Adaptive nonlinear filters for narrow-band interference suppression in spread-spectrum CDMA systems

TitleAdaptive nonlinear filters for narrow-band interference suppression in spread-spectrum CDMA systems
Publication TypeJournal Article
Year of Publication1999
AuthorsKrishnamurthy, V., and A. Logothetis
JournalCommunications, IEEE Transactions on
Volume47
Pagination742 -753
Date Publishedmay.
ISSN0090-6778
Keywordsadaptive Kalman filters, adaptive nonlinear filters, adaptive signal processing, approximate conditional mean filter, autoregressive processes, BER, bit error rate, code division multiple access, computational cost, finite state Markov chain, Gaussian autoregressive process, Gaussian processes, HMM estimator, HMM-KF algorithm, i.i.d. process, independently identically distributed process, interference suppression, Kalman filter, Markov processes, narrow-band interference suppression, noise, nonlinear filtering techniques, nonlinear filters, observation noise variance, optimisation, parameter estimation, parameter estimator, radiofrequency interference, received sampled signal, recursive expectation maximization algorithm, recursive hidden Markov model, signal-to-noise ratio, simulation studies, SNR enhancement, spread spectrum communication, spread-spectrum CDMA systems, spread-spectrum signal, zero-mean white Gaussian process
Abstract

This paper presents a novel nonlinear filter and parameter estimator for narrow band interference suppression in code division multiple access spread-spectrum systems. As in the article by Rusch and Poor (1994), the received sampled signal is modeled as the sum of the spread-spectrum signal (modeled as a finite state independently identically distributed (i.i.d.) process-here we generalize to a finite state Markov chain), narrow-band interference (modeled as a Gaussian autoregressive process), and observation noise (modeled as a zero-mean white Gaussian process). The proposed algorithm combines a recursive hidden Markov model (HMM) estimator, Kalman filter (KF), and the recursive expectation maximization algorithm. The nonlinear filtering techniques for narrow-band interference suppression presented in Rusch and Poor and our proposed HMM-KF algorithm have the same computational cost. Detailed simulation studies show that the HMM-KF algorithm outperforms the filtering techniques in Rusch and Poor. In particular, significant improvements in the bit error rate and signal-to-noise ratio (SNR) enhancement are obtained in low to medium SNR. Furthermore, in simulation studies we investigate the effect on the performance of the HMM-KF and the approximate conditional mean (ACM) filter in the paper by Rusch and Poor, when the observation noise variance is increased. As expected, the performance of the HMM-KF and ACM algorithms worsen with increasing observation noise and number of users. However, HMM-KF significantly outperforms ACM in medium to high observation noise

URLhttp://dx.doi.org/10.1109/26.768768
DOI10.1109/26.768768

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