We first discuss two approaches for designing complex wavelet packets which can be used as orthogonal carriers for modulations like QAM and PM, and then compare the performance of the wavelet packet based modulation scheme with that of discrete multitone modulation using DFT bases. The results show that the wavelet packet based scheme yields a lower average bit error probability compared to the DFT based scheme. The improved performance of the wavelet packet based scheme is because of the spectrally contained nature of the wavelet packet bases which are under the control of the designer.

Recently, Ding (see IEEE Signal Processing Lett., vol.3, p.150-2, May 1996) has pointed out several classes of multipath channels that are not blindly identifiable from fractionally spaced samples and second-order cyclic spectra. In this letter, we consider the blind identification problem using multiple antennas and show that the multipath channels will not give rise to any common roots among the subchannels formed from the antennas and, hence, they can be identified from second-order statistics. In our development, we will point out the role of band-limitedness of the channels in characterizing different classes.

In this letter, we propose a method for blind separation of d co-channel BPSK signals arriving at an antenna array. Our method involves two steps. In the first step, the received data vectors at the output of the array is grouped into 2/sup d/ clusters. In the second step, we assign the 2/sup d/ d-tuples with /spl plusmn/1 elements to these clusters in a consistent fashion. From the knowledge of the cluster to which a data vector belongs, we estimate the bits transmitted at that instant. Computer simulations are used to study the performance of our method.>

An adaptive approach for estimating all (or some) of the orthogonal eigenvectors of the data covariance matrix (of a time series consisting of real narrowband signals in additive white noise) is presented. The inflation approach is used to estimate each of these vectors as minimum eigenvectors (eigenvectors corresponding to the minimum eigenvalue) of appropriately constructed symmetric positive definite matrices. This reformulation of the problem is made possible by the fact that the problem of estimating the minimum eigenvector of a symmetric positive definite matrix can be restated as the unconstrained minimization of an appropriately constructed nonlinear nonconvex cost function. The modular nature of the algorithm that results from this reformation makes the proposed approach highly parallel, resulting in a high-speed adaptive approach for subspace estimation.>