Frequency domain blind MIMO system identification based on second and higher order statistics

We present a novel frequency-domain framework for the identification of a multiple-input multiple-output (MIMO) system driven by white, mutually independent, unobservable inputs. The system frequency response is obtained based on singular value decomposition (SVD) of a matrix constructed based on the power-spectrum and slices of polyspectra of the system output. By appropriately selecting the polyspectra slices, we can create a set of such matrices, each of which could independently yield the solution, of they could all be combined in a joint diagonalization scheme to yield a solution with improved statistical performance. The freedom to select the polyspectra slices allows us to bypass the frequency-dependent permutation ambiguity that is usually associated with frequency domain SVD, while at the same time allows us compute and cancel the phase ambiguity. An asymptotic consistency analysis of the system magnitude response estimate is performed     

Higher order spectra based deconvolution of ultrasound images

We address the problem of improving the spatial resolution of ulrasound images through blind deconvolution. The ultrasound image formation process in the RF domain can be expressed as a spatio-temporal convolution between the tissue response and the ultrasonic system response, plus additive noise. Convolutional components of the dispersive attenuation and aberrations introduced by propagating through the object being imaged are also incorporated in the ultrasonic system response. Our goal is to identify and remove the convolutional distortion in order to reconstruct the tissue response, thus enhancing the diagnostic quality of the ultrasonic image. Under the assumption of an independent, identically distributed, zero-mean, non-Gaussian tissue response, we were able to estimate distortion kernels using bicepstrum operations on RF data. Separate 1D distortion kernels were estimated corresponding to axial and lateral image lines and used in the deconvolution process. The estimated axial kernels showed similarities to the experimentally measured pulse-echo wavelet of the imaging system. Deconvolution results from B-scan images obtained with clinical imaging equipment showed a 2.5-5.2 times gain in lateral resolution, where the definition of the resolution has been based on the width of the autocovariance function of the image. The gain in axial resolution was found to be between 1.5 and 1.9     

Co-channel interference modeling and analysis in a Poisson field of interferers in wireless communications

The paper considers interference in a wireless communication network caused by users that share the same propagation medium. Under the assumption that the interfering users are spatially Poisson distributed and under a power-law propagation loss function, it has been shown in the past that the interference instantaneous amplitude at the receiver is /spl alpha/-stable distributed. Past work has not considered the second-order statistics of the interference and has relied on the assumption that interference samples are independent. In this paper, we provide analytic expressions for the interference second-order statistics and show that depending on the properties of the users' holding times, the interference can be correlated. We provide conditions under which the interference becomes m-dependent, /spl phi/-mixing, or long-range dependent. Finally, we present some implications of our theoretical findings on signal detection.     

Noncausal nonminimum phase ARMA modeling of non-Gaussian processes

A method is presented for the estimation of the parameters of a noncausal nonminimum phase ARMA model for non-Gaussian random processes. Using certain higher order cepstra slices, the Fourier phases of two intermediate sequences (h_min(n) and h_max(n)) can be computed, where h_min(n) is composed of the minimum phase parts of the AR and MA models, and h_max(n) of the corresponding maximum phase parts. Under the condition that there are no zero-pole cancellations in the ARMA model, these two sequences can be estimated from their phases only, and lead to the reconstruction of the AR and MA parameters, within a scalar and a time shift. The AR and MA orders do not have to be estimated separately, but they are by product of the parameter estimation procedure. Through simulations it is shown that, unlike existing methods, the estimation procedure is fairly robust if a small order mismatch occurs. Since the robustness of the method in the presence of additive noise depends on the accuracy of the estimated phases of h_min(n) and h_max(n), the phase errors due to finite length data are studied and their statistics are derived     

A family of frequency- and time-domain contrasts for blind separation of convolutive mixtures of temporally dependent signals

This paper addresses the problem of blind separation of convolutive mixtures via contrast maximization. New frequency domain contrast functions are constructed based on higher order spectra of the observations. They allow to separate mixtures of sources that are spatially independent and temporally possibly nonlinear processes. Using Parseval's formula, the former criteria yield a general class of time-domain contrasts, which extends to the convolutive case results that have been previously obtained in the context either of instantaneous mixtures or of independent and identically distributed (i.i.d.) sources. A Monte Carlo simulation study is carried out for comparison between the different contrasts, thus providing a guideline about the choice of an appropriate contrast.     

Signal reconstruction from the phase of the bispectrum

The authors present a simple procedure, the bispectrum signal reconstruction (BSR) algorithm, to recover the Fourier phase of a signal from the phase of its bispectrum. By simple analogy, a procedure that recovers the Fourier magnitude of a signal from the magnitude of its bispectrum is also presented. In addition, the authors propose an iterative scheme, the bicepstrum iterative reconstruction algorithm (BIRA), for the reconstruction of a finite impulse response (FIR) sequence from only the phase of its bispectrum, and they demonstrate how some a priori information on the energy of the cepstra coefficients can improve significantly the convergence rate of the algorithm. Both schemes are based on the key observation that the differences of the bispectrum coefficients contain all the information concerning the Fourier phase of the signal, whereas their sums contain the Fourier-magnitude information     

On modeling the tissue response from ultrasonic B-scan images

The authors model tissue as a collection of point scatterers embedded in a uniform media, and show that the higher-order statistics (HOS) of the scatterer spacing distribution can be estimated from digitized radio frequency (RF) scan line segments and be used in obtaining tissue signatures. The authors assume that RF echoes are non-Gaussian, on the grounds of empirical/theoretical justifications presented in the literature. Based on their model for tissue microstructure, the authors develop schemes for the estimation of reasonable periodicity as well as correlations among nonperiodic scatterers, Using HOS of the scattered signal, the authors define as tissue "color" a quantity that describes the scatterer spatial correlations, show how to evaluate it from the higher-order correlations of the digitized RF scan line segments, and investigate its potential as a tissue signature. The tools employed, i.e., HOS, were chosen as the most appropriate ones because they suppress Gaussian processes, such as the one arising from the diffused scatterers. HOS, unlike second-order statistics, also preserve the Fourier-phase of the signature, the color of the tissue response. Working on simulated and clinical data, the authors show that the proposed periodicity estimation technique is superior to the widely used power spectrum and cepstrum techniques in terms of the accuracy of estimations. The authors also show that even when there is no significant periodicity in data, they are still able to characterize tissues using signatures based on the higher-order cumulant structure of the scatterer spacing distribution.     

Extending network lifetime for ALLIANCES

ALLIANCES is a newly proposed wireless MAC protocol that exploits the cooperation of source nodes and relay nodes to resolve collisions and further improve throughput. Until now, ALLIANCES did not consider energy, which is the most precious commodity of mobile communications. In this paper, we propose an energy-conserving version of ALLIANCES that introduces a sleep state. Our analytical results show that the energy-conserving model can save at least 54% of energy consumption when compared to the original energy model (referred to as the basic energy model in this paper). Because a relay selection scheme is a significant piece of ALLIANCES, directly affecting the throughput and the energy distribution, we also propose an energy-aware relay selection scheme (ERS). ERS maintains the performance benefits of the previously proposed location relay selection scheme (LRS), but more evenly distributes the amount of energy remaining throughout the network. We implement our energy-conserving model and ERS in the popular network simulator (NS-2) to evaluate and compare the energy remaining at each node and the network’s throughput when using LRS and ERS. Simulation results show that the energy-conserving model can save up to 80% of energy without nodes taking longer to communicate. In addition, using the energy-conserving model, ERS provides 23.3% longer lifetime of the network than LRS, without noticeable throughput degradation.