OFDM for Wireless Multimedia Communications
OFDM for Wireless Multimedia Communications
Blind separation of synchronous co-channel digital signals using anantenna array. I. Algorithms
IEEE Transactions on Signal Processing
Subspace methods for the blind identification of multichannel FIRfilters
IEEE Transactions on Signal Processing
Blind PARAFAC receivers for DS-CDMA systems
IEEE Transactions on Signal Processing
A subspace approach to blind space-time signal processing forwireless communication systems
IEEE Transactions on Signal Processing
Parallel factor analysis in sensor array processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Identifiability results for blind beamforming in incoherentmultipath with small delay spread
IEEE Transactions on Signal Processing
Cramer-Rao lower bounds for low-rank decomposition ofmultidimensional arrays
IEEE Transactions on Signal Processing
Fast communication: Constrained Tucker-3 model for blind beamforming
Signal Processing
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
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In some antenna array-based wireless communication systems the received signal is multidimensional and can be treated as a tensor (3D array) instead of a matrix (2D array). In this paper, we make use of a generalized tensor decomposition known as constrained Block-PARAFAC and propose a tensor (3D) model for the signal received by three types of wireless communication systems. The considered wireless communication systems are multiuser systems subject to frequency-selective multipath and employing multiple receiver antennas together with (i) oversampling or (ii) direct-sequence spreading or (iii) multicarrier modulation. The proposed modeling approach aims at unifying the received signal model of these systems into a single PARAFAC model. We show that the proposed model has a constrained structure, where model constraints and associated dimensions depend on each particular system. The proposed constrained Block-PARAFAC model is demonstrated by expanding the tensor using Kronecker products of canonical vectors. As an application of this model to tensor signal processing, a new tensor-based receiver is proposed for blind multiuser equalization, which combines PARAFAC-based modeling with a subspace method. Simulation results are presented to illustrate the performance of the proposed blind receiver.