Abstract
In this paper, we propose a new application of blind source separation (BSS) in OFDM systems with zero padding (ZP). We first focus on the model of ZP-OFDM to reduce it to an instantaneous linear mixing matrix model. Then we apply blind separation source (BSS) techniques based on the natural gradient (NG) and particularly EASI and M-EASI algorithms. Hence, we endow the method with adaptive properties and extend the use of BSS to complex symbols. This novel method blindly cancels the inter-channel interference (ICI) introduced by the channel with no use of cyclic prefix, meaning a power efficiency improvement and avoiding the recovery difficulties when some of the subcarriers are hit by a channel frequency response null. We include some experiments to illustrate the excellent performance of the proposed application even when ill-conditioned channels.
Thanks to Spanish goverment for funding TIC-2003-03781.
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References
Edfors, O., Sandell, M., van de Beek, J.J., Landström, D., Sjöberg, F.: An introduction to orthogonal frequency-division multiplexing. Tutorial (1996), http://courses.ece.uiuc.edu/ece459/spring02/ofdmtutorial_OnlinePDF.pdf
Cardoso, J.F.: Blind signal separation: Statistical principles. Proc. of the IEEE 86, 2009–2025 (1998)
Comon, P.: Independent component analysis, a new concept? Signal Processing 36, 287–314 (1994)
Murillo-Fuentes, J.J., González-Serrano, F.J.: A sinusoidal contrast function for the blind separation of statistically independent sources. IEEE Tras. on Signal Processing (2004) (acepted for publication)
Theis, F., Jung, A., Puntonet, C., Lang, E.: Linear geometric ICA: Fundamentals and algorithms. Neural Computation 15, 1–21 (2002)
Amari, S.I.: Natural gradient works efficiently in learning. Neural Computation 10, 251–276 (1998)
Cardoso, J.F., Laheld, B.H.: Equivariant adaptive source separation. IEEE Trans. on Signal Processing 44, 3017–3030 (1996)
Murillo-Fuentes, J., González-Serrano, F.: Median equivariant adaptive separation via independence: application to communications. Neurocomputing 49, 389–409 (2002)
Cichocki, A., Douglas, S., Amari, S.: Robust techniques for independent component analysis (ICA) with noisy data. Neurocomputing, 113–129 (1998)
Douglas, S., Cichocki, A., Amari, S.: Self-whitening algorithms for adaptive equalization and deconvolution. IEEE Trans. on Signal Processing 47, 1161–1165 (1999)
Caamaño-Fernández, A., Boloix-Tortosa, R., Ramos, J., Murillo-Fuentes, J.J.: High order statistics in multiuser detection. IEEE Tras. on Man and Cybernetics A (2004) (acepted for publication)
Ding, Y., Davidson, T.N., Lou, Z.Q., Wong, K.M.: Minimum ber block precoders for zero-forcing equalitation. IEEE Trans. on Signal Processing 51, 2410–2423 (2003)
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Boloix-Tortosa, R., Murillo-Fuentes, J.J. (2004). Blind Source Separation in the Adaptive Reduction of Inter-channel Interference for OFDM. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_144
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DOI: https://doi.org/10.1007/978-3-540-30110-3_144
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