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Signal Processing, IEEE Transactions on

Issue 12 • Date Dec. 2010

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Displaying Results 1 - 25 of 45
  • Table of contents

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

    Page(s): C2
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  • Optimal Filter Designs for Separating and Enhancing Periodic Signals

    Page(s): 5969 - 5983
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1336 KB) |  | HTML iconHTML  

    In this paper, we consider the problem of separating and enhancing periodic signals from single-channel noisy mixtures. More specifically, the problem of designing filters for such tasks is treated. We propose a number of novel filter designs that 1) are specifically aimed at periodic signals, 2) are optimal given the observed signal and thus signal adaptive, 3) offer full parametrizations of periodic signals, and 4) reduce to well-known designs in special cases. The found filters can be used for a multitude of applications including processing of speech and audio signals. Some illustrative signal examples demonstrating its superior properties as compared to other related filters are given and the properties of the various designs are analyzed using synthetic signals in Monte Carlo simulations. View full abstract»

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  • Detection Algorithms to Discriminate Between Radar Targets and ECM Signals

    Page(s): 5984 - 5993
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (569 KB) |  | HTML iconHTML  

    We address adaptive detection of coherent signals backscattered by possible point-like targets or originated from electronic countermeasure (ECM) systems in presence of thermal noise, clutter, and possible noise-like interferers. In order to come up with a class of decision schemes capable of discriminating between targets and ECM signals, we resort to generalized likelihood ratio test (GLRT) implementations of a generalized Neyman-Pearson rule (i.e., for multiple hypotheses). The adaptive detectors rely on secondary data, free of signal components, but sharing the statistical characterization of the noise in the cell under test. The performance assessment focuses on an adaptive beamformer orthogonal rejection test (ABORT)-like detector; analytical expressions for the probability of false alarm, the probability of detection of the target, and the probability of blanking the ECM (coherent) signal are given. More remarkably, it guarantees the constant false alarm rate (CFAR) property. The performance assessment shows that it can outperform the adaptive sidelobe blanker (ASB) in presence of ECM systems. View full abstract»

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  • Quickest Detection in Multiple On–Off Processes

    Page(s): 5994 - 6006
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (668 KB) |  | HTML iconHTML  

    We consider the quickest detection of idle periods in multiple ON-OFF processes. At each time, only one process can be observed, and the observations are random realizations drawn from two different distributions depending on the current state (ON or OFF) of the chosen process. The objective is to catch an idle period in any of the ON-OFF processes as quickly as possible subject to a reliability constraint. We show that this problem presents a fresh twist to the classic problem of quickest change detection that considers only one stochastic process. A Bayesian formulation of the problem is developed for both infinite and finite number of processes based on the theory of partially observable Markov decision process (POMDP). While a general POMDP is PSPACE-hard, we show that the optimal decision rule has a simple threshold structure for the infinite case. For the finite case, basic properties of the optimal decision rule are established, and a low-complexity threshold policy is proposed which converges to the optimal decision rule for the infinite case as the number of processes increases. This problem finds applications in spectrum sensing in cognitive radio networks where a secondary user searches for idle channels in the spectrum. View full abstract»

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  • Noise Invalidation Denoising

    Page(s): 6007 - 6016
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    A denoising technique based on noise invalidation is proposed. The adaptive approach derives a noise signature from the noise order statistics and utilizes the signature to denoise the data. The novelty of this approach is in presenting a general-purpose denoising in the sense that it does not need to employ any particular assumption on the structure of the noise-free signal, such as data smoothness or sparsity of the coefficients. An advantage of the method is in denoising the corrupted data in any complete basis transformation (orthogonal or non-orthogonal). Experimental results show that the proposed method, called noise invalidation denoising (NIDe), outperforms existing denoising approaches in terms of mean square error (MSE). View full abstract»

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  • SNR Estimation Over SIMO Channels From Linearly Modulated Signals

    Page(s): 6017 - 6028
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1622 KB) |  | HTML iconHTML  

    In this paper, we address the problem of data-aided (DA) and nondata-aided (NDA) per-antenna signal-to-noise ratio (SNR) estimation over wireless single-input multiple-output (SIMO) channels from linearly modulated signals. Under constant channels and additive white Gaussian noise (AWGN), we first derive the DA maximum-likelihood (ML) SNR estimator in closed-form expression. The performance of the DA ML estimator is analytically carried out by deriving the closed-form expression of its bias and variance. Besides, in order to compare its performance with the fundamental limit, we derive the DA Cramér-Rao lower bound (CRLB) in closed-form expression. In the NDA case, the expectation-maximization (EM) algorithm is derived to iteratively maximize the log-likelihood function. The performance of the NDA ML estimator is empirically assessed using Monte Carlo simulations. Moreover, we introduce an efficient algorithm, which applies to any one/two-dimensional M-ary constellation, to numerically compute the NDA CRLBs. In this paper, the noise components are assumed to be spatially uncorrelated over all the antenna elements and temporally white. In both cases, we show that our new inphase and quadrature I/Q-based estimators offer substantial performance improvements over the single-input single-output (SISO) ML SNR estimator due to the optimal usage of the statistical dependence between the antenna branches, and that it reaches the corresponding CRLB over a wide SNR range. We also show that the use of the I/Q-based ML estimators can lead to remarkable performance improvements over the moment-based estimators for the same antenna-array size. Moreover, it is shown that SIMO configurations can contribute to decreasing the required number of iterations of the EM algorithm. View full abstract»

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  • Network Tomography: Identifiability and Fourier Domain Estimation

    Page(s): 6029 - 6039
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    The statistical problem for network tomography is to infer the distribution of X, with mutually independent components, from a measurement model Y = AX, where A is a given binary matrix representing the routing topology of a network under consideration. The challenge is that the dimension of X is much larger than that of Y and thus the problem is often ill-posed. This paper studies some statistical aspects of network tomography. We first develop a unifying theory on the identifiability of the distribution of X. We then focus on an important instance of network tomography-network delay tomography, where the problem is to infer internal link delay distributions using end-to-end delay measurements. We propose a novel mixture model for link delays and develop a fast algorithm for estimation based on the General Method of Moments. Through extensive model simulations and real Internet trace driven simulation, the proposed approach is shown to be favorable to previous methods using simple discretization for inferring link delays in a heterogeneous network. View full abstract»

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  • Efficient Maximum Entropy Reconstruction of Nuclear Magnetic Resonance T1-T2 Spectra

    Page(s): 6040 - 6051
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    This paper deals with the reconstruction of T1-T2 correlation spectra in nuclear magnetic resonance relaxometry. The ill-posed character and the large size of this inverse problem are the main difficulties to tackle. While maximum entropy is retained as an adequate regularization approach, the choice of an efficient optimization algorithm remains a challenging task. Our proposal is to apply a truncated Newton algorithm with two original features. First, a theoretically sound line search strategy suitable for the entropy function is applied to ensure the convergence of the algorithm. Second, an appropriate preconditioning structure based on a singular value decomposition of the forward model matrix is used to speed up the algorithm convergence. Furthermore, we exploit the specific structures of the observation model and the Hessian of the criterion to reduce the computation cost of the algorithm. The performances of the proposed strategy are illustrated by means of synthetic and real data processing. View full abstract»

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  • Undermodeling-Error Quantification for Quadratically Nonlinear System Identification in the Short-Time Fourier Transform Domain

    Page(s): 6052 - 6065
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (483 KB) |  | HTML iconHTML  

    In this paper, we introduce an estimation error analysis for quadratically nonlinear system identification in the short-time Fourier transform (STFT) domain. The identification scheme consists of a parallel connection of a linear component, represented by crossband filters between subbands, and a quadratic component, which is modeled by multiplicative cross-terms. We mainly concentrate on two types of undermodeling errors. The first is caused by employing a purely linear model in the estimation process (i.e., nonlinear undermodeling), and the second is a consequence of restricting the number of estimated crossband filters in the linear component. We derive analytical relations between the noise level, nonlinearity strength, and the obtainable mean-square error (mse) in subbands. We show that for low signal-to-noise ratio (SNR) conditions, a lower mse is achieved by allowing for nonlinear undermodeling and utilizing a purely linear model. However, as the SNR increases, the performance can be generally improved by incorporating a nonlinear component into the model. We further show that as the SNR increases, a larger number of crossband filters should be estimated to attain a lower mse, whether a linear or nonlinear model is employed. Experimental results support the theoretical derivations. View full abstract»

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  • A Fixed-Lag Particle Filter for the Joint Detection/Compensation of Interference Effects in GPS Navigation

    Page(s): 6066 - 6079
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    Interference are among the most penalizing error sources in global positioning system (GPS) navigation. So far, many effort has been devoted to developing GPS receivers more robust to the radio-frequency environment. Contrary to previous approaches, this paper does not aim at improving the estimation of the GPS pseudoranges between the mobile and the GPS satellites in the presence of interference. As an alternative, we propose to model interference effects as variance jumps affecting the GPS measurements which can be directly detected and compensated at the level of the navigation algorithm. Since the joint detection/estimation of the interference errors and motion parameters is a highly non linear problem, a particle filtering technique is used. An original particle filter is developed to improve the detection performance while ensuring a good accuracy of the positioning solution. View full abstract»

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  • Analysis of the Stereophonic LMS/Newton Algorithm and Impact of Signal Nonlinearity on Its Convergence Behavior

    Page(s): 6080 - 6092
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (951 KB) |  | HTML iconHTML  

    The strong cross-correlation that exists between the two input audio channels makes the problem of stereophonic acoustic echo cancellation (AEC) complex and challenging to solve. Recently, two new implementations of the LMS/Newton algorithm that uses a linear decorrelation technique were proposed. This method helps to mitigate the effect of the ill-conditioned problem on the convergence rate of the LMS/Newton adaptive algorithm. The complexity of these algorithms is significantly lower than the recursive least-squares (RLS) algorithm, which is known to provide excellent echo cancellation. Furthermore, unlike the various versions of the RLS algorithm, the LMS/Newton algorithm is more robust to numerical errors. It has also been suggested that applying nonlinearities to signals at the two audio channels will help to alleviate the misalignment problem of stereophonic AEC systems. Simulation studies reveal that application of certain classes of nonlinearities to the two-channel LMS/Newton algorithms helps to further reduce the misalignment but it also leads to an unexpected and significant reduction in the rate of convergence of the mean-square error. The contributions of this paper are twofold. First, we provide an analysis of the two-channel LMS/Newton algorithm that was proposed in our earlier work. Second, we provide a theoretical understanding for the appearance of the slow modes of convergence in the presence of nonlinearities and show that they can be resolved through a preprocessing step. View full abstract»

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  • Very Robust Low Complexity Lattice Filters

    Page(s): 6093 - 6104
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (659 KB) |  | HTML iconHTML  

    In this paper, a novel lattice filter structure is derived by combining the “tapped numerator” and “injected numerator” lattices [Y. C. Lim, “On the Synthesis of IIR Digital Filters Derived From Single Channel AR Lattice Network,” IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-32, no. 4, pp. 741-749, August 1984]. The injector coefficients are optimized for finite wordlength performance while the tapped coefficients synthesize the transfer function of the filters. For an N th-order digital filter, the proposed “tapped numerator” and “injected numerator” hybrid lattice structure, after optimization, has only 2N+1 multipliers. It is not only canonic in the number of multipliers but also possesses much improved finite wordlength properties such as very low parameter sensitivity and very uniform signal powers across signal nodes. The excellent finite wordlength performance of the proposed structure is demonstrated with a design example and compared with those of traditional structures, including several well-known lattice structures. View full abstract»

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  • Analytical Footprints: Compact Representation of Elementary Singularities in Wavelet Bases

    Page(s): 6105 - 6118
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    We introduce a family of elementary singularities that are point-Hölder α-regular. These singularities are self-similar and are the Green functions of fractional derivative operators; i.e., by suitable fractional differentiation, one retrieves a Dirac δ function at the exact location of the singularity. We propose to use fractional operator-like wavelets that act as a multiscale version of the derivative in order to characterize and localize singularities in the wavelet domain. We show that the characteristic signature when the wavelet interacts with an elementary singularity has an asymptotic closed-form expression, termed the analytical footprint. Practically, this means that the dictionary of wavelet footprints is embodied in a single analytical form. We show that the wavelet coefficients of the (nonredundant) decomposition can be fitted in a multiscale fashion to retrieve the parameters of the underlying singularity. We propose an algorithm based on stepwise parametric fitting and the feasibility of the approach to recover singular signal representations. View full abstract»

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  • Global Optimization by Adapted Diffusion

    Page(s): 6119 - 6125
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (775 KB) |  | HTML iconHTML  

    In this paper, we study a diffusion stochastic dynamics with a general diffusion coefficient. The main result is that adapting the diffusion coefficient to the Hamiltonian allows to escape local wide minima and to speed up the convergence of the dynamics to the global minima. We prove the convergence of the invariant measure of the modified dynamics to a measure concentrated on the set of global minima and show how to choose a diffusion coefficient for a certain class of Hamiltonians. View full abstract»

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  • On the Approximate Solution of a Class of Large Discrete Quadratic Programming Problems by \Delta \Sigma Modulation: The Case of Circulant Quadratic Forms

    Page(s): 6126 - 6139
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    We show that ΔΣ modulators can be interpreted as heuristic solvers for a particular class of optimization problems. Then, we exploit this theoretical result to propose a novel technique to deal with very large unconstrained discrete quadratic programming (UDQP) problems characterized by quadratic forms entailing a circulant matrix. The result is a circuit-based optimization approach involving a recast of the original problem into signal processing specifications, then tackled by the systematic design of an electronic system. This is reminiscent of analog computing, where untreatable differential equations were solved by designing electronic circuits analog to them. The approach can return high quality suboptimal solutions even when many hundreds of variables are considered and proved faster than conventional empirical optimization techniques. Detailed examples taken from two different domains illustrate that the range of manageable problems is large enough to cover practical applications. View full abstract»

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  • Compressive Sensing on Manifolds Using a Nonparametric Mixture of Factor Analyzers: Algorithm and Performance Bounds

    Page(s): 6140 - 6155
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    Nonparametric Bayesian methods are employed to constitute a mixture of low-rank Gaussians, for data x ∈ RN that are of high dimension N but are constrained to reside in a low-dimensional subregion of RN. The number of mixture components and their rank are inferred automatically from the data. The resulting algorithm can be used for learning manifolds and for reconstructing signals from manifolds, based on compressive sensing (CS) projection measurements. The statistical CS inversion is performed analytically. We derive the required number of CS random measurements needed for successful reconstruction, based on easily-computed quantities, drawing on block-sparsity properties. The proposed methodology is validated on several synthetic and real datasets. View full abstract»

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  • Algorithms for Complex ML ICA and Their Stability Analysis Using Wirtinger Calculus

    Page(s): 6156 - 6167
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (559 KB) |  | HTML iconHTML  

    We derive a class of algorithms for independent component analysis (ICA) based on maximum likelihood (ML) estimation and perform stability analysis of natural gradient ML ICA with and without the constraint for unitary demixing matrix. In the process, we demonstrate how Wirtinger calculus facilitates derivations, and most importantly, performing second-order analysis in the complex domain and eliminates the need for making simplifying assumptions. We derive natural gradient complex ML ICA update rule and its variant with a unitary constraint, as well as a Newton algorithm for better convergence behavior. The conditions for local stability are derived and studied using a generalized Gaussian density (GGD) source model. When the sources are circular and non-Gaussian, we show analytically that both the ML and ML-unitary ICA update rules converge to the inverse of mixing matrix subject to a phase shift. When the sources are noncircular and non-Gaussian, we show that the nonunitary ML ICA update rule is more stable than the ML-unitary ICA update rule. When the sources are noncircular Gaussians, both update rules are stable only when the sources have distinct noncircularity indices. Simulation results are given to support these results. View full abstract»

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  • Sidelobe Control in Collaborative Beamforming via Node Selection

    Page(s): 6168 - 6180
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (601 KB) |  | HTML iconHTML  

    Collaborative beamforming (CB) is a power efficient method for data communications in wireless sensor networks (WSNs) which aims at increasing the transmission range in the network by radiating the power from a cluster of sensor nodes in the directions of the intended base stations or access points (BSs/APs). The CB average beampattern shows a deterministic behavior and the mainlobe of the CB sample beampattern is independent of the particular node locations. However, the CB for a cluster of a finite number of collaborative nodes results in a sample beampattern with sidelobes that severely depend on the particular node locations. High level sidelobes can cause unacceptable interference when they occur at directions of unintended BSs/APs. Therefore, sidelobe control in CB has a potential to decrease the interference at unintended BSs/APs and increase the network transmission rate by enabling simultaneous multilink CB. Traditional sidelobe control techniques are proposed for centralized antenna arrays and are not suitable for WSNs. In this paper, we show that scalable and low-complexity sidelobe control techniques suitable for CB in WSNs can be developed based on a node selection technique which makes use of the randomness of the node locations. A node selection algorithm with low-rate feedback is developed to search over different node combinations. The performance of the proposed algorithm is analyzed in terms of the average number of search trials required for selecting the collaborative nodes, the resulting interference, and the corresponding transmission rate improvements. Our simulation results show that the interference can be significantly reduced and the transmission rate can be significantly increased when node selection is implemented with CB. The simulation results also show close agreement with our theoretical results. View full abstract»

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  • Iterative HOS-SOS (IHOSS) Algorithm for Direction-of-Arrival Estimation and Sensor Localization

    Page(s): 6181 - 6194
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (626 KB) |  | HTML iconHTML  

    A new method for joint direction-of-arrival (DOA) and sensor position estimation is introduced. The sensors are assumed to be randomly deployed except two reference sensors. The proposed method exploits the advantages of both higher-order-statistics (HOS) and second-order-statistics (SOS) with an iterative algorithm, namely Iterative Higher-Order Second-Order Statistics (IHOSS). A new cumulant matrix estimation technique is proposed for the HOS approach by converting the multisource problem into a single source one. IHOSS performs well even in case of correlated source signals due to the effectiveness of the proposed cumulant matrix estimate. A cost function is defined for the joint DOA and position estimation. The iterative procedure is guaranteed to converge. The ambiguity problem in sensor position estimation is solved by observing the source signals at least in two different frequencies. The conditions on these frequencies are presented. Closed-form expressions are derived for the deterministic Cramér-Rao bound (CRB) for DOA and unknown sensor positions for noncircular complex Gaussian noise with unknown covariance matrix. Simulation results show that the performance of IHOSS is significantly better than the HOS approaches for DOA estimation and closely follows the CRB for both DOA and sensor position estimations. View full abstract»

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  • Space-Time Coding for MIMO Radar Detection and Ranging

    Page(s): 6195 - 6206
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (791 KB) |  | HTML iconHTML  

    Space-time coding (STC) has been shown to play a key role in the design of MIMO radars with widely spaced antennas: In particular, rank-one coding amounts to using the multiple transmit antennas as power multiplexers, while full-rank coding maximizes the transmit diversity, compromises between the two being possible through rank-deficient coding. In detecting a target at known distance and Doppler frequency, no uniformly optimum transmit policy exists, and diversity maximization turns out to be the way to go only in a (still unspecified) large signal-to-noise ratio region. The aim of this paper is to shed some light on the optimum transmit policy as the radar is to detect a target at an unknown location: To this end, at first the Cramér-Rao bounds as a function of the STC matrix are computed, and then waveform design is stated as a constrained optimization problem, where now the constraint concerns also the accuracy in target ranging, encapsulated in the Fisher Information on the range estimate. Results indicate that such accuracy constraints may visibly modify the required transmit policy and lead to rank-deficient STC also in regions where pure detection would require pursuing full transmit diversity. View full abstract»

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  • Statistics of Co-Channel Interference in a Field of Poisson and Poisson-Poisson Clustered Interferers

    Page(s): 6207 - 6222
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    With increasing spatial reuse of radio spectrum, co-channel interference is becoming a dominant noise source and may severely degrade the communication performance of wireless transceivers. In this paper, we consider the problem of statistical-physical modeling of co-channel interference from an annular field of Poisson or Poisson-Poisson cluster distributed interferers. Poisson and Poisson-Poisson cluster processes are commonly used to model interferer distributions in large wireless networks without and with interferer clustering, respectively. Further, by considering the interferers distributed over a parametric annular region, we derive interference statistics for finite- and infinite- area interference region with and without a guard zone around the receiver. Statistical modeling of interference is a useful tool to analyze outage probabilities in wireless networks and design interference-aware transceivers. Our contributions include: 1) developing a unified framework for deriving interference models for various wireless network environments; 2) demonstrating the applicability of the symmetric alpha stable and Gaussian mixture (with Middleton Class A as a particular form) distributions in modeling co-channel interference; and 3) deriving analytical conditions on the system model parameters for which these distributions accurately model the statistical properties of the interference. Applications include co-channel interference modeling for various wireless networks, including wireless ad hoc, cellular, local area, and femtocell networks. View full abstract»

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  • Training Sequence Design for Discriminatory Channel Estimation in Wireless MIMO Systems

    Page(s): 6223 - 6237
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1150 KB) |  | HTML iconHTML  

    This paper proposes a training-based channel estimation scheme for achieving quality-of-service discrimination between legitimate and unauthorized receivers in wireless multiple-input multiple-output (MIMO) channels. The proposed method has applications ranging from user discrimination in wireless TV broadcast systems to the prevention of eavesdropping in secret communications. By considering a wireless MIMO system that consists of a multiple-antenna transmitter, a legitimate receiver (LR) and an unauthorized receiver (UR), we propose a multi-stage training-based discriminatory channel estimation (DCE) scheme that aims to optimize the channel estimation performance of the LR while limiting the channel estimation performance of the UR. The key idea is to exploit the channel estimate fed back from the LR at the beginning of each stage to enable the judicious use of artificial noise (AN) in the training signal. Specifically, with knowledge of the LR's channel, AN can be properly superimposed with the training data to degrade the UR's channel without causing strong interference on the LR. The channel estimation performance of the LR in earlier stages may not be satisfactory due to the inaccuracy of the channel estimate and constraints on the UR's estimation performance, but can improve rapidly in later stages as the quality of channel estimate improves. The training data power and AN power are optimally allocated by minimizing the normalized mean-square error (NMSE) of the LR subject to a lower limit constraint on the NMSE of the UR. The proposed DCE scheme is then extended to the case with multiple LRs and multiple URs. Simulation results are presented to demonstrate the effectiveness of the proposed DCE scheme. View full abstract»

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  • Code-Aided Maximum-Likelihood Ambiguity Resolution Through Free-Energy Minimization

    Page(s): 6238 - 6250
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    In digital communication receivers, ambiguities in terms of timing and phase need to be resolved prior to data detection. In the presence of powerful error-correcting codes, which operate in low signal-to-noise ratios (SNR), long training sequences are needed to achieve good performance. In this contribution, we develop a new class of code-aided ambiguity resolution algorithms, which require no training sequence and achieve good performance with reasonable complexity. In particular, we focus on algorithms that compute the maximum-likelihood (ML) solution (exactly or in good approximation) with a tractable complexity, using a factor-graph representation. The complexity of the proposed algorithm is discussed and reduced complexity variations, including stopping criteria and sequential implementation, are developed. View full abstract»

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  • Competitive Spectrum Management With Incomplete Information

    Page(s): 6251 - 6265
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (630 KB) |  | HTML iconHTML  

    An important issue in wireless communication is the interaction between selfish independent wireless communication systems operating in the same frequency band. Due to the selfish nature of each system, this interaction is well modeled as a strategic game where each player (system) behaves to maximize its own utility. This paper studies an interference game where each system (player) has incomplete information about the other player's channel conditions. Using partial information, players choose between frequency division multiplexing (FDM) strategy and full spread (FS) strategy where power is spread across the transmission band. An important notion in game theory is the Nash equilibrium (NE) which represents a steady point in the game; that is, each player can only lose by unilaterally deviating from it. A trivial Nash equilibrium point in this game is where players mutually choose FS and interfere with each other. This point may lead to poor spectrum utilization from a global network point of view and even for each user individually. In this paper, we provide a closed form expression for a nonpure-FS ε -Nash equilibrium point; i.e., an equilibrium point where players choose FDM for some channel realizations and FS for the others. To reach this point, the only instantaneous channel state information (CSI) required by each user is its own interference-to-signal ratio. We show that operating in this nonpure-FS ε-Nash equilibrium point increases each user's throughput and therefore improves the spectrum utilization, and demonstrate that this performance gain can be substantial. Finally, important insights are provided into the behavior of selfish and rational wireless users as a function of the channel parameters such as fading probabilities, the interference-to-signal ratio. View full abstract»

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Aims & Scope

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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Meet Our Editors

Editor-in-Chief
Zhi-Quan (Tom) Luo
University of Minnesota