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In this paper, the conditions for blind identifiability from second-order statistics (SOS) of multiple-input multiple-output (MIMO) channels under orthogonal space--time block coded (OSTBC) transmissions are studied. The main contribution of the paper consists in the proof that, assuming more than one receive antenna, any OSTBC with a transmission rate higher than a given threshold, which is inversely proportional to the number of transmit antennas, permits the blind identification of the MIMO channel from SOS. Additionally, it has been proven that any real OSTBC with an odd number of transmit antennas is identifiable, and that any OSTBC transmitting an odd number of real symbols permits the blind identification of the MIMO channel regardless of the number of receive antennas, which extends previous identifiability results and suggests that any nonidentifiable OSTBC can be made identifiable by slightly reducing its code rate. The implications of these theoretical results include the explanation of previous simulation examples and, from a practical point of view, they show that the only nonidentifiable OSTBCs with practical interest are the Alamouti codes and the real square orthogonal design with four transmit antennas. Simulation examples and further discussion are also provided. Index Terms--Blind identifiability, multiple-input multipleoutput (MIMO) communications, orthogonal space-time block codes (OSTBC), second-order statistics (SOS).

A new deterministic technique is proposed for blind equalization of finite impulse response multiple-input multiple-output (FIR-MIMO) channels. The batch and adaptive algorithms are obtained by reformulating canonical correlation analysis (CCA) as a set of coupled least squares (LS) regression problems.

A new approach is presented for adaptive beamforming that has provided increased robustness against the mismatch problem as well as additional control over the sidelobe level. The computer simulations have shown an improved performance of the support vector machine (SVM)-based beamformer when compared with other robust beamforming methods.

A new criterion is described for effective channel order detection of single-input multiple-output (SIMO) channels. The straightforward combination of both cost functions attains its minimum at the correct channel order even for moderate signal-to-noise ratios (SNRs), while the proposed method works with small data sets, colored signals and channels with small head and tail taps.

A new generalized correlation function is developed that includes the information of both the distribution and that of the time structure of a stochastic process. An attempt is made to find out how this measure is interpreted from a kernel method as well as from information theoretic learning points of view, demonstrating some relevant properties.

This letter proposes a clustering-based approach for solving the underdetermined (i.e., fewer mixtures than sources) postnonlinear blind source separation (PNL BSS) problem when the sources are sparse. Although various algorithms exist for the underdetermined BSS problem for sparse sources, as well as for the PNL BSS problem with as many mixtures as sources, the nonlinear problem in an underdetermined scenario has not been satisfactorily solved yet. The method proposed in this letter aims at inverting the different nonlinearities, thus reducing the problem to linear underdetermined BSS. To this end, first a spectral clustering technique is applied that clusters the mixture samples into different sets corresponding to the different sources. Then, the inverse nonlinearities are estimated using a set of multilayer perceptrons (MLPs) that are trained by minimizing a specifically designed cost function. Finally, transforming each mixture by its corresponding inverse nonlinearity results in a linear underdetermined BSS problem, which can be solved using any of the existing methods. Index Terms--Blind source separation, multilayer perceptrons, postnonlinear (PNL) mixtures, sparse sources, spectral clustering, underdetermined source separation.

A new blind equalization approach that aims to force the probability density function (PDF) at the equalizer output to match the known constellation PDF is presented. The proposed method relies on the Parzen window method to estimate the data PDF and is implemented by a stochastic gradient descent algorithm.

The problem of blind equalization of constant modulus (CM) signals, formulated within the support vector regression (SVR) framework is discussed. Simulation examples show that linear and nonlinear blind SV equalizers offer better performance than cumulant-based techniques, mainly in applications when only a small number of data samples is available.

The suitability of a class of Parzen-window-based entropy estimates, namely Renyi's entropy, as a criterion for blind deconvolution of linear channels are investigated. The results indicate that this nonparametric entropy estimation approach outperforms the standard Bell-Sejnowski and normalized kurtosis algorithms in blind deconvolution.

The application of error-entropy minimization algorithms to digital communications channel equalization is examined. A new optimization criterion, based on the Renyi's error entropy, is used.

Based on a Gaussian mixture model for the reflectivity sequence, we present a new technique for blind deconvolution of seismic data. The method obtains a deconvolution filter that maximizes at its output a measure of the relative entropy between the proposed Gaussian mixture and a pure Gaussian distribution. A new updating procedure for the mixture parameters is included in the algorithm: it allows us to apply the algorithm without any prior knowledge about the signal and noise. A simulation example illustrates the performance of the proposed method. Index Terms - Deconvolution, estimation, iterative methods, seismology.

In this letter, we present a simple expression for the optimization of the threshold detection performance for direct-sequence spread-spectrum code acquisition in the presence of carrier-frequency offset. The proposed scheme divides the total integration time into subintervals, and the results of the coherent integrations performed over these subintervals are noncoherently combined prior to detection. The proposed expression allows obtaining the optimum number of coherent-integration subintervals for a given total integration time. Index Terms--Code acquisition, direct sequence, frequency offset, noncoherent integration, spread spectrum.

In this paper we present the smoothed piecewise-linear (SPWL) model as a useful tool in the device modeling field. The SPWL model is an extension of the well-known canonical piecewise-linear model proposed by Chua, which substitutes the abrupt absolute value function for a smoothing function (the logarithm of hyperbolic cosine), thus providing the model with several interesting properties. In particular, this function makes the model derivable, which is important to predict the intermodulation distortion behavior. Moreover, it allows one to control the smoothness of the global model by means of a single smoothing parameter. The parameters of the model are adapted to fit the nonlinear function, while the smoothing parameter is selected according to derivative constraints. The applied learning algorithm is a second-order gradient method. The proposed SPWL model is successfully applied to model a microwave HEMT transistor under optical illumination using real measurements. The model receives as input the bias voltages of the transistor, the instantaneous voltages, and the optical power and provides the drain to source current. The performance and computational burden of the SPWL model is compared with an empirical model and with some neural networks-based alternatives. Index Terms--MESFET/HEMT modeling, nonlinear modeling, piecewise-linear modeling, smoothing methods.

Digital filtering is a common approach to achieve simultaneous sampling of several input signals acquired with a multiplexing delay. In this brief an error bound is obtained for Lagrange interpolation filters as a function of the oversampling ratio of the input signals, the fractional delay, and the filter's order. This bound can be used to ensure that the error is small enough to maintain a desired resolution (number of significant bits), thus leading to design equations for simultaneous sampling systems. For example, using these equations, we are able to find that an oversampling ratio of 71 is necessary to maintain a resolution of 12 bits with a first order Lagrange's filter, while a sixth-order filter is required when the oversampling ratio is only five. The theoretical results are validated through simulation, and the computational cost of the Lagrange's interpolator is compared with a polyphase filter. Index Terms--Analog-digital conversion, data acquisition, digital filters, Lagrange interpolation, signal sampling, time delay.