Victor Barroso

Robotic ocean vehicles for marine science applications: the European ASIMOV project

The key objective of the ASIMOV project is the development and integration of advanced technological systems to achieve coordinated operation of an Autonomous Surface Craft (ASC) and an Autonomous Underwater Vehicle (AUV) while ensuring a fast communication link between the two vehicles. The ASC/AUV ensemble is being used to study the extent of shallow water hydrothermalism and to determine the patterns of community diversity at vents in the D. Joao de Castro (DJC) bank in the Azores.

Instantaneous frequency of multicomponent signals

The failure of the traditional definition of instantaneous frequency (IF/sub t/) in the multicomponent case has been often reported. We determine the reasons for the failure of this definition. This enables us to understand and integrate all previously reported cases in a simple unified theory. As a direct consequence, we are able to extrapolate and predict the behavior of the traditional definition for any type of multicomponent signal.

Time-reversed OFDM communication in underwater channels

Time reversal is a feedback wave focusing technique that can be used to transparently compensate for multipath distortion in digital communications over several types of physical propagation media, such as radio or acoustic channels. While much of the work on this topic has focused on coherent communication through single-carrier modulation, time reversal can in fact be applied to any signaling scheme. This work discusses issues related to protocol design and data modulation/demodulation when multicarrier signals are used, with an emphasis on underwater communication. It is shown that focusing information can be derived at the transmitter array by prefiltering a single observed broadband channel probe, thus streamlining the design of communication protocols. Conventional prefix-based demodulation can be achieved for sufficiently well focused OFDM signals, but this may be somewhat demanding in terms of transmitter hardware. A receiver architecture based on multiple-input/multiple-output decision-feedback equalization is proposed when few transmit elements are used, creating significant residual intersymbol interference (ISI).

Closed-form blind channel identification and source separation in SDMA systems through correlative coding

We address the problem of blind identification of multiuser multiple-input multiple-output (MIMO) finite-impulse response (FIR) digital systems. This problem arises in spatial division multiple access (SDMA) architectures for wireless communications. We present a closed-form, i.e., noniterative, consistent estimator for the MIMO channel based only on second-order statistics. To obtain this closed form we introduce spectral/correlation asymmetry between the sources by filtering each source output with adequate correlative filters. Our algorithm uses the closed form MIMO channel estimate to cancel the intersymbol interference (ISI) due to multipath propagation and to discriminate between the sources at the wireless base station receiver. Simulation results show that, for single-user channels, this technique yields better channel estimates in terms of mean-square error (MSE) and better probability of error than a well-known alternative method. Finally, we illustrate its performance for MIMO channels in the context of the global system for mobile communications (GSM) system.

Closed-form correlative coding (CFC/sub 2/) blind identification of MIMO channels: isometry fitting to second order statistics

We present a blind closed-form consistent channel estimator for multiple-input multiple-output (MIMO) systems that uses only second order statistics. We spectrally modulate the output of each source by correlative coding it with a distinct filter. The correlative filters are designed to meet the following desirable characteristics: no additional power or bandwidth is required; no synchronization between the sources is needed; the original data rate is maintained. We first prove an identifiability theorem: under a simple spectral condition on the transmitted random processes, the MIMO channel is uniquely determined, up to a phase offset per user, from the second order statistics of the received data. We then develop the closed-form algorithm that attains this identifiability bound. We show that minimum-phase finite impulse response filters with arbitrary memory satisfy our sufficient spectral identifiability condition. This results in a computationally attractive scheme for retrieving the data information sequences after the MIMO channel has been identified. We assess the performance of the proposed algorithms by computer simulations. In particular, the results show that our technique outperforms the previously introduced transmitter-induced conjugate cyclostationarity approach when there are carrier frequency misadjustments.

Intrinsic variance lower bound (IVLB): an extension of the Cramer-Rao bound to Riemannian manifolds

We consider parametric statistical models in which the parameter space /spl theta/ is a connected Riemannian manifold. This mathematical structure on the parameter space subsumes, as special cases, submanifolds of Euclidean spaces appearing in parametric estimation scenarios with a priori smooth deterministic constraints, and quotient spaces (such as Grassmann manifolds) which arise in certain parametric estimation scenarios with ambiguities. The Riemannian structure on the parameter space /spl theta/ turns it into a metric space and the associated Riemannian distance is used here to quantify estimation errors. We present the intrinsic variance lower bound (IVLB) which places a lower limit on the accuracy, measured in terms of the mean-square Riemannian distance, of unbiased estimators taking values in /spl theta/. The IVLB depends both on the curvature of the parameter space and a coordinate-free extension of the well-known Fisher information matrix (FIN). We show that for flat Euclidean spaces, the IVLB collapses to the Cramer-Rao bound (CRB). In this sense, we may interpret the IVLB as a generalization of the CRB for curved parameter spaces. Computer simulations illustrating the application of the IVLB are included.

Blind array channel division multiple access (AChDMA) for mobile communications

The paper introduces array channel division multiple access (AChDMA), which is a new blind algorithm for advanced SDMA in mobile communications systems. As an SDMA technique, AChDMA increases the system capacity by improving its time and frequency reuse. Being a blind algorithm, it requires no training sequences, previously known directions of arrival, or user codes, AChDMA separates the moving sources by tracking their multipath configuration and resolving their distinct generalized steering vectors. It maximizes a finite mixture log-likelihood function, combining an efficient initialization procedure with an EM-based algorithm that provides fast convergence to the global maximum. AChDMA reconstructs the mobile data sequences using only internal variables of the EM algorithm. These characteristics and its parallel structure make AChDMA suitable for real-time mobile communications. We test AChDMA with synthetic data in a number of different scenarios, illustrating the ability of the blind algorithm to separate and track in time the moving sources, and showing its good performance in a variety of practical situations.

Noncoherent Communication in Multiple-Antenna Systems: Receiver Design and Codebook Construction

In this paper, we address the problem of space-time codebook design for noncoherent communications in multiple-antenna wireless systems. In contrast with other approaches, the channel matrix is modeled as an unknown deterministic parameter at both the receiver and the transmitter, and the Gaussian observation noise is allowed to have an arbitrary correlation structure, known by the transmitter and the receiver. In order to handle the unknown deterministic space-time channel, a generalized likelihood ratio test (GLRT) receiver is considered. A new methodology for space-time codebook design under this noncoherent setup is proposed. It optimizes the probability of error of the GLRT receivers detector in the high signal-to-noise ratio (SNR) regime by solving a high-dimensional nonlinear nonsmooth optimization problem in a two-step approach. First, a convex semidefinite programming (SDP) relaxation of the codebook design problem yields a rough estimate of the optimal codebook. This is then refined through a geodesic descent optimization algorithm that exploits the Riemannian geometry imposed by the power constraints on the space-time codewords. The results obtained through computer simulations illustrate the advantages of our method. For the specific case of spatio-temporal white observation noise, our codebook constructions replicate the performance of state-of-the-art known solutions. The main point here is that our methodology permits extending the codebook construction to any given correlated noise environment. The simulation results show good performance of these new designed codes in colored noise setups.

Definitions of Instantaneous Frequency under physical constraints

Abstract Villes definition of Instantaneous Frequency (IF), even though widely used and accepted, fails in most cases of practical interest. This failure has been often reported, namely in the multicomponent case. In this paper, we will analyze the advantages and shortcomings of that definition, and determine when and why it fails, integrating all previously reported cases of failure in a simple unified theory. We will also be able to extrapolate and predict the behavior of the traditional definition for any type of multicomponent signal. Alternative definitions of IF are discussed.

Newton Method for Riemannian Centroid Computation in Naturally Reductive Homogeneous Spaces

We address the problem of computing the Riemannian centroid of a constellation of points in a naturally reductive homogeneous manifold. We note that many interesting manifolds used in engineering (such as the special orthogonal group, Grassman, sphere, positive definite matrices) possess this structure. We develop an intrinsic Newton scheme for the centroid computation. This is achieved by exploiting a formula that we introduce for obtaining the Hessian of the squared Riemannian distance on naturally reductive homogeneous spaces. Some results of finding the centroid of a constellation of points in these spaces are presented, which evidence the quadratic convergence of the Newton method derived herein. These computer simulation results show that, as expected, the Newton method has a faster convergence rate than the usual gradient-based approaches