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Bayesian Foundations of Channel Estimation for Smart Radios

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, we revisit the philosophical foundations of the field of channel estimation. Our main intention is to come up with a partial answer to the question: “given some available sensed signals, how should cognitive radios ideally perform channel estimation?”.We specifically introduce a general framework to provide optimal channel estimates under any prior knowledge at the sensing device. Our discussion is articulated as a top-down approach, introducing successively (i) a discussion on the philosophical foundations of channel estimation as a simplification means for the general problem of wireless detection, (ii) an information theoretically optimal approach to channel detection assuming the sensing device has infinite memory, and (iii) a derived optimal approach when limited memory size is accounted for. The key mathematical tools used in this discussion emerge from Bayesian probability theory and are known as the maximum entropy principle and the minimum update principle. Derivations are carried out for the particular case of channel estimation in orthogonal frequency division multiplexing (OFDM) systems. While some theoretical results will be proven to match already known techniques, such as Kalman filters, another set of novel results will be shown by simulations to perform better than known channel estimation schemes.
Rocznik
Strony
41--49
Opis fizyczny
Bibliogr. 19. poz., fig.
Twórcy
autor
  • ST-Ericsson, Supélec, 635 Route des Lucioles, 06560 Sophia Antipolis, France
autor
  • ST-Ericsson, Eurécom, 635 Route des Lucioles, 06560 Sophia Antipolis, France
autor
  • Alcatel-Lucent Chair, Supélec , Plateau de Moulon, 3 rue Joliot-Curie, 91192 Gif sur Yvette, France
Bibliografia
  • [1] O. Edfors, M. Sandell, J.-J. van de Beek, S. K. Wilson, and P. O. Börjesson , “OFDM channel estimation by singular value decomposition”, IEEE Trans. Commun., vol. 46, pp. 931-939, July 1998.
  • [2] M. Morelli et al., “A comparison of pilot-aided Channel estimation methods for OFDM systems”, IEEE Transactions on Signal Processing, vol. 49, pp. 3065–3073, 2001.
  • [3] Y. Li, “Pilot-symbol-aided channel estimation for OFDM in wireless systems”,IEEE Trans. Veh. Technol., vol. 49, pp. 1207–1215, July 2000.
  • [4] Y. Li, L.J. Cimini, N.R. Sollenberger, “Robust channel estimation for OFDM systems with rapid dispersive fading channels”, IEEE Transactions on Communications, Vol. 46, Issue 7, pp. 902–915, July 1998
  • [5] R. Couillet, M. Debbah, “Mathematical Foundations of Cognitive Radios,” Journal of Telecommunications and Information Technology, no. 4, 2009.
  • [6] R. Couillet, M. Debbah, “A Bayesian Framework for Collaborative Multi-Source Signal Detection,” IEEE Trans. on Signal Processing, submitted.
  • [7] R. Couillet, M. Debbah, “A maximum entropy approach to OFDM channel estimation,” Proceedings of IEEE SPAWC Conference, Perugia, Italy, 2008.
  • [8] E. T. Jaynes, “Information Theory and Statistical Mechanics”, Physical Review, APS, vol. 106, no. 4, pp. 620-630, 1957.
  • [9] A. Caticha, “Lectures on Probability, Entropy and Statistical Physics”, arXiv:0808.0012v1 [physics.data-an], 2008.
  • [10] C. E. Shannon, “A mathematical theory of communications”, Bell System Technical Journal, vol. 27, no. 7, pp. 379-423, 1948.
  • [11] E. T. Jaynes, “Probability Theory: The Logic of Science”, Cambridge University Press, 2003.
  • [12] R. Couillet, M. Debbah, “A maximum entropy approach to OFDM channel estimation”, arxiv Preprint http://arxiv.org/pdf/0811.0778
  • [13] K. E. Baddour and , N. C. Beaulieu, “Autoregressive modeling for fading channel simulation,” IEEE Transaction on Wireless Communications, July 2005.
  • [14] S. Sesia, I. Toufik and M. Baker, “LTE, The UMTS Long Term Evolution: From Theory to Practice”, Wiley & Sons, 2009.
  • [15] J. Cai, X. Shen and J.W. Mark, “Robust channel estimation for OFDM wireless communication systems-an H∞ approach”, IEEE Trans. Wireless Commun., vol. 3,no. 6, pp. 2060–2071, 2004.
  • [16] D. Schafhuber, G. Matz. and F. Hlawatsch, “Adaptive Wiener filters for time-varying channel estimation in wireless OFDM systems”, IEEE ICASSP, vol. 4, pp. 688–691, 2003
  • [17] J. G. Proakis, Digital Communications. New York: McGraw Hill, 1995.
  • [18] R. H. Clarke, ‘A Statistical Theory of Mobile-radio Reception’. Bell Syst. Tech. J., pp. 957–1000, July 1968.
  • [19] W. C. Jakes, Microwave Mobile Communications. New York: John Wiley & Sons, Ltd/Inc., 1974.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f6095033-f6ba-45bb-b55a-42e62050f9a9
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