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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 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.

The need for underwater reliable connections requested to provide different services as sea monitoring, devices control and camera images/videos may lead to require also large rate values. Due to the very limited bandwidth achieving few Khz, space diversity techniques can aid in this twofold mission. In this paper we investigate the use of an underwater acoustic MIMO technique, and jointly adopt the Pulse Position Modulation (PPM), in order to improve the data rates without reducing the reliability of the link. PPM is known to be power efficient modulation format while it is bandwidth inefficient so the use of MIMO can compensate that drawback with reasonable complexity. The performance has been evaluated on the basis of signal recordings for what concerns interference.

In this paper we investigate how to rephrase the concepts of cognitive access, originally developed for radio communications, in the framework of underwater acoustic communications. Differently from classical cognitive radio access, where each signal at the receiver is generated by a communication source, in the underwater scenario acoustic signals at the general receive node are likely to be due to communication source as well as natural/artificial acoustic sources (e.g mammals, ship engines and so forth). In order to maximize the access probability for cognitive acoustic nodes, we then focus on understanding the nature of the sensed interference via the tools of image classification, being the image the spectrogram of the acquired signal.

This paper addresses the problem of texture images recovery from compressively sampled measurements. Texture images hardly present a sparse, or even compressible, representation in transformed domains (e.g. wavelet) and are therefore difficult to deal with in the Compressive Sampling (CS) framework. Herein, we resort to the recently defined Graph-based transform (GBT), formerly introduced for depth map coding, as a sparsifying transform for classes of textures sharing the similar spatial patterns. Since GBT proves to be a good candidate for compact representation of some classes of texture, we leverage it for CS texture recovery. To this aim, we resort to a modified version of a state-of-the-art recovery algorithm to reconstruct the texture representation in the GBT domain. Numerical simulation results show that this approach outperforms state-of-the-art CS recovery algorithms on texture images.

The goal of this chapter is twofold. Firstly, we provide the reader with a summary of the state-of-the-art depth map interpolation techniques. Secondly, we present recent results making use of markovian priors for depth map interpolation. Specifically, we provide insight into Markovian mathematical models recently presented in the literature, and we show that such priors allow interpolation without making use of contemporaneously acquired luminance views. This feature extends the scope of the interpolation procedure and decouple its performances from those of the luminance/depth images registration stage.

The passive radar uses opportunistic illuminators, such as FM radio or TV transmitters, cellular telephone base stations, etc., to detect and track moving targets. Due to the fact that the radar itself does not transmit any power, it is undetectable. The other advantages of passive radar include its low power consumption, low cost and potential ability to detect stealth targets, due to the multistatic geometry and low frequencies used as weel as, it reduces electromagnetic pollution. This paper tackles with the problem of detecting targets close to the coastal and present on the sea surface by using opportunistic signals as The receiving operations aim at detecting the possible presence of reflective objects by stressing the diversity properties of the multiple-input multiple-output configuration due to multiple transmissions related to different communication services.

Vehicle to Infrastructure (V2I) networks can help managing different on-the-road applications and services, especially where the vehicles population is low and Internet access is needed for messaging, alerts, entertainment. Accessing a network through a proper technology requires the possibility of establishing connections with a minimum quality level; besides, a mechanism for switching from a network with poor performance to a better one by means of a so-called Vertical HandOver (VHO) may result of paramount importance. In this work we propose a switching mechanism based on sensing and processing of some physical/access/networking parameters in order to choose the technologies allowing the minimization of outages.

In compressive sensing, the Restricted Isometry Property is an analytical condition on the measurement matrix that assures reconstruction of a signal which is sparse either in the spatial or in a transformed domain given an undersampled measurements' set. In this paper, we demonstrate the RIP for a sparse, structured measurements matrix, referred to as Radon-like CS matrix. The sparse Radon-Like CS matrix favorably applies to real sensing problems, since it significantly reduces the energy/bandwidth cost of actually collecting each and every sensed values contributing to the CS measurements. Simulation results confirm the feasibility of field reconstruction from undersampled CS measurements set obtained using the Radon-Like CS matrix.

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.

This work addresses the modeling of traffic generated by a video source operating in the context of adaptive streaming services. Traffic modeling is a key in several network design issues, such as dimensioning of core and access network resources, developing pricing procedures, carrying out cost-revenue studies. The actual traffic generated during a video streaming session depends on both the video source and the bandwidth variations imposed by lower communication layers. We propose a new traffic model that jointly encompasses these two effects. Specifically, we consider the modeling of the sequence of frame sizes generated by a video streaming source that dynamically adapts its rate to the available communication channel bandwidth using bitstream switching techniques. In order to represent the source rate adaptation to the random network bandwidth variations on the communication channel, we resort to a framework based on Hidden Markov Processes (HMPs). Our HMP model represents the first joint source and sending rate model in adaptive streaming literature. Thanks to effective modeling assumptions on the frame size probability density function (pdf), the HMP parameters can be estimated by means of the Expectation Maximization algorithm. The traffic model is validated by numerical simulations of a mobile adaptive video streaming scenario. We study the model's ability to predict several traffic statistics, including the traffic load of a video streaming source in different network points. Besides, we evaluate the model accuracy in characterizing aggregate video traffic resulting from multiplexing various video sources. In all experiments, we show that the proposed model is able to accurately capture the traffic characteristics.

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.

Wireless sensor networks (WSN), 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 CS scheme for acquisition of spatially sparse fields in a WSN. The paper contribution is twofold. Firstly, we introduce a sparse, structured CS matrix and we 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 wrt state-of-the-art approaches when employed for sensing a spatially sparse field by means of a WSN., Comment: Submitted to EURASIP Journal on Advances in Signal Processing

This paper introduces a compressive sampling (CS) scheme for pulse stream images. We adopt a particular sparse sampling matrix, that we call Radon-like CS matrix. Whilst the Radon transform evaluates projections of a given image along different directions, the Radon-like CS matrix evaluates randomly weighted projections of the image along a few directions. We demonstrate that the such CS measurements are invertible and assess the reconstruction accuracy of CS with the Radonlike sampling matrix by numerical trials. The Radon-like CS performs almost as well as state of the art techniques, with a reduced number of operations. As an application example, we show that, when implemented in a resource limited framework such as a Smart Sensors Grid, the sampling matrix significantly reduces inter-node signaling and then the associated energy consumption.

Mobile video streaming services are challenging, as they obey several system constraints, such as random access facilities, efficient server storage, and flexible rate adaptation. Rate adaptation can be performed by means of seamless switching among different encoded bitstreams. The H.264 video coding standard explicitly supports bitstream switching using specific frame coding modes, namely switching pictures (SP). Locations of SP frames affect the overall bit rate and quality of streamed video. In this study, we address the issue of optimal joint selection of the SP frames locations and bit budget allocation at frame layer. The optimization is carried out via a game theoretic approach under assigned system constraints on the overall streaming rate and the maximum random access delay. Numerical simulations show that our frame layer optimal encoding procedure brings advantages in terms of several characteristics of the streamed video, encompassing enhanced rate-distortion, reduced transmission buffer occupancy, equalization of the transmission delays, and more efficient switching.

In this paper we introduce a novel, reduced dimension, Polynomial Regression based Classifier (PRC). The classical PRC expands the observed feature data set by considering higher order data statistics. The herein presented novel PRC preliminary performs projections of the data on suitable subspaces associated with the different classes. The projection operation is followed by discarding the contributions due to the higher order mixed sample moments evaluated on the data. Thereby, the overall polynomial approximation order is maintained while the dimensionality of the expanded feature space exploited by the reduced dimension classifier is drastically reduced. We assess the performance of both the full and the reduced PRC by numerical simulations on different scenarios. The reduced dimension PRC performs at least as well as the classical PRC with a significantly lower number of involved terms. This paves the way for extensively exploiting the PRC flexibility and applicability to complex classification problem although in resource limited system environments, such as, for instance, real-time applications on FPGAs.

Power Line Communications (PLC) are one of the most recent technologies proposed to allow the InHome Internet access, in competition with different Wireless Standards as Wi-Fi. The advantages offered by PLC deal with network availability since each modem connected to the plug is able, in principle, to access the Internet while in the Wi-Fi scenario, the propagation environment strongly influences the received signal strength and consequently connection performance. The conventional approach followed in planning the physical layer of PLC systems, like for example in HomePlug solution, is based on Orthogonal Frequency Division Multiplexing (OFDM), able to combat the frequency selective nature of the channel frequency response. However, OFDM presents some drawbacks as, for example, Peak to Average Power Ratio (PAPR) and Inter Carrier Interference (ICI) while a different approach based on constrained signal shaping (single carrier) can solve these problems. In this direction, this work shows a frequency shaping approach where the frequency occupancy is minimized through constraints on spectral emission and link quality level.

The problem of image restoration is considered, where the goal is to recover the original image starting from its blurred and noisy degraded version. A Bayesian restoration procedure is introduced based on modeling the image given the measurements as a Markov Random Fields characterized by spatially variant local priors. A suitable complex valued line process is introduced, generalizing previous literature works, to account for both the intensity and the orientation of image edges. The presence and the orientation of image edges are locally estimated by a computationally efficient filtering stage especially tuned to visually relevant image features, namely a first-order Circular Harmonic Function filter. Simulation results shows the effectiveness of the complex line process in describing local image discontinuities.

In this paper we introduce a novel edge directed image interpolation algorithm so as to obtain an high-resolution image, given a low-resolution image. The interpolation is based on the local image directionality features estimated on the low-resolution image. The in depth analysis of the local edge features is accomplished at a low computational cost by filtering the low-resolution image by means of the first order filter belonging to the class of the Circular Harmonic Functions (CHF). The interpolation algorithm shows low computational complexity. Numerical results show that the CHF-driven interpolation outperforms state of the art estimators from both a subjective and objective point of view, in several simulation conditions.

In this paper a blind computationally efficient Maximum Likelihood (ML) phase offset estimator for QAM constellations is introduced. The estimator is based on the computation of a weighted phase histogram named Constellation Phase Signature (CPS). The CPS is defined as the phase-dependent distribution of the received signal modulus after the application of a non-linear transformation. Here we show how the CPS can be exploited to derive a sub-optimal Maximum Likelihood (ML) estimator that approaches the CRLB for medium-high SNR. Specifically, the values of the measured CPS can be recognized as frequencies of recurrence of the received samples phases. Stemming on this observation, the CPS values can be regarded as a multinomial distributed random variable for which we are able to conduct the maximization of the likelihood function. The theoretical analysis of the estimator performance is carried out in close form. The theoretical estimator performance is compared with those of state-of-art estimators and of the Cramer-Rao lower bound (CRLB). The performance improvement is clearly appreciated, since, at medium to high SNR, the herein described estimator approaches the Cramer-Rao lower bound on all constellations.

In this paper we develop a strategy for visual relevancy evaluation using Rate Distortion analysis in the Circular Harmonic Functions (CHF) Domain. In the CHF domain, the different visual characteristics of the transformed frames - edges, lines, forks, crosses, etc - are emphasized. Resorting to this domain we analyze a new approach to evaluate the distortion due to an event of video data loss accounting for the visual relevance of individual lost data. In a real video streaming session, distortion measurements taking into account the visual impact of error events can be stored along with pre-coded video sequence so as to drive the strategies for ad-hoc error protection. Numerical simulations show that the approach herein proposed to evaluate the visual impact of loss events in the decoded sequences captures the visual distortion much better than commonly adopted mean square error criteria between the decoded and the original sequence.

Fast and bit-saving video bit-rate switching is an important issue in video streaming systems on a time varying channel as the one offered by a wireless mesh network, or the one sensed during a vertical handover. The recent H.264 video coding standard supports the seamless switching among bitstreams coded at different bit-rates by means of suitably coded frames, named switching pictures. This work addresses the modelling of the traffic generated by a H.264 source performing bit-rate switching using SP frames. The H.264 source is modelled by a Markov chain where each state models the generation of an entire group of pictures (GOP), and is characterized by the kind of SP frame encoded in the GOP. Inter- frame correlation, typical of video sources, is suitably taken into account by the interstate dependence. The accuracy of the model is assessed by comparison of the cell loss rate of a fixed size buffer filled with a synthetic source according to the model herein proposed, with a state of the art AR model and with a real H.264 video codec.

In this paper we present theoretical performance analysis for a blind frequency offset estimator for cross quadrature amplitude modulated constellations. The estimator is based on applying a tentative frequency offset compensation by means of a nonlinear transformation of the received signal samples and on estimating an accumulation function in different angular windows. For perfect frequency offset compensation, the measurements are suitably clustered and their accumulation, named "constellation phase signature" (CPS), is a peaked function of the window orientation. Hence, the frequency offset estimator is selected by maximization of the peakness of the accumulation function. The performance analysis is shown to match the numerical simulations for medium to high values of SNR.

This paper introduces a novel blind frequency offset estimator for signaling using cross Quadrature Amplitude Modulated constellations. The estimator is based on the tentative frequency offset compensation of nonlinearly transformed received signal samples and on their accumulation in different angular windows. When the compensation perfectly removes the frequency offset, the measurements are suitably clustered and their accumulation, that we call the "Constellation Phase Signature" (CPS), is a peaked function of the window orientation. Hence, the frequency offset estimator is obtained by adjusting the compensation so that the peakness of the sample CPS is maximized. The performance of the estimator has been evaluated by numerical simulations. Comparison of the CPS based estimator with a selected existing estimator shows that it performs well for medium to high values of SNR.

This paper addresses the problem of semiblind equalization for long sparse channels using a sparse equalizer. We formulate the equalization problem following a Bussgang approach, derive the coherent MMSE input synbol estimator, and assess the performances of the analyzed semi-blind Bussgang equalizer in typical sparse channels. The simulations show that the semi-blind linear equalization algorithm allows flexible integration of the training in the eqaulization procedure while the sparse, not connected, support favourably copes with channels characterized by long delayed echoes.

In this work1 we discuss a game theoretic approach to bitstream switching in video coding. Fast and bit-saving video bitstream switching is an important issue in video communication system on time varying channels. The most recent video coding standard, namely H.264, support the seamless switching among bitstreams coded at different bitrates by means of suitably coded frames, named Switching Pictures. Since the rate-distortion characteristics of switching frames differ from those of I and P frames, their location affect both the bit-rate and the quality of the coded sequence. In this work, we address the optimization of the SP frames location under an assigned bitrate budget. At this aim we restor to a game theoretic approach and we show that the optimal solution is met when the SP frames are assigned to the frame with the smallest innovation. Experimental results show the advantage in terms of both rate and distortion achieved by the optimized Switching frame insertion with respect to basic H264 coding.

This paper presents a novel procedure, named Hierarchical Compressive Sampling Matching Pursuit (CoSaMP), for reconstruction of compressively sampled sparse signals whose coefficients are organized according to a nested structure. The Hierarchical CoSaMP is inspired by the CoSaMP algorithm, and it is based on a suitable hierarchical extension of the support over which the compressively sampled signal is reconstructed. We analytically demonstrate the convergence of the Hierarchical CoSaMP and show by numerical simulations that the Hierarchical CoSaMP outperforms state-of-the-art algorithms in terms of accuracy for a given number of measurements at a restrained computational complexity.