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{"references": ["R.W. Lucky, \"Techniques for adaptive equalization of digital\ncommunication\", Bell Syst, Tech, J., Vol. 45, pp. 255-286, 1965.", "John G. Proakis, \"Digital Communications\", McGraw Hill Higher\nEducation, 4e \u00e9dition, Paperback (December 1, 2000).", "S. Safi, \"Identification aveugle des canaux \u00e0 phase non minimale en\nutilisant les statistiques d'ordre sup\u00e9rieur: application aux r\u00e9seaux\nmobiles\", Th\u00e8se d'Habilit\u00e9, Cadi Ayyad University, Marrakesh,\nMorocco, (2008).", "M. Bellanger, \u00ab Analyse des signaux et filtrage num\u00e9rique adaptatif \u00bb,\nMasson ed., Paris, 1989 400p.", "B. Picinbono, \u00ab Signaux aleatoires, tome 3, bases du traitement\nstatistique du signal \u00bb, Dunod, Paris, 1995.", "Haykin S., \"Adaptive Filter Theory\", 3e \u00e9dition, Upper Saddle River,\nNew Jersey, 1996.", "Macchi O., \u00ab L'\u00e9galisation num\u00e9rique en communications, Annales des\nt\u00e9l\u00e9communications \u00bb, vol.53, n_1-2, pp. 39-58, 1998.", "Brossier J.M., \u00ab Egalisation et synchronisation, Signal et communication\nnum\u00e9rique, collection Traitement du Signal \u00bb, Hermes 1997.", "Sklar B., \"Digital Communications \u2013 Fundamentals and applications\",\nPrentice Hall 2001.\n[10] Labat J., Macchi O., Laot C., \"Adaptive decision feedback Equalizers\":\nCan you skip the training period, IEEE trans. On Communications,\n1996.\n[11] Tugnait J. K., Tong L., Ding Z., \"Single User Channel Estimation and\nEqualization\", IEEE Signal Processing Magazine, pp. 17-28, may 2000.\n[12] D. Godard, \"Self recovering equalization and carrier tracking in twodimensional\ndata communication\".\n[13] M. Honig, D. Messerschmitt, \"Adaptive Filters: Sructures, Algorithms,\nand Applications\". Boston: Kluwer Academic Publishers, 1984."]}
This paper presented a study of three algorithms, the equalization algorithm to equalize the transmission channel with ZF and MMSE criteria, application of channel Bran A, and adaptive filtering algorithms LMS and RLS to estimate the parameters of the equalizer filter, i.e. move to the channel estimation and therefore reflect the temporal variations of the channel, and reduce the error in the transmitted signal. So far the performance of the algorithm equalizer with ZF and MMSE criteria both in the case without noise, a comparison of performance of the LMS and RLS algorithm.
RLS Bran A, Adaptive filtering second equalizer, Proakis (B) MMSE, ZF., LMS
RLS Bran A, Adaptive filtering second equalizer, Proakis (B) MMSE, ZF., LMS
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