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Signal Processing, IEEE Transactions on

Issue 4 • Date April 2012

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Displaying Results 1 - 25 of 55
  • Table of contents

    Publication Year: 2012 , Page(s): C1 - C4
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  • IEEE Transactions on Signal Processing publication information

    Publication Year: 2012 , Page(s): C2
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  • Robust Estimation of Noise Standard Deviation in Presence of Signals With Unknown Distributions and Occurrences

    Publication Year: 2012 , Page(s): 1545 - 1555
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (824 KB) |  | HTML iconHTML  

    In many applications, d-dimensional observations result from the random presence or absence of random signals in independent and additive white Gaussian noise. An estimate of the noise standard deviation can then be very useful to detect or to estimate these signals, especially when standard likelihood theory cannot be applied because of too little prior knowledge about the signal probability distributions. The present paper introduces a new scale estimator of the noise standard deviation when the noisy signals have unknown probability distributions and unknown probabilities of presence less than or equal to one half. The latter assumption can be regarded as to a weak assumption of sparsity. Applied to the detection of noncooperative radio-communications, this new estimator outperforms the standard MAD and its alternatives as well as the trimmed and winsorized robust scale estimators. The Matlab code corresponding to the proposed estimator is available at http://perso.telecom-bretagne.eu/pastor. View full abstract»

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  • Rooting-Based Harmonic Retrieval Using Multiple Shift-Invariances: The Complete and the Incomplete Sample Cases

    Publication Year: 2012 , Page(s): 1556 - 1570
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2924 KB) |  | HTML iconHTML  

    In the present paper, we propose a novel method for estimating one-dimensional damped and undamped harmonics. Our method utilizes the multiple shift-invariance property comprised in the signal model. We develop a new rank-reduction estimator which is formed as the weighted sum of the individual matrix polynomials obtained from individual shift-invariance equations. The uniqueness conditions for the proposed rank-reduction criteria are derived under the assumption that all samples are available. Moreover, a novel technique for the incomplete data case, where some samples are missing, is presented. In this case, the rank-reduction estimator may suffer from ambiguities. To overcome this problem, we propose an extension of the rank-reduction estimator that is based on polynomial intersection and the properties of the Sylvester matrix. The latter algorithm yields unique estimates of the damped harmonics. The proposed high-resolution techniques are search-free and therefore, they enjoy moderate computational complexity. View full abstract»

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  • Robust Nonparametric Regression via Sparsity Control With Application to Load Curve Data Cleansing

    Publication Year: 2012 , Page(s): 1571 - 1584
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2785 KB) |  | HTML iconHTML  

    Nonparametric methods are widely applicable to statistical inference problems, since they rely on a few modeling assumptions. In this context, the fresh look advocated here permeates benefits from variable selection and compressive sampling, to robustify nonparametric regression against outliers-that is, data markedly deviating from the postulated models. A variational counterpart to least-trimmed squares regression is shown closely related to an l0-(pseudo)norm-regularized estimator, that encourages sparsity in a vector explicitly modeling the outliers. This connection suggests efficient solvers based on convex relaxation, which lead naturally to a variational M-type estimator equivalent to the least-absolute shrinkage and selection operator (Lasso). Outliers are identified by judiciously tuning regularization parameters, which amounts to controlling the sparsity of the outlier vector along the whole robustification path of Lasso solutions. Reduced bias and enhanced generalization capability are attractive features of an improved estimator obtained after replacing the l0-(pseudo)norm with a nonconvex surrogate. The novel robust spline-based smoother is adopted to cleanse load curve data, a key task aiding operational decisions in the envisioned smart grid system. Computer simulations and tests on real load curve data corroborate the effectiveness of the novel sparsity-controlling robust estimators. View full abstract»

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  • Adaptive Data Fusion for Wireless Localization in Harsh Environments

    Publication Year: 2012 , Page(s): 1585 - 1596
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2125 KB) |  | HTML iconHTML  

    The dynamic and unpredictable characteristics of wireless channels in harsh environments have resulted in a poor performance of localization systems. Conventional implementations rely on unrealistic assumptions driven by tractability requirements, such as linear models or Gaussian errors. In this paper, we present a framework for data fusion in localization systems based on determining likelihood functions that represent the relationship between measurements and distances. In this framework, such likelihoods are dynamically adapted to the propagation conditions. The subsequent usage of a particle filter (PF) leads to an adaptive likelihood particle (ALPA) filter that addresses the nonlinear and non-Gaussian behavior of measurements over time. The ALPA filter's performance is quantified by using received-signal-strength (RSS) and time-of-arrival (TOA) measurements collected with wireless local area network (WLAN) devices. We compare the accuracy obtained to the accuracy of conventional implementations and to the posterior Cramér-Rao lower bound (PCRLB). Both empirical and simulation results show that the proposed ALPA filter significantly improves the accuracy of conventional approaches, obtaining an error close to the PCRLB. View full abstract»

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  • Shift & 2D Rotation Invariant Sparse Coding for Multivariate Signals

    Publication Year: 2012 , Page(s): 1597 - 1611
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2146 KB) |  | HTML iconHTML  

    Classical dictionary learning algorithms (DLA) allow unicomponent signals to be processed. Due to our interest in two-dimensional (2D) motion signals, we wanted to mix the two components to provide rotation invariance. So, multicomponent frameworks are examined here. In contrast to the well-known multichannel framework, a multivariate framework is first introduced as a tool to easily solve our problem and to preserve the data structure. Within this multivariate framework, we then present sparse coding methods: multivariate orthogonal matching pursuit (M-OMP), which provides sparse approximation for multivariate signals, and multivariate DLA (M-DLA), which empirically learns the characteristic patterns (or features) that are associated to a multivariate signals set, and combines shift-invariance and online learning. Once the multivariate dictionary is learned, any signal of this considered set can be approximated sparsely. This multivariate framework is introduced to simply present the 2D rotation invariant (2DRI) case. By studying 2D motions that are acquired in bivariate real signals, we want the decompositions to be independent of the orientation of the movement execution in the 2D space. The methods are thus specified for the 2DRI case to be robust to any rotation: 2DRI-OMP and 2DRI-DLA. Shift and rotation invariant cases induce a compact learned dictionary and provide robust decomposition. As validation, our methods are applied to 2D handwritten data to extract the elementary features of this signals set, and to provide rotation invariant decomposition. View full abstract»

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  • Robust Design of Adaptive Equalizers

    Publication Year: 2012 , Page(s): 1612 - 1626
    Cited by:  Papers (8)
    Multimedia
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (625 KB) |  | HTML iconHTML  

    Although equalizers promise to improve the signal- to-noise energy ratio, zero forcing equalizers are derived classically in a deterministic setting minimizing intersymbol interference, while minimum mean square error (MMSE) equalizer solutions are derived in a stochastic context based on quadratic Wiener cost functions. In this paper, we show that it is possible-and in our opinion even simpler-to derive the classical results in a purely deterministic setup, interpreting both equalizer types as least squares solutions. This, in turn, allows the introduction of a simple linear reference model for equalizers, which supports the exact derivation of a family of iterative and recursive algorithms with robust behavior. The framework applies equally to multiuser transmissions and multiple-input multiple-output (MIMO) channels. A major contribution is that due to the reference approach the adaptive equalizer problem can equivalently be treated as an adaptive system identification problem for which very precise statements are possible with respect to convergence, robustness and l2-stability. Robust adaptive equalizers are much more desirable as they guarantee a much stronger form of stability than conventional in the mean square sense convergence. Even some blind channel estimation schemes can now be included in the form of recursive algorithms and treated under this general framework. View full abstract»

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  • Shift-Invariant and Sampling Spaces Associated With the Fractional Fourier Transform Domain

    Publication Year: 2012 , Page(s): 1627 - 1637
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4288 KB) |  | HTML iconHTML  

    Shift-invariant spaces play an important role in sampling theory, multiresolution analysis, and many other areas of signal and image processing. A special class of the shift-invariant spaces is the class of sampling spaces in which functions are determined by their values on a discrete set of points. One of the vital tools used in the study of sampling spaces is the Zak transform. The Zak transform is also related to the Poisson summation formula and a common thread between all these notions is the Fourier transform. In this paper, we extend some of these notions to the fractional Fourier transform (FrFT) domain. First, we introduce two definitions of the discrete fractional Fourier transform and two semi-discrete fractional convolutions associated with them. We employ these definitions to derive necessary and sufficient conditions pertaining to FrFT domain, under which integer shifts of a function form an orthogonal basis or a Riesz basis for a shift-invariant space. We also introduce the fractional Zak transform and derive two different versions of the Poisson summation formula for the FrFT. These extensions are used to obtain new results concerning sampling spaces, to derive the reproducing-kernel for the spaces of fractional band-limited signals, and to obtain a new simple proof of the sampling theorem for signals in that space. Finally, we present an application of our shift-invariant signal model which is linked with the problem of fractional delay filtering. View full abstract»

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  • Dirichlet Process Mixtures for Density Estimation in Dynamic Nonlinear Modeling: Application to GPS Positioning in Urban Canyons

    Publication Year: 2012 , Page(s): 1638 - 1655
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3536 KB) |  | HTML iconHTML  

    In global positioning systems (GPS), classical localization algorithms assume, when the signal is received from the satellite in line-of-sight (LOS) environment, that the pseudorange error distribution is Gaussian. Such assumption is in some way very restrictive since a random error in the pseudorange measure with an unknown distribution form is always induced in constrained environments especially in urban canyons due to multipath/masking effects. In order to ensure high accuracy positioning, a good estimation of the observation error in these cases is required. To address this, an attractive flexible Bayesian nonparametric noise model based on Dirichlet process mixtures (DPM) is introduced. Since the considered positioning problem involves elements of non-Gaussianity and nonlinearity and besides, it should be processed on-line, the suitability of the proposed modeling scheme in a joint state/parameter estimation problem is handled by an efficient Rao-Blackwellized particle filter (RBPF). Our approach is illustrated on a data analysis task dealing with joint estimation of vehicles positions and pseudorange errors in a global navigation satellite system (GNSS)-based localization context where the GPS information may be inaccurate because of hard reception conditions. View full abstract»

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  • Hyperspectral Image Unmixing Using a Multiresolution Sticky HDP

    Publication Year: 2012 , Page(s): 1656 - 1671
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5066 KB) |  | HTML iconHTML  

    This paper is concerned with joint Bayesian endmember extraction and linear unmixing of hyperspectral images using a spatial prior on the abundance vectors. We propose a generative model for hyperspectral images in which the abundances are sampled from a Dirichlet distribution (DD) mixture model, whose parameters depend on a latent label process. The label process is then used to enforces a spatial prior which encourages adjacent pixels to have the same label. A Gibbs sampling framework is used to generate samples from the posterior distributions of the abundances and the parameters of the DD mixture model. The spatial prior that is used is a tree-structured sticky hierarchical Dirichlet process (SHDP) and, when used to determine the posterior endmember and abundance distributions, results in a new unmixing algorithm called spatially constrained unmixing (SCU). The directed Markov model facilitates the use of scale-recursive estimation algorithms, and is therefore more computationally efficient as compared to standard Markov random field (MRF) models. Furthermore, the proposed SCU algorithm estimates the number of regions in the image in an unsupervised fashion. The effectiveness of the proposed SCU algorithm is illustrated using synthetic and real data. View full abstract»

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  • Joint Blind Source Separation With Multivariate Gaussian Model: Algorithms and Performance Analysis

    Publication Year: 2012 , Page(s): 1672 - 1683
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (797 KB) |  | HTML iconHTML  

    In this paper, we consider the joint blind source separation (JBSS) problem and introduce a number of algorithms to solve the JBSS problem using the independent vector analysis (IVA) framework. Source separation of multiple datasets simultaneously is possible when the sources within each and every dataset are independent of one another and each source is dependent on at most one source within each of the other datasets. In addition to source separation, the IVA framework solves an essential problem of JBSS, namely the identification of the dependent sources across the datasets. We propose to use the multivariate Gaussian source prior to achieve JBSS of sources that are linearly dependent across datasets. Analysis within the paper yields the local stability conditions, nonidentifiability conditions, and induced Cramér-Rao lower bound on the achievable interference to source ratio for IVA with multivariate Gaussian source priors. Additionally, by exploiting a novel nonorthogonal decoupling of the IVA cost function we introduce both Newton and quasi-Newton optimization algorithms for the general IVA framework. View full abstract»

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  • Kernel Sparse Representation-Based Classifier

    Publication Year: 2012 , Page(s): 1684 - 1695
    Cited by:  Papers (17)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1309 KB) |  | HTML iconHTML  

    Sparse representation-based classifier (SRC), a combined result of machine learning and compressed sensing, shows its good classification performance on face image data. However, SRC could not well classify the data with the same direction distribution. The same direction distribution means that the sample vectors belonging to different classes distribute on the same vector direction. This paper presents a new classifier, kernel sparse representation-based classifier (KSRC), based on SRC and the kernel trick which is a usual technique in machine learning. KSRC is a nonlinear extension of SRC and can remedy the drawback of SRC. To make the data in an input space separable, we implicitly map these data into a high-dimensional kernel feature space by using some nonlinear mapping associated with a kernel function. Since this kernel feature space has a very high (or possibly infinite) dimensionality, or is unknown, we have to avoid working in this space explicitly. Fortunately, we can indeed reduce the dimensionality of the kernel feature space by exploiting kernel-based dimensionality reduction methods. In the reduced subspace, we need to find sparse combination coefficients for a test sample and assign a class label to it. Similar to SRC, KSRC is also cast into an ℓ1-minimization problem or a quadratically constrained ℓ1 -minimization problem. Extensive experimental results on UCI and face data sets show KSRC improves the performance of SRC. View full abstract»

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  • Robust Secure Transmission in MISO Channels Based on Worst-Case Optimization

    Publication Year: 2012 , Page(s): 1696 - 1707
    Cited by:  Papers (37)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (726 KB) |  | HTML iconHTML  

    This paper studies robust transmission schemes for multiple-input single-output (MISO) wiretap channels. Both the cases of direct transmission and cooperative jamming with a helper are investigated with imperfect channel state information (CSI) for the eavesdropper links. Robust transmit covariance matrices are obtained based on worst-case secrecy rate maximization, under both individual and global power constraints. For the case of an individual power constraint, we show that the nonconvex maximin optimization problem can be transformed into a quasi-convex problem that can be efficiently solved with existing methods. For a global power constraint, the joint optimization of the transmit covariance matrices and power allocation between the source and the helper is studied. We also investigate the robust wiretap transmission problem for the case with a quality-of-service constraint at the legitimate receiver. Numerical results show the advantage of the proposed robust design. In particular, for the global power constraint scenario, although cooperative jamming is not necessary for optimal transmission with perfect eavesdropper's CSI, we show that robust jamming support can increase the worst-case secrecy rate and lower the signal to interference-plus-noise ratio at the eavesdropper in the presence of channel mismatches between the transmitters and the eavesdropper. View full abstract»

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  • Broadband Underwater Localization of Multiple Sources Using Basis Pursuit De-Noising

    Publication Year: 2012 , Page(s): 1708 - 1717
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1914 KB) |  | HTML iconHTML  

    Locating multiple underwater acoustic sources is a problem that can be solved using antenna array beamforming based on the matched field (MF) processing. However, known MF beamforming techniques fail to provide good performance for multiple sources, a high noise power, and/or when the sources are close to each other. This paper proposes an MF technique for solving the localization problem. The proposed technique exploits formulation of the localization problem in terms of sparse representation of a small number of source positions among a much larger number of potential positions. The sparse representation is formulated as the basis pursuit de-noising (BPDN) problem for complex-valued variables. The solution is found as a joint solution to a set of BPDN problems corresponding to the set of source frequencies subject to the joint support. The joint BPDN problem is efficiently solved using the Homotopy approach and coordinate descent search. For further reduction in the complexity, a position grid refinement method is applied. Using simulated and real experimental data, it is shown that the technique can provide accurate source localization for multiple sources. The proposed technique outperforms other MF techniques in resolving sources positioned closely to each other, tolerance to the noise and capability of locating multiple sources. View full abstract»

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  • Adaptive Compressed Sensing Radar Oriented Toward Cognitive Detection in Dynamic Sparse Target Scene

    Publication Year: 2012 , Page(s): 1718 - 1729
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2400 KB) |  | HTML iconHTML  

    Recently, the idea of compressed sensing (CS) has been used in radar system, and the concept of compressed sensing radar (CSR) has been proposed in which the target scene can be sparsely represented in the range-Doppler plane. With sufficiently incoherent transmission waveform, the target scene can be reconstructed by the technique of CS. With the idea that the transmission waveform can adapt in response to the operational information in cognitive radar system, we propose the notion of adaptive compressed sensing radar (ACSR) whose transmission waveform and sensing matrix can be updated by the target scene information fed back by the recovery algorithm. The methods for optimizing the transmission waveform and sensing matrix separately and simultaneously are both presented to decrease the cross correlations between different target responses. The principle for an ACSR system to synthesize the transmission waveform and sensing matrix matched to the target scene is also investigated. This novel ACSR system offers more degrees of freedom than classical radar system and better recovery performance than the CSR system. View full abstract»

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  • Optimization of the Receive Filter and Transmit Sequence for Active Sensing

    Publication Year: 2012 , Page(s): 1730 - 1740
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2213 KB) |  | HTML iconHTML  

    This paper discusses the joint design of receive filters and transmit signals for active sensing applications such as radar and active sonar. The goal is to minimize the mean-square error (MSE) of target's scattering coefficient estimate in the presence of clutter and interference, which is equivalent to maximizing the signal-to-clutter-plus-interference ratio. A discrete-time signal model is assumed and practical constant-modulus or low peak-to-average-power ratio (PAR) constraints are imposed on the transmit signal. Several optimization methods are proposed for this joint design. Furthermore, the MSE criterion is expressed in the frequency domain and a corresponding MSE lower bound is derived. Numerical examples for different types of interferences are included to demonstrate the effectiveness of the proposed designs. View full abstract»

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  • The Multiple Model CPHD Tracker

    Publication Year: 2012 , Page(s): 1741 - 1751
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2229 KB) |  | HTML iconHTML  

    The probability hypothesis density (PHD) is a practical approximation to the full Bayesian multi-target filter. The cardinalized PHD (CPHD) filter was proposed to deal with the “target death” problem of the PHD filter. A multiple-model PHD exists; in this work, a multiple model version of the considerably more complex CPHD filter is derived. It is implemented using Gaussian mixtures, and a track management (for display and scoring) strategy is developed. View full abstract»

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  • Robust Rate-Adaptive Wireless Communication Using ACK/NAK-Feedback

    Publication Year: 2012 , Page(s): 1752 - 1765
    Cited by:  Papers (3)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3107 KB) |  | HTML iconHTML  

    To combat the detrimental effects of the variability in wireless channels, we consider cross-layer rate adaptation based on limited feedback. In particular, based on limited feedback in the form of link-layer acknowledgements (ACK) and negative acknowledgements (NAK), we maximize the physical-layer transmission rate subject to an upper bound on the expected packet error rate. We take a robust approach in that we do not assume any particular prior distribution on the channel state. We first analyze the fundamental limitations of such systems and derive an upper bound on the achievable rate for signaling schemes based on uncoded QAM and random Gaussian ensembles. We show that, for channel estimation based on binary ACK/NAK feedback, it may be preferable to use a separate training sequence at high error rates, rather than to exploit low-error-rate data packets themselves. We also develop an adaptive recursive estimator, which is provably asymptotically optimal and asymptotically efficient. View full abstract»

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  • Opportunistic Distributed Space-Time Coding for Decode-and-Forward Cooperation Systems

    Publication Year: 2012 , Page(s): 1766 - 1781
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3850 KB) |  | HTML iconHTML  

    In this paper, we consider a decode-and-forward (DF) cooperation system consisting of two cooperative users in sending their information to a common destination, for which the distributed space-time coding (DSTC) is applied in an opportunistic manner, called opportunistic DSTC (O-DSTC), depending on whether the two users succeed in decoding each other's information or not. We propose two O-DSTC schemes for the full-duplex and half-duplex relaying scenarios, which are, respectively, referred to as the full-duplex and half-duplex-based O-DSTC. We evaluate the outage performance of the proposed O-DSTC as well as the conventional selective DF (S-DF) cooperation and fixed DSTC (F-DSTC) schemes. Numerical results show that the O-DSTC outperforms the conventional S-DF and F-DSTC schemes considering both full-duplex and half-duplex. In addition, we develop the diversity-multiplexing tradeoff (DMT) of the proposed O-DSTC, conventional S-DF and F-DSTC schemes by considering the two cooperative users with different data rates (also known as different multiplexing gains). We show that, for both the full-duplex and half-duplex modes, the proposed O-DSTC strictly outperforms the conventional S-DF and F-DSTC in terms of DMT. It is also shown that, in the full-duplex-based O-DSTC scheme, the diversity gain obtained by any of the two cooperative users not only depends on its own multiplexing gain, but also relates to its partner's multiplexing gain. By jointly considering the two users' DMT, the full-duplex-based O-DSTC scheme can achieve the optimal diversity gain when the two users are with the same multiplexing gain. For the half-duplex-based O-DSTC scheme, the DMT performance of the two users are independent of each other, which differs from the full-duplex-based O-DSTC scheme where mutual dependence exists between the cooperative users in terms of DMT. View full abstract»

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  • Parameterized Cancellation of Partial-Band Partial-Block-Duration Interference for Underwater Acoustic OFDM

    Publication Year: 2012 , Page(s): 1782 - 1795
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2786 KB) |  | HTML iconHTML  

    Despite that underwater acoustic channels are well known to contain various interferences, research on interference mitigation in underwater acoustic communications has been very limited. In this paper, we deal with a wideband orthogonal frequency division multiplexing (OFDM) transmission in the presence of an external interference which occupies partially the signal band and whose time duration is shorter than the OFDM block. We parameterize the unknown interference waveform by a number of parameters assuming prior knowledge of the frequency band and time duration of the interference, and develop an iterative receiver, which couples interference detection via a generalized likelihood-ratio-test (GLRT), interference reconstruction and cancellation, channel estimation, and data detection. In addition to simulation results, we verify the receiver performance using data sets collected from two experiments. In both time-invariant and time-varying channels, the proposed iterative receiver achieves robust performance in the presence of unknown interference. View full abstract»

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  • Active Cooperation Between Primary Users and Cognitive Radio Users in Heterogeneous Ad-Hoc Networks

    Publication Year: 2012 , Page(s): 1796 - 1805
    Cited by:  Papers (17)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2265 KB) |  | HTML iconHTML  

    In this paper, we consider a heterogeneous ad-hoc network where primary users may cooperate with cognitive radio (CR) users for the transmission of their data. We propose a new cooperation protocol that allows CR users to relay primary user signals in exchange for some spectrum. The spectrum released by primary users is used by CR users for their own data transmission. The proposed protocol maximizes the primary user power savings and the CR users' own data transmission rate. In addition, it provides more robust (potentially continuous) service for CR users, compared to the conventional practice in cognitive networks where cognitive users transmit in the spectrum holes of primary users (i.e., their service is interrupted when primary users need to transmit and no spectrum holes are available). More specifically, we propose a CR user power allocation scheme that maximizes the rate of transmission of CR user own data, for any given CR user power budget and a given bandwidth released from the primary user. Furthermore, we determine a range of possible transmission power levels that can be used by the primary user during cooperation without sacrificing its target transmission rate, and we derive a necessary condition on the quality of the channel between the primary user and the CR user that enables cooperation. Extensive numerical and simulation studies illustrate our theoretical developments and show that cooperation between a primary and CR user may lead, for example, to up to 80% savings of primary user power when compared to a noncooperation scheme at the same transmission power level. View full abstract»

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  • Detecting and Counteracting Statistical Attacks in Cooperative Spectrum Sensing

    Publication Year: 2012 , Page(s): 1806 - 1822
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4211 KB) |  | HTML iconHTML  

    In this paper we propose a novel Bayesian method to improve the robustness of cooperative spectrum sensing against misbehaving secondary users, which may send wrong sensing reports in order to artificially increase or reduce the throughput of a cognitive network. We adopt a statistical attack model in which every malicious node is characterized by a certain probability of attack. The key features of the proposed method are: (i) combined spectrum sensing, identification of malicious users, and estimation of their attack probabilities; (ii) use of belief propagation on factor graphs to efficiently solve the Bayesian estimation problem. Our analysis shows that the proposed joint estimation approach outperforms traditional cooperation schemes based on exclusion of the unreliable nodes from the spectrum sensing process, and that it nearly achieves the performance of an ideal maximum likelihood estimation if attack probabilities remain constant over a sufficient number of sensing time slots. Results illustrate that belief propagation applied to the considered problem is robust with respect to different network parameters (e.g., numbers of reliable and malicious nodes, attack probability values, sensing duration). Finally, spectrum sensing estimates obtained via belief propagation are proved to be consistent on average for arbitrary graph size. View full abstract»

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  • On Multiple Antenna Spectrum Sensing Under Noise Variance Uncertainty and Flat Fading

    Publication Year: 2012 , Page(s): 1823 - 1832
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2630 KB) |  | HTML iconHTML  

    We investigate a spectrum sensing method based on asymptotic analysis of the discrete Fourier transform of the received multiantenna signal, possibly non-Gaussian, for flat-fading primary user signals in white noise under noise variance uncertainty. The proposed approach is based on the generalized likelihood ratio test (GLRT) paradigm for a restricted version of the problem obtained by ignoring the spatial structure of the primary users' received signals, and it permits the noise variances to be different at different antennas without requiring knowledge of their values. Simulation examples show the efficacy of the proposed approach compared with the energy detector and some existing time-domain GLRT approaches. A performance analysis of the proposed detector is carried out and verified via simulations. It is also shown that the proposed test statistic is equivalent to an existing time-domain GLRT statistic except that the latter has been derived under the assumption that received signal is Gaussian whereas we make no such assumption. View full abstract»

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  • Sensing and Probing Cardinalities for Active Cognitive Radios

    Publication Year: 2012 , Page(s): 1833 - 1848
    Cited by:  Papers (5)
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    In a cognitive radio network, opportunistic spectrum access (OSA) to the underutilized spectrum involves not only sensing the spectrum occupancy but also probing the channel quality in order to identify an idle and good channel for data transmission-particularly if a large number of channels is open for secondary spectrum reuse. Although such a joint mechanism, referred to as active sensing, may improve the OSA performance due to diversity, it inevitably incurs additional energy consumption. In this paper, we consider a wideband cognitive radio network with limited available frame energy and treat a fundamental energy allocation problem: how available energy should be optimally allocated for sensing, probing, and data transmission to maximize the achievable average OSA throughput. By casting this problem into the multiarmed bandit framework under probably approximately correct (PAC) learning, we put forth a proactive strategy for determining the optimal sensing cardinality (the number of channels chosen to sense) and probing cardinality (the number of channels chosen to probe) that maximize the average throughput of the secondary user with limited available frame energy. This framework determines the optimal amount of pure exploration for the active sensing OSA bandit problem in which we refine the action (median) elimination algorithm for channel probing to minimize the sample complexity in PAC learning. Numerical results show that the optimal active sensing achieves a significant throughput gain over the (even optimal) sensing alone. Therefore, this work provides an energy allocation policy to optimally balance the available energy between exploration (sensing and probing) and exploitation (data transmission), giving the optimal diversity-energy tradeoff for the average OSA throughput. View full abstract»

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IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals

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Sergios Theodoridis
University of Athens