Society demands access to high capacity wireless communications and to the services that can be provided on top of them. To satisfy these demands, engineers are constantly developing new technologies. These developments have to be selectively transferred to the university curricula. The student has to be familiar not only with the basic theory and techniques, but also with those of the more advanced techniques that provide a more profound insight. One of these techniques is based on Multiple-Input Multiple-Output (MIMO) systems. In this paper we show how to build a web platform for learning advanced digital communications based on a MIMO testbed. Using as starting point a 4 × 4 flexible dual band (2.4/5 GHz), we first develop a webservice interface to provide remote access to the services offered by the testbed and then implement a virtual laboratory where the students can parameterize and perform advanced experiments.

In this letter we derive a simple and tight closed-form approximation for the ergodic capacity of orthogonal space-time block coding in arbitrary fading channels. The expression is an analytical function of the power covariance matrix of the channel. In the case of uncorrelated channels the expression only depends on the variances of the channel power gains. These channel statistics can be easily obtained from both analytical and physical fading channel models. Simulations results show the accuracy of the proposed expression

Cubic spline interpolation is commonly applied in signal reconstruction problems. However, overshooting between samples is normally observed, and typically the reconstructed signal does not preserve the statistical properties of the original data or other desired properties such as monotonicity or convexity. These undesirable effects are minimized in the case of piecewise linear (PWL) interpolation, of course with a discontinuous derivative. In this paper we use a parameterized family of splines, named /spl alpha/splines, that allows a smooth transition from PWL (/spl alpha/ = 0) to cubic spline interpolation (/spl alpha/ = 1). Closed-form expressions that relate /spl alpha/ to the smoothness and variance of the interpolation are derived. Moreover, a fast interpolation technique based on digital filtering can be applied.

MIMO systems based on dual-polarized antennas at transmitter and receiver constitute an interesting alternative to conventional MlMO configurations. This paper analyzes the ergodic capacity of such systems in urban micro- and pico-cellular environments. The MIMO channel is modeled by using a site-specific ray-tracing propagation tool. This technique permits to analyze the impact of environmental parameters, like antennas location and orientation, on the system performance. Ergodic capacity estimations in a specific urban environment are presented.

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.

Modem digital communications are DSP based. We present a DSP-based digital communications lab. Although based on very simple hardware, we try to show that most practical situations that we can find in the design of digital communication systems can be reproduced in our setting. The main idea in our approach is to give the same importance to discrete-time and continuous-time communication signals. Looking for student motivation, the lab is based on a problem solving approach making the students develop from simple to more complex communications systems from the very beginning. It needs very simple equipment already known to the students, so most of the time is devoted to digital communications work, and it is low cost. Personal and team work is combined in the lab, pursuing student motivation.

In this paper the problem of blind equalization of constant modulus (CM) signals is formulated within the support vector (SV) regression framework. The quadratic inequalities derived from the CM property are transformed into linear ones, thus yielding a quadratic programming (QP) problem. Then an iterative reweighted procedure is proposed to blindly restore the CM property. The technique can be generalized to nonlinear blind equalization using kernel functions. We present simulation examples showing 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, such as in packet-based transmission over fast fading channels.

We address the blind identification of single-input-multiple output (SIMO) finite impulse response systems when the input signal is sparse. The problem is equivalent to underdetermined blind source separation (BSS), but with temporal correlation among the sources. Exploiting the sparse character of the input signal, the algorithm solves three different problems: first, to estimate the directions of the columns of the channel matrix; second, to estimate the L/sub 2/-norm of the columns; and finally, to find the correct ordering of the columns of the mixing matrix. The last step is not required for the blind source separation (BSS) problem, since any permutation of the columns is admissible for BSS. The performance and computational cost of the algorithm in a noiseless situation is compared against subspace-based techniques.

Based on a Gaussian mixture model for the reflectivity sequence, the authors 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 one to apply the algorithm without any prior knowledge about the signal and noise. A simulation example illustrates the performance of the proposed method.