A. van Veen

An analytical constant modulus algorithm

Iterative constant modulus algorithms such as Godard (1980) and CMA have been used to blindly separate a superposition of cochannel constant modulus (CM) signals impinging on an antenna array. These algorithms have certain deficiencies in the context of convergence to local minima and the retrieval of all individual CM signals that are present in the channel. We show that the underlying constant modulus factorization problem is, in fact, a generalized eigenvalue problem, and may be solved via a simultaneous diagonalization of a set of matrices. With this new analytical approach, it is possible to detect the number of CM signals present in the channel, and to retrieve all of them exactly, rejecting other, non-CM signals. Only a modest amount of samples is required. The algorithm is robust in the presence of noise and is tested on measured data collected from an experimental set-up.

Subspace-based signal analysis using singular value decomposition

A unified approach is presented to the related problems of recovering signal parameters from noisy observations and identifying linear system model parameters from observed input/output signals, both using singular value decomposition (SVD) techniques. Both known and new SVD-based identification methods are classified in a subspace-oriented scheme. The SVD of a matrix constructed from the observed signal data provides the key step in a robust discrimination between desired signals and disturbing signals in terms of signal and noise subspaces. The methods that are presented are distinguished by the way in which the subspaces are determined and how the signal or system model parameters are extracted from these subspaces. Typical examples, such as the direction-of-arrival problem and system identification from input/output measurements, are elaborated upon, and some extensions to time-varying systems are given. >

Joint angle and delay estimation using shift-invariance techniques

In a multipath communication scenario, it is often relevant to estimate the directions and relative delays of each multipath ray. We derive a closed-form subspace-based method for the simultaneous estimation of these parameters from an estimated channel impulse response, using knowledge of the transmitted pulse shape function. The algorithm uses a two-dimensional (2-D) ESPRIT-like shift-invariance technique to separate and estimate the phase shifts due to delay and direction of incidence with automatic pairing of the two parameter sets. Improved resolution is obtained by enlarging the data matrix with shifted and conjugated copies of itself.

Analysis of positron profiling data by means of ‘‘VEPFIT’’

The program VEPFIT is presented which extrats the relevant parameters from positron measurements on implanted materials and layered structures. Measurements of the Doppler Broadening parameter S and the positronium fraction F vs the energy of the incident positrons are analyzed by means of a semi‐linear fitting procedure. The principles of the analysis method are given and the performance of the program on artificially and experimentally obtained data is demonstrated. Fitting strategies are outlined and the accuracy of the fitting results is discussed.

A subspace approach to blind space-time signal processing for wireless communication systems

The two key limiting factors facing wireless systems today are multipath interference and multiuser interference. In this context, a challenging signal processing problem is the joint space-time equalization of multiple digital signals transmitted over multipath channels. We propose a blind approach that does not use training sets to estimate the transmitted signals and the space-time channel. Instead, this approach takes advantage of spatial and temporal oversampling techniques and the finite alphabet property of digital signals to determine the user symbol sequences. The problem of channels with largely differing and ill-defined delay spreads is discussed. The proposed approach is tested on actual channel data.

Helium desorption/permeation from bubbles in silicon: A novel method of void production

Abstract The annealing behaviour of helium bubbles formed by helium implantation into silicon has been studied using both helium desorption spectroscopy and transmission electron microscopy. The combination of these techniques has demonstrated that helium can permeate out of bubbles in silicon during annealing to leave behind empty cavities.

Estimation of multipath parameters in wireless communications

In a parametric multipath propagation model, a source is received by an antenna array via a number of rays, each described by an arrival angle, a delay, and a fading parameter. Unlike the fading, the angles and delays are stationary over long time intervals. This fact is exploited in a new subspace-based high-resolution method for simultaneous estimation of the angle/delay parameters from multiple estimates of the channel impulse response. A computationally expensive optimization search can be avoided by using an ESPRIT-like algorithm. Finally, we investigate certain resolution issues that take the fact that the source is bandlimited into account.

Analog Beamforming in MIMO Communications With Phase Shift Networks and Online Channel Estimation

In multiple-input multiple-output (MIMO) systems, the use of many radio frequency (RF) and analog-to-digital converter (ADC) chains at the receiver is costly. Analog beamformers operating in the RF domain can reduce the number of antenna signals to a feasible number of baseband channels. Subsequently, digital beamforming is used to capture the desired user signal. In this paper, we consider the design of the analog and digital beamforming coefficients, for the case of narrowband signals. We aim to cancel interfering signals in the analog domain, thus minimizing the required ADC resolution. For a given resolution, we will propose the optimal analog beamformer to minimize the mean squared error between the desired user and its receiver estimate. Practical analog beamformers employ only a quantized number of phase shifts. For this case, we propose a design technique to successively approximate the desired overall beamformer by a linear combination of implementable analog beamformers. Finally, an online channel estimation technique is introduced to estimate the required statistics of the wireless channel on which the optimal beamformers are based.

VEPFIT applied to depth profiling problems

Abstract The modelling and fitting program VEPFIT has been employed in recent years for resolving defect depth profiles and depth structures of deposited layers. Recent activities concerning program development include the testing of a new model of MOS systems for implementation into VEPFIT and a study into decomposition of Doppler-broadened photo-peaks. Further methods are proposed using VEPFIT for analysis of lifetime measurements and for modelling of positron transport with multi-energy groups.

Algebraic methods for deterministic blind beamforming

Deterministic blind beamforming algorithms try to separate superpositions of source signals impinging on a phased antenna array by using the deterministic properties of the signals or the channels such as their constant modulus or directions-of-arrival. Progress in this area has been abundant over the past ten years and has resulted in several powerful algorithms. Unlike optimal or adaptive methods, the algebraic methods discussed in this review act on a fixed block of data and give closed-form expressions for beamformers by focusing on algebraic structures. This typically leads to subspace estimation and generalized eigenvalue problems. After introducing a simple and widely used multipath channel model, the paper provides an anthology of properties that are available, as well as generic algorithms that exploit them.