighted Majority Algorithm, Information and Computation, vol.108, issue.2, pp.212-261, 1994.
DOI : 10.1006/inco.1994.1009

P. Auer, N. Cesa-bianchi, and C. Gentile, Adaptive and Self-Confident On-Line Learning Algorithms, Journal of Computer and System Sciences, vol.64, issue.1, pp.48-75, 2002.
DOI : 10.1006/jcss.2001.1795

P. Mertikopoulos, E. V. Belmega, and A. L. Moustakas, Matrix exponential learning: Distributed optimization in MIMO systems, 2012 IEEE International Symposium on Information Theory Proceedings, pp.3028-3032, 2012.
DOI : 10.1109/ISIT.2012.6284117

URL : https://hal.archives-ouvertes.fr/hal-00741823

S. M. Kakade, S. Shalev-shwartz, and A. Tewari, Regularization techniques for learning with matrices, The Journal of Machine Learning Research, vol.13, pp.1865-1890, 2012.

H. Bölcskei, D. Gesbert, and A. J. Paulraj, On the capacity of OFDM-based spatial multiplexing systems, IEEE Transactions on Communications, vol.50, issue.2, pp.225-234, 2002.
DOI : 10.1109/26.983319

K. B. Letaief and Y. J. Zhang, Advances in smart antennas - Dynamic multiuser resource allocation and adaptation for wireless systems, IEEE Wireless Communications, vol.13, issue.4, pp.38-47, 2006.
DOI : 10.1109/MWC.2006.1678164

C. R. Stevenson, G. Chouinard, Z. Lei, W. Hu, and S. J. Shellhammer, The first cognitive radio wireless regional area network standard, IEEE Commun. Mag, vol.80222, issue.47 1, pp.130-138, 2009.
DOI : 10.1109/mcom.2009.4752688

E. Hazan and C. Seshadri, Efficient learning algorithms for changing environments, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, 2009.
DOI : 10.1145/1553374.1553425

D. P. Palomar, J. M. Cioffi, and M. Lagunas, Uniform power allocation in MIMO channels: a game-theoretic approach, IEEE Transactions on Information Theory, vol.49, issue.7, p.1707, 2003.
DOI : 10.1109/TIT.2003.813513

H. Robbins, Some aspects of the sequential design of experiments, Bulletin of the American Mathematical Society, vol.58, issue.5, pp.527-535, 1952.
DOI : 10.1090/S0002-9904-1952-09620-8

P. Mertikopoulos, E. V. Belmega, A. L. Moustakas, and S. Lasaulce, Distributed Learning Policies for Power Allocation in Multiple Access Channels, IEEE Journal on Selected Areas in Communications, vol.30, issue.1, pp.96-106, 2012.
DOI : 10.1109/JSAC.2012.120109

URL : https://hal.archives-ouvertes.fr/hal-00648840

M. Zinkevich, Online convex programming and generalized infinitesimal gradient ascent, ICML '03: Proceedings of the 20th International Conference on Machine Learning, 2003.

C. D. Cantrell, Modern Mathematical Methods for Physicists and Engineers, Measurement Science and Technology, vol.12, issue.12, 2000.
DOI : 10.1088/0957-0233/12/12/702

P. Mertikopoulos and E. V. Belmega, Adaptive Spectrum Management in MIMO-OFDM Cognitive Radio: An Exponential Learning Approach, Proceedings of the 7th International Conference on Performance Evaluation Methodologies and Tools, 2013.
DOI : 10.4108/icst.valuetools.2013.254385

URL : https://hal.archives-ouvertes.fr/hal-01382305

J. Kwon and P. Mertikopoulos, A continuous-time approach to online optimization, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01382299

F. Alvarez, J. Bolte, and O. Brahic, Hessian Riemannian Gradient Flows in Convex Programming, SIAM Journal on Control and Optimization, vol.43, issue.2, pp.477-501, 2004.
DOI : 10.1137/S0363012902419977

G. Calcev, D. Chizhik, B. Göransson, S. Howard, H. Huang et al., A Wideband Spatial Channel Model for System-Wide Simulations, IEEE Transactions on Vehicular Technology, vol.56, issue.2, p.389, 2007.
DOI : 10.1109/TVT.2007.891463

G. Scutari, D. P. Palomar, and S. Barbarossa, Simultaneous Iterative Water-Filling for Gaussian Frequency-Selective Interference Channels, 2006 IEEE International Symposium on Information Theory, 2006.
DOI : 10.1109/ISIT.2006.261855

S. Sorin, Exponential weight algorithm in continuous time, Mathematical Programming, pp.513-528, 2009.
DOI : 10.1007/s10107-007-0111-y

R. T. Rockafellar, Convex Analysis, 1970.
DOI : 10.1515/9781400873173

P. Hall and C. C. Heyde, Martingale Limit Theory and Its Application, ser. Probability and Mathematical Statistics, 1980.

K. Azuma, Weighted sums of certain dependent random variables, Tohoku Mathematical Journal, vol.19, issue.3, pp.357-367, 1967.
DOI : 10.2748/tmj/1178243286