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Wireless sensor networks (WSNs), i.e., networks of autonomous, wireless sensing nodes spatially deployed over a geographical area, are often faced with acquisition of spatially sparse fields. In this paper, we present a novel bandwidth/energy-efficient compressive sampling (CS) scheme for the acquisition of spatially sparse fields in a WSN. The paper contribution is twofold. Firstly, we introduce a sparse, structured CS matrix and analytically show that it allows accurate reconstruction of bidimensional spatially sparse signals, such as those occurring in several surveillance application. Secondly, we analytically evaluate the energy and bandwidth consumption of our CS scheme when it is applied to data acquisition in a WSN. Numerical results demonstrate that our CS scheme achieves significant energy and bandwidth savings with respect to state-of-the-art approaches when employed for sensing a spatially sparse field by means of a WSN

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UnderWater Acoustic (UWA) channels are narrow bandwidth and typically affected by some interference due to propagation, Doppler effect, noise and possible disturbing signals originated by different acoustic sources. Orthogonal Frequency Division Modulation (OFDM) is an effective answer to such impairments. Resource management in the framework of OFDM usually makes use of channel knowledge without taking care of its cost. This work tackles with the dualism between channel estimation and channel prediction and its impact on adaptive modulation, both from a bandwidth and power point of view also in the presence of acoustic interference. The main result is that adaptivity is worth even if requires some processing both at the transmit and receive sides. © 2013 IEEE

Cognitive access is growing in importance in radio frequency wireless applications for occupying spectrum holes leaved free by licensed and unlicensed primary services. Recently the cognitive paradigm involved underwater communications aspects since this propagation scenario is, generally, rich of acoustic sources. This justifies the curiosity of the scientific community to investigate the possibility of using some of the concept borrowed by the cognitive paradigm. Specifically, we consider an underwater active sensor network where each cognitive node applies a Wigner-Ville image processing based pattern analysis procedure not only for evaluating the presence of interference but also its nature. In fact, if the source of interference is a network node, coexistence issues should be taken into account, while, when the interference source is external (mammals, bubbles or ship engines), in principle the transmission is possible in the interfered bands, provided the presence of the interference does not affect the overall transmission quality. © 2013 IEEE

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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