Modulation Classification for MIMO-OFDM Signals via Approximate Bayesian Inference

The problem of modulation classification for a multiple-antenna (MIMO) system employing orthogonal frequency division multiplexing (OFDM) is investigated under the assumption of unknown frequency-selective fading channels and signal-to-noise ratio (SNR). The classification problem is formulated as a Bayesian inference task, and solutions are proposed based on Gibbs sampling and mean field variational inference. The proposed methods rely on a selection of the prior distributions that adopts a latent Dirichlet model for the modulation type and on the Bayesian network formalism. The Gibbs sampling method converges to the optimal Bayesian solution and, using numerical results, its accuracy is seen to improve for small sample sizes when switching to the mean field variational inference technique after a number of iterations. The speed of convergence is shown to improve via annealing and random restarts. While most of the literature on modulation classification assume that the channels are flat fading, that the number of receive antennas is no less than that of transmit antennas, and that a large number of observed data symbols are available, the proposed methods perform well under more general conditions. Finally, the proposed Bayesian methods are demonstrated to improve over existing non-Bayesian approaches based on independent component analysis and on prior Bayesian methods based on the `superconstellation' method.Comment: To be appear in IEEE Trans. Veh. Technolog

Self generated randomness, defect wandering and viscous flow in stripe glasses

We show that the competition between interactions on different length scales, as relevant for the formation of stripes in doped Mott insulators, can cause a glass transition in a system with no explicitly quenched disorder. We analytically determine a universal criterion for the emergence of an exponentially large number of metastable configurations that leads to a finite configurational entropy and a landscape dominated viscous flow. We demonstrate that glassines is unambiguously tied to a new length scale which characterizes the typical length over which defects and imperfections in the stripe pattern are allowed to wander over long times.Comment: 17 pages, 9 figure

Analysis of complexity and modulation spectra parameterizations to characterize voice roughness

Disordered voices are frequently assessed by speech pathologists using acoustic perceptual evaluations. This might lead to problems due to the subjective nature of the process and due to the in uence of external factors which compromise the quality of the assessment. In order to increase the reliability of the evaluations the design of new indicator parameters obtained from voice signal processing is desirable. With that in mind, this paper presents an automatic evaluation system which emulates perceptual assessments of the roughness level in human voice. Two parameterization methods are used: complexity, which has already been used successfully in previous works, and modulation spectra. For the latter, a new group of parameters has been proposed as Low Modulation Ratio (LMR), Contrast (MSW) and Homogeneity (MSH). The tested methodology also employs PCA and LDA to reduce the dimensionality of the feature space, and GMM classiffers for evaluating the ability of the proposed features on distinguishing the different roughness levels. An effciency of 82% and a Cohen's Kappa Index of 0:73 is obtained using the modulation spectra parameters, while the complexity parameters performed 73% and 0:58 respectively. The obtained results indicate the usefulness of the proposed modulation spectra features for the automatic evaluation of voice roughness which can derive in new parameters to be useful for clinicians