T. Pollet, M. V. Bladel, and M. Moeneclaey, BER sensitivity of OFDM systems to carrier frequency offset and Wiener phase noise, IEEE Transactions on Communications, vol.43, issue.2/3/4, pp.191-193, 1995.
DOI : 10.1109/26.380034

L. Tomba, On the effect of Wiener phase noise in OFDM systems, IEEE Transactions on Communications, vol.46, issue.5, pp.580-583, 1998.
DOI : 10.1109/26.668721

C. Garnier, L. Clavier, Y. Delignon, M. Loosvelt, and D. Boulinguez, Multiple access for 60 GHz mobile ad hoc network, Vehicular Technology Conference. IEEE 55th Vehicular Technology Conference. VTC Spring 2002 (Cat. No.02CH37367), pp.1517-1521, 2002.
DOI : 10.1109/VTC.2002.1002870

S. Wu and Y. Bar-ness, A New Phase Noise Mitigation Method in OFDM Systems with Simultaneous CPE and ICI Correction, Proc. MCSS, 2003.
DOI : 10.1007/978-94-017-0502-8_56

D. Petrovic, W. Rave, and G. Fettweis, Intercarrier interference due to phase noise in OFDM - estimation and suppression, IEEE 60th Vehicular Technology Conference, 2004. VTC2004-Fall. 2004, 2004.
DOI : 10.1109/VETECF.2004.1400429

D. D. Lin and T. J. Lim, The Variational Inference Approach to Joint Data Detection and Phase Noise Estimation in OFDM, IEEE Transactions on Signal Processing, vol.55, issue.5, pp.1862-1874, 2007.
DOI : 10.1109/TSP.2006.890915

K. Nikitopoulos and A. Polydoros, Compensation schemes for phase noise and residual frequency offset in OFDM systems, GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270), pp.330-333, 2002.
DOI : 10.1109/GLOCOM.2001.965133

S. Wu and Y. Bar-ness, OFDM channel estimation in the presence of frequency offset and phase noise, Proc. IEEE ICC, pp.3366-3370, 2003.

D. Lin, R. Pacheco, T. Lim, and D. Hatzinakos, Joint estimation of channel response, frequency offset and phase noise in OFDM, IEEE Trans. Signal Process, vol.54, issue.9, pp.3542-3554, 2006.

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, J

G. Wei and M. Tanner, A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms, Journal of the American Statistical Association, vol.51, issue.411, pp.699-704, 1990.
DOI : 10.1214/aos/1176346060

C. Andrieu, N. De-freitas, A. Doucet, and M. Jordan, An introduction to MCMC for machine learning, Machine Learning, pp.5-43, 2003.

J. Booth, J. Hobert, and W. Jank, A survey of Monte Carlo algorithms for maximizing the likelihood of a two-stage hierarchical model, Statistical Modelling, vol.1, issue.4, pp.333-349, 2001.
DOI : 10.1191/147108201128249

B. Delyon, M. Lavielle, and E. Moulines, Convergence of a stochastic approximation version of the EM algorithm, Ann. Stat, vol.27, pp.94-128, 1999.

A. Doucet, N. D. Freitas, and N. Gordon, Sequential Monte Carlo Methods in Practice, 2001.
DOI : 10.1007/978-1-4757-3437-9

P. M. Djuric, J. H. Kotecha, J. Zhang, Y. Huang, T. Ghirmai et al., Particle Filtering, IEEE Signal Processing Magazine, vol.20, issue.5, pp.19-38, 2003.
DOI : 10.1109/MSP.2003.1236770
URL : https://hal.archives-ouvertes.fr/hal-01437041

J. Liu and R. Chen, Sequential Monte Carlo Methods for Dynamic Systems, Journal of the American Statistical Association, vol.24, issue.443, pp.1032-1044, 1998.
DOI : 10.1073/pnas.94.26.14220

M. Bolic, P. Djuric, and S. Hong, New resampling algorithms for particle filters, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2003.
DOI : 10.1109/ICASSP.2003.1202435

J. Hol, T. Schon, and F. Gustafsson, On Resampling Algorithms for Particle Filters, 2006 IEEE Nonlinear Statistical Signal Processing Workshop, 2006.
DOI : 10.1109/NSSPW.2006.4378824

M. Bolic, P. Djuric, and S. Hong, Resampling algorithms for particle filters: A computational complexity perspective, EURASIP J. Appl. Signal Process, vol.15, pp.2267-2277, 2004.

R. Chen and J. S. Liu, Mixture Kalman filters, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.62, issue.3, pp.493-508, 2000.
DOI : 10.1111/1467-9868.00246

T. Schon, F. Gustafsson, and P. Nordlund, Marginalized particle filters for mixed linear/nonlinear state-space models, IEEE Transactions on Signal Processing, vol.53, issue.7, pp.2279-2289, 2005.
DOI : 10.1109/TSP.2005.849151

A. Doucet, S. Godsill, and C. Andrieu, On sequential Monte Carlo sampling methods for Bayesian filtering, Statistics and Computing, vol.10, issue.3, pp.197-208, 2000.
DOI : 10.1023/A:1008935410038

A. Kong, J. Liu, and W. Wong, Sequential Imputations and Bayesian Missing Data Problems, Journal of the American Statistical Association, vol.52, issue.425, pp.278-288, 1994.
DOI : 10.1080/01621459.1987.10478458

O. Cappé and E. Moulines, On the use of particle filtering for maximum likelihood parameter estimation, Proc. EUSIPCO, Turkey, 2005.

A. Doucet and V. Tadic, Parameter estimation in general state-space models using particle methods, Annals of the Institute of Statistical Mathematics, vol.94, issue.2, pp.409-422, 2003.
DOI : 10.1007/BF02530508

C. Andrieu, A. Doucet, and V. Tadic, On-Line Parameter Estimation in General State-Space Models, Proceedings of the 44th IEEE Conference on Decision and Control, pp.332-337, 2005.
DOI : 10.1109/CDC.2005.1582177

C. Andrieu and A. Doucet, Online expectation-maximization type algorithms for parameter estimation in general state space models, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2003.
DOI : 10.1109/ICASSP.2003.1201620

F. Cérou, F. L. Gland, and N. Newton, Stochastic particle methods for linear tangent filtering equations, " in Optimal Control and PDE's -Innovations and Applications. In honor of Alain Bensoussan on the occasion of his 60th birthday, pp.231-240, 2001.

R. Redner and H. Walker, Mixture Densities, Maximum Likelihood and the EM Algorithm, SIAM Review, vol.26, issue.2, pp.195-239, 1984.
DOI : 10.1137/1026034

N. Gordon, D. Salmond, and A. Smith, Novel approach to nonlinear/non-Gaussian Bayesian state estimation, IEE Proceedings F Radar and Signal Processing, vol.140, issue.2, pp.107-113, 1993.
DOI : 10.1049/ip-f-2.1993.0015

J. Liu and M. West, Combined Parameter and State Estimation in Simulation-Based Filtering, Sequential Monte Carlo in, pp.197-223, 2001.
DOI : 10.1007/978-1-4757-3437-9_10

C. Andrieu, A. Doucet, S. Singh, and V. Tadic, Particle Methods for Change Detection, System Identification, and Control, Proceedings of the IEEE, pp.423-438, 2004.
DOI : 10.1109/JPROC.2003.823142

B. Vo, B. Vo, and S. Singh, Sequential Monte Carlo methods for static parameter estimation in random set models, Proc. IEEE ISSNIP, pp.313-318, 2004.

N. Chopin, A sequential particle filter method for static models, Biometrika, vol.89, issue.3, pp.539-552, 2002.
DOI : 10.1093/biomet/89.3.539

C. Andrieu, N. D. Freitas, and A. Doucet, Sequential MCMC for Bayesian model selection, Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics. SPW-HOS '99, pp.130-134, 1999.
DOI : 10.1109/HOST.1999.778709
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.338.2727

A. Papavasiliou, A uniformly convergent adaptive particle filter, Journal of Applied Probability, vol.5, issue.04, pp.1053-1068, 2005.
DOI : 10.1239/jap/1032438382

V. Zaritskii, V. Svetnik, and L. Shimelevich, Monte Carlo technique in problems of optimal data processing Automation and remote control, pp.95-103, 1975.

N. Vaswani and ". , PF-EIS & PF-MT: New Particle Filtering Algorithms for Multimodal Observation Likelihoods and Large Dimensional State Spaces, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, pp.1193-1196, 2007.
DOI : 10.1109/ICASSP.2007.367056

P. Tichavsky, Posterior Cramer-Rao bound for adaptive harmonic retrieval, IEEE Transactions on Signal Processing, vol.43, issue.5, pp.1299-1302, 1995.
DOI : 10.1109/78.382422