SAM-6.1

Beamforming and Multiuser Detection in CDMA Systems with External Interferences
Olga Muñoz, Juan A Fernandez-Rubio (TSC Department.Polytechnic University of Catalunya)

Multiuser detection has been investigated to mitigate near-far effect in CDMA systems. Antenna arrays have shown to provide spatial diversity and cancel undesired signals. In this paper we consider the synergy of both multiuser detection and antenna arrays for the base station of a CDMA system. The receiver we proposed consists of the known multiuser decorrelator, which cancels multiple-access interferences followed by a beamformer for each user, which cancels the external interferences. This receiver adds an extra branch to the decorrelator. This additional branch, corresponding to a fictitious user with an unused code and zero power, allows to estimate the external interference signal subspace and compute a suitable beamforming weight-vector that cancels the external interferences. The receiver is also extended to the asynchronous case and all of this without any training signal or any a priori spatial information.

SAM-6.2

Joint Angle and Delay Estimation for DS-CDMA with Application to Reduced Dimension Space-Time RAKE Receivers
Yung-Fang Chen, Michael D Zoltowski (School of Electrical and Computer Engineering, Purdue University)

In this paper, we propose an algorithm for joint estimation of the angle of arrival (AOA) and delay of each dominant multipath for the desired user for use in a reduced dimension space-time RAKE receiver for DS-CDMA communications that is ``near-far" resistant. After we estimate the desired spatio-frequency signal vector, we propose the 2D unitary ESPRIT algorithm as our estimator which provides closed-form as well as automatically paired AOA-delay estimates. We effectively have a single snapshot of 2D data and thus require 2D smoothing for extracting multiple snapshots. The comparative performance of two 2D smoothing schemes, pre-eigenanalysis and post-eigenanalysis 2D smoothing, is discussed. The space-time data model for the IS-95 uplink is presented. The performance of a reduced dimension space-time RAKE receiver for the IS-95 uplink using the AOA-delay estimates is assessed through Monte-Carlo simulations.

SAM-6.3

Maximum likelihood separation of phase modulated signals
Amir Leshem (Faculty of Information Technology and Systems, Delft University of Technology)

In this paper we present a Newton scoring algorithm for the maximum likelihood separation and direction of arrival estimation of constant modulus signals, using a calibrated array. The main technical step is the inversion of the Fisher information matrix, and an analytic formula for the update step in the Newton method. We present the algorithm based on the derived update and discuss potential initializations. We also present the computational complexity of the update. Finally we present simulation results comparing the method to the ESPRIT and the CM-DOA.

SAM-6.4

Localization of a Distributed Source Which is "Partially Coherent", Modeling and Cramer Rao Bounds.
Raviv Raich, Jason M Goldberg, Hagit Messer (Tel Aviv University)

The problem of using antenna array measurements to estimate the bearing of a mobile communications user surrounded by local scatterers is considered. The concept of ``partial coherence" is introduced to account for the temporal as well as spatial correlation effects often encountered in mobile radio propagation channels. A simple, intuitive parametric model for temporal channel correlation is presented. The result is an overall spatio-temporal channel model which is more realistic than formerly proposed models (which assume either full or zero temporal channel correlation). Thus, previously posed bearing estimation problems for a ``distributed" or ``scattered" source are generalized to a joint spatio-temporal parameter estimation problem. A study of the associated Cramer-Rao Bound for the case of known transmitted signal of constant modulus indicates that the inherent accuracy limitations associated with this generalized problem lie somewhere between the cases of zero and full temporal correlation and become more severe as temporal channel correlation increases.

SAM-6.5

Robust Constant Modulus Arrays Based on Fractional Lower-Order Statistics
Marilli Rupi (Laboratorio Elaborazione dei Segnali e Telematica, Dipartimento di Ingegneria Elettronica, Universita degli Studi di Firenze, Firenze, FI 50139, ITALY), Panagiotis Tsakalides (Signal and Image Processing Institute, Department of Electrical Engineering - Systems, University of Southern California, Los Angeles, CA 90089-2564), Chrysostomos L Nikias (Integrated Media Systems Center, Department of Electrical Engineering - Systems, University of Southern California, Los Angeles, CA 90089-2564), Enrico Del Re (Laboratorio Elaborazione dei Segnali e Telematica, Dipartimento di Ingegneria Elettronica, Universita degli Studi di Firenze, Firenze, FI 50139, ITALY)

This paper addresses the problem of blind equalization for digital communications using an array of sensors at the receiver to copy constant modulus signals in the presence of heavy-tailed additive channel noise. First, we demonstrate the negative effects of channel noise to the original CMA cost function in terms of convergence. Then, we introduce a new CMA criterion based on the fractional lower-order statistics (FLOS) of the received data. The proposed criterion is able to mitigate impulsive noise at the receiver and at the same time restores the constant modulus character of the transmitted communication signal. We perform an analytical study of the properties of the new cost function and we illustrate its convergence behavior through computer simulations.

SAM-6.6

Multi-Step Linear Predictors-Based Blind Equalization of Multiple-Input Multiple-Output Channels
Jitendra K Tugnait, Bin Huang (Auburn University)

Blind equalization of MIMO (multiple-input multiple-output) communications channels is considered using primarily the second-order statistics of the data. In several applications the underlying equivalent discrete-time mathematical model is that of a MIMO linear system where the number of inputs equals the number of users (sources) and the number of outputs is related to the number of sensors and the sampling rate. Recently we investigated the structure of multi-step linear predictors for IIR/FIR MIMO systems with irreducible transfer functions and derived an upper bound on its length (Tugnait, 1998 IEEE DSP Workshop). In past multi-step linear predictors have been considered in the literature only for single-input multiple-output models. In this paper we apply the results of (Tugnait, 1998 IEEE DSP Workshop) for blind equalization of MIMO channels using MMSE linear equalizers. Extensions to the case where the ``subchannel'' transfer functions have common zeros/factors is also investigated. An illustrative simulation example is provided

SAM-6.7

Multiuser Blind Identification Using a Linear Parameterization of the Channel Matrix and Second-Order Statistics
Thomas P Krauss, Michael D Zoltowski (Purdue University)

We observe that the channel matrix in the standard multiuser, multichannel (MIMO) digital communications model is linear in the channel coefficients. Also, recent work incorporating ``basis functions'' suggests that the multipath channel itself is in a subspace formed by delayed versions of the transmission pulse. Hence the channel matrix is linear in the coefficients of this subspace. We propose two algorithms based on the sample covariance matrix of the received signal (i.e., second-order statistics) that take advantage of this linear parameterization: a new identification algorithm that estimates the outer product of the model coefficients via multiplication by a predetermined matrix, and a multiuser version of the previously presented ``subspace method'' that employs knowledge of the transmission pulse. While both methods are superior to the original non-parameterized subspace method in terms of computation and performance, the new method requires less comptation and in some cases outperforms the other.

SAM-6.8

A De-Rotation Approach to the Blind Separation of Synchronous Co-Channel BPSK Signals
Mei Meng, Naushad H Dowlut, Tim N Davidson (Communications Research Laboratory, McMaster University)

In this paper we propose a simple algorithm for the blind separation of $m$ synchronous co-channel BPSK signals by an array of $n$ receiving antennas. We exploit the geometric implications of the finite alphabet property of BPSK signals. After a standard channel whitening step, the noiseless received data vectors describe an $m$-cube, which is a rotated version of the hypercube defined by the $2^m$ distinct vectors of $\pm 1$'s corresponding to all the possible combinations of the states of the sources. We provide a simple procedure to estimate the vertices of this rotated hypercube in the noisy case and then show how the rotation matrix can be determined up to a sign and permutation of its columns. De-rotation of the channel-whitened data results in source separation. Simulations show the good performance of our proposed technique.

< SAM-5 SAM-7 >

Last Update: February 4, 1999 Ingo Höntsch