Blind channel estimation for downlink CDMA systems

The problem of channel estimation in code-division multiple-access (CDMA) systems is considered. Using only the spreading code of the user of interest, a technique is proposed to identify the impulse response of the multipath channel from the received data sequence. While existing blind methods suffer from high computational complexity and sensitivity to accurate knowledge of the noise subspace rank, the proposed method overcomes both problems. In particular we estimate the noise subspace by a simple matrix power that is computationally efficient and requires no knowledge of the noise subspace rank. Once an estimate of the noise subspace is available the channel impulse response can be directly identified through a small size (order of the channel) SVD or a least squares approach. Extensive simulations demonstrate similar performance of our method as compared to the existing schemes but at a considerably lower computational cost

Adaptive power techniques for blind channel estimation in CDMA systems

The problem of blind adaptive channel estimation in code-division multiple access (CDMA) systems is considered. Motivated by the iterative power method, which is used in numerical analysis for estimating singular values and singular vectors, we develop recursive least squares (RLS) and least mean squares (LMS) subspace-based adaptive algorithms in order to identify the impulse response of the multipath channel. The schemes proposed in this paper use only the spreading code of the user of interest and the received data and are therefore blind. Both versions (RLS and LMS) exhibit rapid convergence combined with low computational complexity. With the help of simulations, we demonstrate the improved performance of our methods as compared with the already-existing techniques in the literature

Blind adaptive channel estimation in OFDM Systems

We consider the problem of blind channel estimation in zero padding OFDM systems, and propose blind adaptive algorithms in order to identify the impulse response of the multipath channel. In particular, we develop RLS and LMS schemes that exhibit rapid convergence combined with low computational complexity and numerical stability. Both versions are obtained by properly modifying the orthogonal iteration method used in Numerical Analysis for the computation of singular vectors. With a number of simulation experiments we demonstrate the satisfactory performance of our adaptive schemes under diverse signaling conditions

The fast Data Projection Method for stable subspace tracking

In this article we consider the Data Projection Method (DPM), which constitutes a simple and reliable means for adaptively estimating and tracking subspaces. Specifically we propose a fast and numerically robust implementation of DPM. Existing schemes can track subspaces corresponding either to the largest or the smallest singular values. DPM, on the other hand, with a simple change of sign in its step size, can switch from one subspace type to the other. Our fast implementation of DPM preserves the simple structure of the original DPM having also a considerably lower computational complexity. The proposed version provides orthonormal vector estimates of the subspace basis which are numerically stable. In other words, our scheme does not accumulate roundoff errors and therefore preserves orthonormality in its estimates. In fact, our scheme constitutes the only numerically stable, low complexity, algorithm for tracking subspaces corresponding to the smallest singular values. In the case of tracking subspaces corresponding to the largest singular values, our scheme exhibits the fastest convergence-towards-orthonormality among all other subspace tracking algorithms of similar complexity