Browsing by Author "De Lathauwer, Lieven"

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    Bayesian Blind PARAFAC Recievers forDS-CDMA Systems
    (2003-10-01) de Baynast, Alexandre; Declercq, David; De Lathauwer, Lieven; Aazhang, Behnaam; Center for Multimedia Communications (http://cmc.rice.edu/)
    In this paper an original Bayesian approach for blind detec-tion for Code Division Multiple Access (CDMA) Systems in presence of spatial diversity at the receiver is developed. In the noiseless context, the blind detection/identification problem relies on the canonical decomposition (also re-ferred as Parallel Factor analysis [Sidiropoulos, IEEE SP 00], PARAFAC. The author in [Bro,INCINC 96] pro-poses a suboptimal solution in least-squares sense. How-ever, poor performance are obtained in presence of high noise level. The recently emerged Markov chain Monte Carlo (MCMC) signal processing method provide a novel paradigm for tackling this problem. Simulation results are presented to demonstrate the effectiveness of this method.
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    Blind PARAFAC Receivers for Multiple Access-Multiple Antenna Systems
    (2003-10-01) de Baynast, Alexandre; De Lathauwer, Lieven; Aazhang, Behnaam; Center for Multimedia Communications (http://cmc.rice.edu/)
    In this paper, we present a new blind receiver for multiple access channel with multiple transmit antennas per user and multiple receive antennas (MIMO channel). After being multiplied by a spreading sequence, each user s data is split into Nt streams that are simultaneously transmitted using Nt transmit antennas. The received signal at each receive antenna is a linear superposition of the Nt transmitted signals of the Nu users perturbed by noise. We propose a new blind detection/identification algorithm under the assumption that the fading is slow and frequency non-selective. This algorithm relies on a generalization of parallel factor analysis (PARAFAC analysis, [Kruskal, Lin. Alg. Appl. 77, Sidiropoulos, Tr. on Sig. Proc. 00]): we show that a generalized canonical decomposition (CANDECOMP) of the 3D data tensor is unique under mild assumptions without noise. Neither algebraic orthogonality nor independence between sources is needed for uniqueness of the decomposition. By performing this decomposi-tion, in rank-(Nt,Nt,1) terms, we are able to retrieve the three sets of parameters: the symbols, the channel fading coefficients (including the antenna gains) and the spreading sequences. In a noisy context, we propose a simple algorithm of the alternating least squares (ALS) type, which yields a performance close to the linear minimum mean square error (LMMSE) receiver which requires knowledge of the channel and spreading sequences.