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Cognitive radio (CR) is currently one of the most promising solutions for designing spectrum-efficient wireless com- munication systems. In this paper, we present a greedy cognitive access policy that handles spectrum sensing with the tools of image processing. By means of the Wigner-Ville transform (WVT), we are able to analyze the time and frequency features of the received interference, and represent them on a time-frequency grid as an image in which brightness reveals the spectrum occupancy. Ac- cording to sensed interference, each secondary user autonomously tries to access the grid following a multiple-division principle, i.e., by properly shaping the signal in the code, time, and frequency domains. In detail, this is realized by processing the WVT to derive the shaping mask (and the matching algorithm) that allows the filling up of the "time spectrum holes." Numerical simulations and a test-bed implementation show how the proposed scheme outper- forms selected state-of-the-art approaches while still presenting an affordable computational cost. Index Terms—Cognitive radio (CR), edge detection (ED), multiple access, Wigner-Ville transform (WVT). I. INTRODUCTION developed here fully embraces the cognitive paradigm and, following (3), is characterized by awareness (through interfer- ence shape detection), the ability to make decisions (shaping is based on the learning/acquisition phases), adaptivity (to con- dition evolutions), reliability (i.e., robustness to impairments), and efficiency (from smart energy consumption). A possible application of this kind of device ranges from medical body area networks (MBANs) or smart grids (not power-lined) to public safety networks. Establishing a connection easily is the common denominator of these networks. In fact, smart grids allow the connection of new devices in an unlicensed band (e.g., electrical vehicles needing to be charged). In the area of public safety, CR was identified in 2008 by both a National Emergency Communications Plan report and the National Broadband Plan (4) as one of the emerging technologies for increasing effi- ciency and effectiveness of spectrum usage. Finally, MBANs need devices able to work in some spectrum bands subject to constraints related to forbidden bands, as specified by different hospitals. In this regard, flexibility and self-configuring ability are two relevant issues. The proposed approach answers those requests.

In this paper we present a Markov Random Field (MRF) based image interpolation procedure suited to both noise-free and noisy measurements. Specifically, after introducing a MRF characterized by means of a novel complex line process representing the visually relevant image features, we derive the global Maximum A Posteriori (MAP) interpolator under the hypothesis of spatially variant additive Gaussian noise. Besides, we derive a closed form local Bayesian MAP interpolator, on the base of which we develop a suboptimal, computationally efficient, single pass interpolation procedure. Numerical simulations demonstrate that the interpolation procedure outperforms state-of-the-art techniques, from both a subjective and objective point of view, in the case of noise-free and noisy measurements.

This letter addresses the problem of X-ray pulsar radiation phase estimation, encountered in research works concerning autonomous deep space navigation systems. Autonomous navigation systems represent an intriguing solution to be employed when Earth-assisted navigation is not viable for long range missions. In such applications, X-ray pulsars, as well as other celestial objects, may be employed as peculiar beacons to allow the spacecrafts to adjust their own route. State of the art techniques for estimation of X-ray pulsar radiation phase involve maximization of generally non-convex objective functions, thus resulting in computationally onerous procedures. Here, we show how the problem of pulsar phase estimation can be recast as a cyclic shift parameter estimation problem under multinomial distributed observations, whose maximum likelihood solution can be implemented by means of a fast, Discrete Fourier Transform based procedure. Numerical results show how the herein described fast, near maximum likelihood, estimator favorably compares with selected state of the art estimators, while presenting a significantly reduced computational complexity.

In this paper, we address the problem of location parameter estimation via a Generalized Method of Moments (GMM) approach. The general framework for the GMM estimation requires the minimization of a suitable, generally nonconvex, elliptic norm. Here we show that, if the estimandum is a shift parameter for a suitable statistic of the observations, a fast, DFT-based, computationally efficient procedure can be employed to perform the estimation. Besides we discuss the relation between the GMM estimation and the maximum likelihood (ML) estimation, showing that the GMM estimation rule provides a closed form ML estimator for shift parameters when the observations are multinomially distributed. As a case study, we analyze a GMM blind phase offset estimator for general quadrature amplitude modulation constellations. Simulation results and theoretical performance analysis show that the GMM estimator outperforms selected state of the art estimators, approaching the Cramer-Rao lower bound for a wide range of signal-to-noise ratio values.

This paper introduces a novel, not data aided, phase-offset estimator for quadrature amplitude modulated (QAM) signals. Contrarily to near-efficient existing phase acquisition techniques, this estimator does not require a preliminary gain adjustment stage while its accuracy preserves the slope of Cramer-Rao bound for medium-high signal-to-noise ratio (SNR) ranges, where it typically outperforms existing blind estimators, with significant improvement for dense and cross QAM constellations. Moreover, it needs only a very rough estimate of the SNR. Like other gain-control-free blind phase-offset estimators, it measures the amount of the cyclic shift by which the (four-folded) phase probability density function (pdf) is rotated under an unknown phase-offset. Estimation of the phase-offset-induced cyclic shift is conducted first by measuring the received data phase pdf by a canonical phase histogram procedure, then by estimating the phase-offset-induced cyclic shift through a cyclic cross correlation-based procedure between the measured phase histogram and a reference phase pdf evaluated within the zero phase-offset hypothesis. Actually, the estimation procedure is presented in a generalized version that considers a tomographic projection of the bidimensional (magnitude/phase) pdf of suitable nonlinear transformations of the received data. The tomographic projection performs a magnitude weighing on the pdf, and this, in turn, results in an improved overall estimation accuracy, as shown by theoretical analysis and numerical simulations here performed to assess the estimator performance.