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The minimum mean-squared-error (MMSE) linear multiuser detector [I-21 is popular because of its good performance and amenability to adaptive implementation. However, there are circumstances in which the linear detector that minimizes bit-error rate (BER) can significantly outperform the MMSE detector. We propose a low-complexity adaptive algorithm for… (More)

—We consider the design and adaptation of a linear equalizer with a finite number of coefficients in the context of a classical linear intersymbol-interference channel with Gaussian noise and a memoryless decision device. If the number of equalizer coefficients is sufficient, the popular minimum mean-squared-error (MMSE) linear equalizer closely… (More)

—We propose the adaptive minimum symbol-error rate algorithm, which is a low-complexity technique for adapting the coefficients of a linear equalizer in systems using pulse-amplitude or quadrature-amplitude modulation. The proposed algorithm very nearly minimizes error probability in white Gaussian noise and can significantly outperform the… (More)

Recent theoretical results in Compressive Sensing (CS) show that sparse (or compressible) signals can be accurately reconstructed from a reduced set of linear measurements in the form of projections onto random vectors. The associated reconstruction consists of a nonlinear optimization that requires knowledge of the actual projection vectors. This work… (More)

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