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We consider the uplink of collaborative wireless communication systems, where multiple relay terminals decode the signal of a nearby user and forward it to a distant single-antenna base station. We present a collaborative uplink transmit beamforming strategy that can be employed at the relay terminals to provide robustness against uncertainties in the channel state information. The proposed beamforming scheme is obtained using the available knowledge about the second-order statistics of the channel and the possibly erroneous channel state information. The beamforming weight vector is derived by minimizing the total transmitted power subject to a constraint that preserves the received signal at the base station for all the channel realizations within a prescribed uncertainty set. We present two beamforming algorithms based on different mathematical descriptions of the uncertainty set. Both algorithms can be applied to fine-of-sight (LOS) propagation and fiat-fading channels. In the first algorithm, the robust beamforming vector is computed at the base station using the uplink data and fed back to the cooperating relay terminals. This centralized processing scheme allows any additional convex constraint to be easily incorporated into the beamforming strategy. In the second algorithm, the beamforming vector of each terminal is locally computed using the available knowledge about the terminal's channel and a single parameter (Lagrange multiplier) that is broadcast from the base station to all the cooperating terminals. Simulation results are presented, showing the superior performance of our proposed algorithms compared with classical transmit beamforming techniques in both LOS propagation and fiat-fading channels. Index Terms--Array signal processing, convex optimization, cooperative systems, distributed beamforming.

The arrival of new data services for wireless mobile communications requires an efficient use of the available bandwidth. Interference-limited cellular systems based on codedivision multiple access (CDMA) can benefit from multiuser detection (MUD) and beamforming with antenna array to reduce multiple-access interference. Group-based techniques have been proposed to reduce the complexity of space--time MUD and have been shown to provide a performance--complexity tradeoff between matched filtering and full MUD. In this paper, the intergroup interference, which is a limiting factor in group-based systems, is reduced using multistage parallel interference cancellation after group-based minimum mean square error (MMSE) linear filtering. In addition, the extra resources that are available at the receiver are exploited by sharing users among groups. The proposed receiver is shown to converge, as the number of stages increases, to the full space--time MMSE linear MUD filter. The results show that the new approach provides bit error rate (BER) performance close to the full MUD receiver at a fraction of the complexity. Index Terms--Code-division multiple access (CDMA), group-based detection, multiuser detection (MUD), space--time systems.

A systematic study of the subspace-based blind channel estimation method is presented. The application of the proposed estimator to a downlink code division multiple access (CDMA) system operating in a frequency-selective fading channel with negligible intersymbol interference (ISI) is considered.

We present a method that estimates three-dimensional statistical maps for electroencephalogram (EEG) source localization. The maps assess the likelihood that a point in the brain contains a dipolar source, under the hypothesis of one, two or three activated sources. This is achieved by examining all combinations of one to three dipoles on a coarse grid and attributing to each combination a score based on an F statistic. The probability density function of the statistic under the null hypothesis is estimated nonparametrically, using bootstrap resampling. A theoretical F distribution is then fitted to the empirical distribution in order to allow correction for multiple comparisons. The maps allow for the systematic exploration of the solution space for dipolar sources. They permit to test whether the data support a given solution. They do not rely on the assumption of uncorrelated source time courses. They can be compared to other statistical parametric maps such as those used in functional magnetic resonance imaging (fMRI). Results are presented for both simulated and real data. The maps were compared with LORETA and MUSIC results. For the real data consisting of an average of epileptic spikes, we observed good agreement between the EEG statistical maps, intracranial EEG recordings, and fMRI activations. Index Terms--Bootstrap resampling, dipole modeling, EEG/MEG source analysis, electroencephalography, inverse problems, model selection, source scanning, statistical map.

A novel Recursive Least Squares Constant Modulus Algorithm (RLS-CMA), where the modulus power of the array output can take on arbitrary positive real values based on the Constant Modules (CM) criterion is discussed. The RLS-CMA has a significantly faster convergence rate and better tracking ability.

On the asymptotic convergence and numerical stability of the Proteus eigenvalue decomposition (EVD) trackers. The subspace tracking algorithms Proteus-1 and Proteus-2 have been studied using the ODE method. Analysis has been based on an alternative ODE system and different Lyapunov functions overcomes certain restrictions and so offers more insight in the operation of the Proteus algorithms and other related subspace trackers.

A study was conducted to characterize novel eigenvalue decomposition tracking algorithms that can be used for subspace tracking. A series of preprocessing steps were analyzed in the study. The algorithms were initialized to determine full matrix estimates and positive eigenvalues covering the power range. Results indicated that the algorithms did not support error buildup or numerical instability.

The study proposes a MUSIC-type distributed signal parameter estimator which is based on reducing a norm of the transformed noise eigenvectors in the source subspace. This technique resembles the MUSIC algorithm with an array manifold which is an important part of location vector weighed with the angular signal density. The efficient dimension of the signal subspace is a function of the product of the extension width, wavelength, array aperture and the signal location. The resolution threshold for the new method is lower than the MUSIC algorithm.

New algorithms for adaptive eigendecomposition of time-varying data covariance matrices are developed based on first-order perturbation analysis. The first-order perturbations enable the estimation of rank-one update for the covariance matrix with exponential windows. The algorithms have varying degrees of complexity, which is caused by the different assumptions used on the eigen structure. The standard of performance is obtained from the computer simulations of the eigendecompositions.

In this paper, we present a new, computationally efficient simulator for time-varying multi-path (fast fading) vector channels that can be used to evaluate the performance of antenna array wireless receivers at the base station. The development of the simulator is based on the emulation of the spatio-temporal correlation properties of the vector channel. The channel is modeled as a single-input multi-output finite impulse response (FIR) system with time-varying coefficients which are obtained via the application of a space-time correlation shaping transformation on some independent random sequences. The various parts of the new simulator are detailed and channel simulation realizations are presented and commented.

A novel focusing approach based on the least-squares and total least-squares methods for the localization of wideband signals is described. New focusing matrices that are constant under multiplication by their Hermitian transpose are utilized to prevent the focusing loss. It is shown that the proposed technique's computational complexity is significantly lower than that for the rotational signal-subspace algorithm. In addition, the focusing gain provided by the new approach is larger than the focusing gain of the rotational signal-subspace method. The new algorithm also has a smaller resolution signal-to-noise ratio.