Decision-feedback interference suppression in CDMA systems: a ML-based semiblind approach
Signal Processing - Special section: Security of data hiding technologies
Blind separation of convolutive mixtures by decorrelation
Signal Processing
Wireless Communications & Mobile Computing
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This paper addresses the problem of blind multiple access interference (MAI) and intersymbol interference (ISI) suppression in direct sequence code division multiple access (DS CDMA) systems. A novel approach to obtain the coefficients of a linear receiver using the maximum likelihood (ML) principle is proposed. The method is blind because it only exploits the statistical features of the transmitted symbols and Gaussian noise in the channel. We demonstrate that an adequate linear constraint on these coefficients ensures that the desired user is extracted and the resulting linearly constrained maximum likelihood linear (LCMLL) receiver can be efficiently implemented using the iterative space alternating generalized expectation-maximization (SAGE) algorithm. In order to take advantage of the diversity inherent to multipath channels, we also introduce a blind RAKE multiuser receiver that proceeds in two steps. First, soft estimates of the desired user transmitted symbols are obtained from each propagation path using a bank of appropriate LCMLL receivers. Afterwards, these estimates are adequately combined to enhance the signal-to-interference-and-noise ratio (SINR). Computer simulations show that the proposed blind algorithms for multiuser detection are near-far resistant and attain convergence using small blocks of data, thus outperforming existing linearly constrained minimum variance (LCMV) blind receivers