Training Sequence Aided Signature Waveform Estimation

Abstract

In code division multiple access channels multiuser detection techniques are known to be effective strategies to counter the presence of multiuser interference towards improving spectral efficiency. Generally, multiuser detectors can provide excellent performance only when the signature waveforms† of all users are precisely known. Hence, the estimation of signature waveforms is a challenging issue in mobile communication systems. In this paper, we compare the performance of two short training sequence aided signature waveform estimators. One is maximum likelihood type signature waveform estimator that requires the knowledge of spreading sequences and short training sequences. The other estimator is recently proposed based on subspace method and requires the knowledge of training sequences only. Through the simulations, we show the signature waveform estimation performance of both systems and the effect of the estimation error on the performance of a multiuser detector. The complexity comparisons of both systems are also given.