Adaptive interference suppression for DS-CDMA systems based on interpolated FIR filters with adaptive interpolators in multipath channels

In this work we propose an adaptive linear receiver structure based on interpolated finite impulse response (FIR) filters with adaptive interpolators for direct sequence code division multiple access (DS-CDMA) systems in multipath channels. The interpolated minimum mean-squared error (MMSE) and the interpolated constrained minimum variance (CMV) solutions are described for a novel scheme where the interpolator is rendered time-varying in order to mitigate multiple access interference (MAI) and multiple-path propagation effects. Based upon the interpolated MMSE and CMV solutions we present computationally efficient stochastic gradient (SG) and exponentially weighted recursive least squares type (RLS) algorithms for both receiver and interpolator filters in the supervised and blind modes of operation. A convergence analysis of the algorithms and a discussion of the convergence properties of the method are carried out for both modes of operation. Simulation experiments for a downlink scenario show that the proposed structures achieve a superior BER convergence and steady-state performance to previously reported reduced-rank receivers at lower complexity

A matrix-pencil approach to blind separation of colored nonstationary signals

For many signal sources such as speech with distinct, nonwhite power spectral densities, second-order statistics of the received signal mixture can be exploited for signal separation. Without knowledge on noise correlation matrix, we propose a simple and yet effective signal extraction method for signal source separation under unknown temporally white noise. This new and unbiased signal extractor is derived from the matrix pencil formed between output autocorrelation matrices at different delays. Based on the matrix pencil, an ESPRIT-type algorithm is derived to get an optimal solution in least square sense. Our method is well suited for systems with colored sensor noises and for nonstationary signals. © 2000 IEEE.published_or_final_versio

Interference estimation with applications to blind multiple-access communication over fading channels

Includes bibliographical references.We consider the detection of nonorthogonal multipulse signals on multiple-access fading channels. The generalized maximum-likelihood rule is employed to decode users whose complex fading gains are unknown. We develop geometrical interpretations for the resulting detectors and their corresponding asymptotic efficiencies. The generalized maximum-likelihood detection rule is then applied to find a matched subspace detector for the frequency-selective fading channel, under the assumption of a short coherence time (or long coherence time without the computational power to track the fading parameters). We propose blind implementations of these detectors for nonorthogonal multipulse signaling on both frequency-nonselective and frequency-selective multiple-access fading channels. These blind detectors extend the results of Wang and Poor to multipulse modulation and fast frequency selective fading. For comparison, the minimum mean-squared error decision rules for these channels are derived and blind implementations of their corresponding detectors are developed.This work was supported by the National Science Foundation under Contract ECS 9979400 and by the Office of Naval Research under Contracts N00014-89-J-1070 and N0014-00-1-0033

A recursive scheme for computing autocorrelation functions of decimated complex wavelet subbands

This paper deals with the problem of the exact computation of the autocorrelation function of a real or complex discrete wavelet subband of a signal, when the autocorrelation function (or Power Spectral Density, PSD) of the signal in the time domain (or spatial domain) is either known or estimated using a separate technique. The solution to this problem allows us to couple time domain noise estimation techniques to wavelet domain denoising algorithms, which is crucial for the development of blind wavelet-based denoising techniques. Specifically, we investigate the Dual-Tree complex wavelet transform (DT-CWT), which has a good directional selectivity in 2-D and 3-D, is approximately shift-invariant, and yields better denoising results than a discrete wavelet transform (DWT). The proposed scheme gives an analytical relationship between the PSD of the input signal/image and the PSD of each individual real/complex wavelet subband which is very useful for future developments. We also show that a more general technique, that relies on Monte-Carlo simulations, requires a large number of input samples for a reliable estimate, while the proposed technique does not suffer from this problem

Space time transceiver design over multipath fading channels

Imperial Users onl

New Blind Block Synchronization for Transceivers Using Redundant Precoders

This paper studies the blind block synchronization problem in block transmission systems using linear redundant precoders (LRP). Two commonly used LRP systems, namely, zero padding (ZP) and cyclic prefix (CP) systems, are considered in this paper. In particular, the block synchronization problem in CP systems is a broader version of timing synchronization problem in the popular orthogonal frequency division multiplexing (OFDM) systems. The proposed algorithms exploit the rank deficiency property of the matrix composed of received blocks when the block synchronization is perfect and use a parameter called repetition index which can be chosen as any positive integer. Theoretical results suggest advantages in blind block synchronization performances when using a large repetition index. Furthermore, unlike previously reported algorithms, which require a large amount of received data, the proposed methods, with properly chosen repetition indices, guarantee correct block synchronization in absence of noise using only two received blocks in ZP systems and three in CP systems. Computer simulations are conducted to evaluate the performances of the proposed algorithms and compare them with previously reported algorithms. Simulation results not only verify the capability of the proposed algorithms to work with limited received data but also show significant improvements in the block synchronization error rate performance of the proposed algorithms over previously reported algorithms

Blind adaptive constrained reduced-rank parameter estimation based on constant modulus design for CDMA interference suppression

This paper proposes a multistage decomposition for blind adaptive parameter estimation in the Krylov subspace with the code-constrained constant modulus (CCM) design criterion. Based on constrained optimization of the constant modulus cost function and utilizing the Lanczos algorithm and Arnoldi-like iterations, a multistage decomposition is developed for blind parameter estimation. A family of computationally efficient blind adaptive reduced-rank stochastic gradient (SG) and recursive least squares (RLS) type algorithms along with an automatic rank selection procedure are also devised and evaluated against existing methods. An analysis of the convergence properties of the method is carried out and convergence conditions for the reduced-rank adaptive algorithms are established. Simulation results consider the application of the proposed techniques to the suppression of multiaccess and intersymbol interference in DS-CDMA systems