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Signal Processing, IEEE Transactions on

Issue 11 • Date Nov. 2010

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Displaying Results 1 - 25 of 51
  • 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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    Freely Available from IEEE
  • Learning Graphical Models for Hypothesis Testing and Classification

    Page(s): 5481 - 5495
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1368 KB) |  | HTML iconHTML  

    Sparse graphical models have proven to be a flexible class of multivariate probability models for approximating high-dimensional distributions. In this paper, we propose techniques to exploit this modeling ability for binary classification by discriminatively learning such models from labeled training data, i.e., using both positive and negative samples to optimize for the structures of the two models. We motivate why it is difficult to adapt existing generative methods, and propose an alternative method consisting of two parts. First, we develop a novel method to learn tree-structured graphical models which optimizes an approximation of the log-likelihood ratio. We also formulate a joint objective to learn a nested sequence of optimal forests-structured models. Second, we construct a classifier by using ideas from boosting to learn a set of discriminative trees. The final classifier can interpreted as a likelihood ratio test between two models with a larger set of pairwise features. We use cross-validation to determine the optimal number of edges in the final model. The algorithm presented in this paper also provides a method to identify a subset of the edges that are most salient for discrimination. Experiments show that the proposed procedure outperforms generative methods such as Tree Augmented Naïve Bayes and Chow-Liu as well as their boosted counterparts. View full abstract»

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  • Data-Aided SNR Estimation in Time-Variant Rayleigh Fading Channels

    Page(s): 5496 - 5507
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (547 KB) |  | HTML iconHTML  

    This paper addresses the data-aided (DA) signal-to-noise ratio (SNR) estimation for constant modulus modulations over time-variant flat Rayleigh fading channels. The time-variant fading channel is modeled by considering the Jakes' model and the first order autoregressive (AR1) model. Closed-form expressions of the Cramér-Rao bound (CRB) for DA SNR estimation are derived for known and unknown fast fading Rayleigh channels parameters cases. As special cases, the CRBs over slow and uncorrelated fading Rayleigh channels are derived. Analytical approximate expressions for the CRBs are derived for low and high SNR. These expressions that enable the derivation of a number of properties that describe the bound's dependence on key parameters such as SNR, channel correlation and sample number. Since the exact maximum likelihood (ML) estimator is computationally intensive in the case of fast-fading channels, two approximate ML estimator solutions are proposed for high and low SNR cases in the case of known channel parameters. The performances of theses estimators are examined analytically in terms of means and variances. In the presence of unknown channel parameters, a high SNR ML estimator based on the AR1 correlation model is derived. It is shown that the ML estimates of the SNR parameter and unknown channel parameters may be obtained in a separable form. Finally, simulation results illustrate the performance of the estimator and confirm the validity of the theoretical analysis. View full abstract»

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  • Enhanced Illumination Sensing Using Multiple Harmonics for LED Lighting Systems

    Page(s): 5508 - 5522
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (729 KB) |  | HTML iconHTML  

    This paper considers frequency division multiplexing (FDM) based illumination sensing in light emitting diode (LED) lighting systems. The purpose of illumination sensing is to identify the illumination contributions of spatially distributed LEDs at a sensor location, within a limited response time. In the FDM scheme, LEDs render periodical illumination pulse trains at different frequencies with prescribed duty cycles. The problem of interest is to estimate the amplitudes of the individual illumination pulse trains. In our previous work, an estimation approach was proposed using the fundamental frequency component of the sensor signal. The number of LEDs that can be supported by this estimation approach is limited to around 100 LEDs at a response time of 0.1 s. For future LED lighting systems, however, it is desirable to support many more LEDs. To this end, in this paper, we seek to exploit multiple harmonics in the sensor signal. We first derive upper limits on the number of LEDs that can be supported in the presence of frequency offsets and noise. Thereafter, we propose a low complexity successive estimation approach that effectively exploits the multiple harmonics. It is shown that the number of the LEDs can be increased by a factor of at least five, compared to the estimation approach using only the fundamental frequency component, at the same estimation error. View full abstract»

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  • Bearings-Only Target Motion Analysis via Instrumental Variable Estimation

    Page(s): 5523 - 5533
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (418 KB) |  | HTML iconHTML  

    This paper deals with the instrumental variable (IV) estimation for the problem of target motion analysis from bearing-only measurements (BO-TMA). By taking asymptotical analysis of the IV estimation, a systematic method for developing consistent IV estimate and the sufficient condition for its asymptotical normality are proposed. The asymptotical covariance of IV estimate is also derived explicitly which can be used to evaluate its performance. These results generalize the previous studies and enhance the application of the IV estimation in target tracking. Numerical examples are shown to verify the theoretical results. View full abstract»

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  • Barankin-Type Lower Bound on Multiple Change-Point Estimation

    Page(s): 5534 - 5549
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (957 KB) |  | HTML iconHTML  

    We compute lower bounds on the mean-square error of multiple change-point estimation. In this context, the parameters are discrete and the Cramér-Rao bound is not applicable. Consequently, we focus on computing the Barankin bound (BB), the greatest lower bound on the covariance of any unbiased estimator, which is still valid for discrete parameters. In particular, we compute the multi-parameter version of the Hammersley- Chapman-Robbins, which is a Barankin-type lower bound. We first give the structure of the so-called Barankin information matrix (BIM) and derive a simplified form of the BB. We show that the particular case of two change points is fundamental to finding the inverse of this matrix. Several closed-form expressions of the elements of BIM are given for changes in the parameters of Gaussian and Poisson distributions. The computation of the BB requires finding the supremum of a finite set of positive definite matrices with respect to the Loewner partial ordering. Although each matrix in this set of candidates is a lower bound on the covariance matrix of the estimator, the existence of a unique supremum w.r.t. to this set, i.e., the tightest bound, might not be guaranteed. To overcome this problem, we compute a suitable minimal-upper bound to this set given by the matrix associated with the Loewner-John Ellipsoid of the set of hyper-ellipsoids associated to the set of candidate lower-bound matrices. Finally, we present some numerical examples to compare the proposed approximated BB with the performance achieved by the maximum likelihood estimator. View full abstract»

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  • On Construction and Simulation of Autoregressive Sources With Near-Laplace Marginals

    Page(s): 5550 - 5559
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (774 KB) |  | HTML iconHTML  

    In this paper, we focus upon the problem of modeling and simulation of stationary non-Gaussian time series. In particular, we consider a first order autoregressive process whose marginal distribution is close to the Laplace density. This model allows us to simulate correlated non-Gaussian signals typically appearing in speech analysis, compression, and noise synthesis. The Monte Carlo rejection method is applied to develop efficient algorithms for simulation of the proposed autoregressive process. We also extend our theory and algorithms to the related issue of constructing a correlated bivariate time-series model with near-Laplace margins. A theoretical analysis of the average complexity of the proposed simulation algorithms is included. View full abstract»

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  • A Hierarchical Bayesian Model for Frame Representation

    Page(s): 5560 - 5571
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2493 KB) |  | HTML iconHTML  

    In many signal processing problems, it is fruitful to represent the signal under study in a frame. If a probabilistic approach is adopted, it becomes then necessary to estimate the hyperparameters characterizing the probability distribution of the frame coefficients. This problem is difficult since in general the frame synthesis operator is not bijective. Consequently, the frame coefficients are not directly observable. This paper introduces a hierarchical Bayesian model for frame representation. The posterior distribution of the frame coefficients and model hyperparameters is derived. Hybrid Markov chain Monte Carlo algorithms are subsequently proposed to sample from this posterior distribution. The generated samples are then exploited to estimate the hyperparameters and the frame coefficients of the target signal. Validation experiments show that the proposed algorithms provide an accurate estimation of the frame coefficients and hyperparameters. Application to practical problems of image denoising in the presence of uniform noise illustrates the impact of the resulting Bayesian estimation on the recovered signal quality. View full abstract»

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  • Adaptive Target Detection With Application to Through-the-Wall Radar Imaging

    Page(s): 5572 - 5583
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1431 KB) |  | HTML iconHTML  

    An adaptive detection scheme is proposed for radar imaging. The proposed detector is a postprocessing scheme derived for one-, two-, and three-dimensional data, and applied to through-the-wall imaging using synthetic aperture radar. The target image statistics depend on the target three-dimensional orientation and position. The statistics can also vary with the standoff distance of the imaging system because of the change in the corresponding scene image resolution. We propose an iterative target detection scheme for the cases in which no or partial a priori knowledge of the target image statistics is available. Properties of the proposed scheme, such as conditions of convergence and optimal configurations are introduced. The detector performance is examined under synthetic and real data. The latter is obtained using a synthetic aperture through-the-wall radar indoor imaging scanner implementing wideband delay and sum beamforming. View full abstract»

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  • Effortless Critical Representation of Laplacian Pyramid

    Page(s): 5584 - 5596
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (325 KB) |  | HTML iconHTML  

    The Laplacian pyramid (LP) is a multiresolution representation introduced originally for images, and it has been used in many applications. A major shortcoming of the LP representation is that it is oversampled. The dependency among the LP coefficients is studied in this paper. It is shown that whenever the LP compression filter is interpolatory, the redundancy in the LP coefficients can be removed effortlessly by merely discarding some of the LP coefficients. Furthermore, it turns out that the remaining, now critically sampled, LP coefficients are actually the coefficients of a wavelet filter bank. As a result, a new algorithm for designing a nonredundant wavelet filter bank from non-biorthogonal lowpass filters is obtained. Our methodology presented in this paper does not depend on the spatial dimension of the data or the dilation matrix for sampling. View full abstract»

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  • Two-Dimensional 2\times Oversampled DFT Modulated Filter Banks and Critically Sampled Modified DFT Modulated Filter Banks

    Page(s): 5597 - 5611
    Multimedia
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    This paper investigates two-dimensional (2D) 2 oversampled DFT modulated filter banks and 2D critically sampled modified DFT (MDFT) modulated filter banks as well as their design. The structure and perfect reconstruction (PR) condition of 2D 2× oversampled DFT modulated filter banks are presented in terms of the polyphase decompositions of prototype filters (PFs). In the double-prototype case, the part solutions of the PR condition are parameterized by imposing the 2D two-channel lifting structure on each pair of the polyphase components of analysis and synthesis PFs. Based on the parametric structure, the analysis and synthesis PFs are separately designed by constrained quadratic programs. The obtained filter banks are of structurally PR. Moreover, 2D critically sampled MDFT modulated filter banks are proposed. It is proved that 2D critically sampled PR MDFT modulated filter banks can be rebuilt from 2D 2 oversampled PR DFT modulated filter banks when the decimation matrices satisfy a permissible condition and the analysis and synthesis PFs are identical and symmetric with respect to the origin. A numerical algorithm is given to design 2D critically sampled PR MDFT modulated filter banks and the obtained filter banks are of numerically PR. View full abstract»

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  • A Spectral Approach for Sifting Process in Empirical Mode Decomposition

    Page(s): 5612 - 5623
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1340 KB) |  | HTML iconHTML  

    In this paper, we propose an alternative to the algorithmic definition of the sifting process used in the original Huang's empirical mode decomposition (EMD) method. Although it has been proven to be particularly effective in many applications, EMD method has several drawbacks. The major problem with EMD is the lack of theoretical Framework which leads to difficulties for the characterization and evaluation this approach. On top of the mathematical model, there are other concerns with mode mixing and transient phenomena, such as intermittency or pure tones separation. This paper follows a previous published nonlinear diffusion-based filtering to solve the mean-envelope estimation in sifting process. The major improvements made in this present work are a non-iterative resolution scheme for the previously proposed partial differential equation (PDE), a new definition of the stopping function used in the PDE framework, and finally an automatic regularization process based on inverse problem theory to deal with mode mixing or transient detection problem. Obtained results confirm good properties of the new version of the PDE-based sifting process and its usefulness for decomposition of various kinds of data. The efficiency of the method is illustrated on some examples using informative and pathological signals for which standard EMD algorithm fails. View full abstract»

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  • Regularized Sampling of Multiband Signals

    Page(s): 5624 - 5638
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (581 KB) |  | HTML iconHTML  

    This paper presents a regularized sampling method for multiband signals, that makes it possible to approach the Landau limit, while keeping the sensitivity to noise and perturbations at a low level. The method is based on band-limited windowing, followed by trigonometric approximation in consecutive time intervals. The key point is that the trigonometric approximation “inherits” the multiband property, that is, its coefficients are formed by bursts of elements corresponding to the multiband components. It is shown that this method can be well combined with the recently proposed synchronous multirate sampling (SMRS) scheme, given that the resulting linear system is sparse and formed by ones and zeroes. The proposed method allows one to trade sampling efficiency for noise sensitivity, and is specially well suited for bounded signals with unbounded energy like those in communications, navigation, audio systems, etc. Besides, it is also applicable to finite energy signals and periodic band-limited signals (trigonometric polynomials). The paper includes a subspace method for blindly estimating the support of the multiband signal as well as its components, and the results are validated through several numerical examples. View full abstract»

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  • No-Go Theorem for Linear Systems on Bounded Bandlimited Signals

    Page(s): 5639 - 5654
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (632 KB) |  | HTML iconHTML  

    In this paper we analyze the existence of efficient bandpass-type systems for the space of bounded bandlimited signals. Here efficient means that the system fulfills the following properties: every output signal contains only frequencies within the passband; every input signal that has only frequencies within the passband is not disturbed by the system; and the system is stable. Without using any further assumptions, such as time-invariance, we prove that a linear realization cannot exist. Moreover, we show that a nonlinear realization is possible. It is well-known that every signal with finite energy can be split into two signals with finite energy, each of which contains a different part of the spectrum. Surprisingly, this does not hold for the space of bounded bandlimited signals. It is shown that there exist bounded bandlimited signals that cannot be split in the above way. These results can be of relevance for all applications where filters are used and the peak value of the signals is decisive, e.g., the design of efficient power amplifiers in wireless communication systems. The no-go results in this paper are helpful to better understand the signal space of bounded bandlimited signals and the limits of signal processing operations on this space. View full abstract»

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  • A Closed-Form Robust Chinese Remainder Theorem and Its Performance Analysis

    Page(s): 5655 - 5666
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (669 KB) |  | HTML iconHTML  

    Chinese remainder theorem (CRT) reconstructs an integer from its multiple remainders that is well-known not robust in the sense that a small error in a remainder may cause a large error in the reconstruction. A robust CRT has been recently proposed when all the moduli have a common factor and the robust CRT is a searching based algorithm and no closed-from is given. In this paper, a closed-form robust CRT is proposed and a necessary and sufficient condition on the remainder errors for the closed-form robust CRT to hold is obtained. Furthermore, its performance analysis is given. It is shown that the reason for the robustness is from the remainder differential process in both searching based and our proposed closed-form robust CRT algorithms, which does no exist in the traditional CRT. We also propose an improved version of the closed-form robust CRT. Finally, we compare the performances of the traditional CRT, the searching based robust CRT and our proposed closed-form robust CRT (and its improved version) algorithms in terms of both theoretical analysis and numerical simulations. The results demonstrate that the proposed closed-form robust CRT (its improved version has the best performance) has the same performance but much simpler form than the searching based robust CRT. View full abstract»

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  • Distributed Learning in Multi-Armed Bandit With Multiple Players

    Page(s): 5667 - 5681
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (897 KB) |  | HTML iconHTML  

    We formulate and study a decentralized multi-armed bandit (MAB) problem. There are M distributed players competing for N independent arms. Each arm, when played, offers i.i.d. reward according to a distribution with an unknown parameter. At each time, each player chooses one arm to play without exchanging observations or any information with other players. Players choosing the same arm collide, and, depending on the collision model, either no one receives reward or the colliding players share the reward in an arbitrary way. We show that the minimum system regret of the decentralized MAB grows with time at the same logarithmic order as in the centralized counterpart where players act collectively as a single entity by exchanging observations and making decisions jointly. A decentralized policy is constructed to achieve this optimal order while ensuring fairness among players and without assuming any pre-agreement or information exchange among players. Based on a time-division fair sharing (TDFS) of the M best arms, the proposed policy is constructed and its order optimality is proven under a general reward model. Furthermore, the basic structure of the TDFS policy can be used with any order-optimal single-player policy to achieve order optimality in the decentralized setting. We also establish a lower bound on the system regret for a general class of decentralized polices, to which the proposed policy belongs. This problem finds potential applications in cognitive radio networks, multi-channel communication systems, multi-agent systems, web search and advertising, and social networks. View full abstract»

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  • Single Antenna Power Measurements Based Direction Finding

    Page(s): 5682 - 5692
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1678 KB) |  | HTML iconHTML  

    In this paper, the problem of estimating direction-of-arrival (DOA) of multiple uncorrelated sources from single antenna power measurements is addressed. Utilizing the fact that the antenna pattern is bandlimited and can be modeled as a finite sum of complex exponentials, we first show that the problem can be transformed into a frequency estimation problem. Then, we explain how the annihilating filter method can be used to solve for the DOA in the noiseless case. In the presence of noise, we propose to use Cadzow denoising that is formulated as an iterative algorithm derived from exploiting the matrix rank and linear structure properties. Furthermore, we have also derived the Cramér-Rao Bound (CRB) and reviewed several alternative approaches that can be used as a comparison to the proposed approach. From the simulation and experimental results, we demonstrate that the proposed approach significantly outperforms other approaches. It is also evident from the Monte Carlo analysis that the proposed approach converges to the CRB. View full abstract»

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  • Tensor Algebra and Multidimensional Harmonic Retrieval in Signal Processing for MIMO Radar

    Page(s): 5693 - 5705
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1305 KB) |  | HTML iconHTML  

    Detection and estimation problems in multiple-input multiple-output (MIMO) radar have recently drawn considerable interest in the signal processing community. Radar has long been a staple of signal processing, and MIMO radar presents challenges and opportunities in adapting classical radar imaging tools and developing new ones. Our aim in this article is to showcase the potential of tensor algebra and multidimensional harmonic retrieval (HR) in signal processing for MIMO radar. Tensor algebra and multidimensional HR are relatively mature topics, albeit still on the fringes of signal processing research. We show they are in fact central for target localization in a variety of pertinent MIMO radar scenarios. Tensor algebra naturally comes into play when the coherent processing interval comprises multiple pulses, or multiple transmit and receive subarrays are used (multistatic configuration). Multidimensional harmonic structure emerges for far-field uniform linear transmit/receive array configurations, also taking into account Doppler shift; and hybrid models arise in-between. This viewpoint opens the door for the application and further development of powerful algorithms and identifiability results for MIMO radar. Compared to the classical radar-imaging-based methods such as Capon or MUSIC, these algebraic techniques yield improved performance, especially for closely spaced targets, at modest complexity. View full abstract»

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  • Low Complexity Equalization for Doubly Selective Channels Modeled by a Basis Expansion

    Page(s): 5706 - 5719
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (369 KB) |  | HTML iconHTML  

    We propose a novel equalization method for doubly selective wireless channels, whose taps are represented by an arbitrary Basis Expansion Model (BEM). We view such a channel in the time domain as a sum of product-convolution operators created from the basis functions and the BEM coefficients. Equivalently, a frequency-domain channel can be represented as a sum of convolution-products. The product-convolution representation provides a low-complexity, memory efficient way to apply the channel matrix to a vector. We compute a regularized solution of a linear system involving the channel matrix by means of the GMRES and the LSQR algorithms, which utilize the product-convolution structure without ever explicitly creating the channel matrix. Our method applies to all cyclic-prefix transmissions. In an OFDM transmission with K subcarriers, each iteration of GMRES or LSQR requires only O(K K) flops and O(K) memory. Additionally, we further accelerate convergence of both GMRES and LSQR by using the single-tap equalizer as a preconditioner. We validate our method with numerical simulations of a WiMAX-like system (IEEE 802.16e) in channels with significant delay and Doppler spreads. The proposed equalizer achieves BERs comparable to those of MMSE equalization, and noticeably outperforms low-complexity equalizers using an approximation by a banded matrix in the frequency domain. With preconditioning, the lowest BERs are obtained within 3-16 iterations. Our approach does not use any statistical information about the wireless channel. View full abstract»

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  • Tensor-Based Channel Estimation and Iterative Refinements for Two-Way Relaying With Multiple Antennas and Spatial Reuse

    Page(s): 5720 - 5735
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1459 KB) |  | HTML iconHTML  

    Relaying is one of the key technologies to satisfy the demands of future mobile communication systems. In particular, two-way relaying is known to exploit the radio resources in a very efficient manner. In this contribution, we consider two-way relaying with amplify-and-forward (AF) MIMO relays. Since AF relays do not decode the signals, the separation of the data streams has to be performed by the terminals themselves. For this task both nodes require reliable channel knowledge of all relevant channel parameters. Therefore, we examine channel estimation schemes for two-way relaying with AF MIMO relays. We investigate a simple Least Squares (LS) based scheme for the estimation of the compound channels as well as a tensor-based channel estimation (TENCE) scheme which takes advantage of the special structure in the compound channel matrices to further improve the estimation accuracy. Note that TENCE is purely algebraic (i.e., it does not require any iterative procedures) and applicable to arbitrary antenna configurations. Then we demonstrate that the solution obtained by TENCE can be improved by an iterative refinement which is based on the structured least squares (SLS) technique. In this application, between one and four iterations are sufficient and consequently the increase in computational complexity is moderate. The iterative refinement is optional and targeted for cases where the channel estimation accuracy is critical. Moreover, we propose design rules for the training symbols as well as the relay amplification matrices during the training phase to facilitate the estimation procedures. Finally, we evaluate the achievable channel estimation accuracy of the LS-based compound channel estimation scheme as well as the tensor-based approach and its iterative refinement via numerical computer simulations. View full abstract»

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  • A Class of Channels Resulting in Ill-Convergence for CMA in Decision Feedback Equalizers

    Page(s): 5736 - 5743
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (742 KB) |  | HTML iconHTML  

    This paper analyzes the convergence of the constant modulus algorithm (CMA) in a decision feedback equalizer using only a feedback filter. Several works had already observed that the CMA presented a better performance than decision directed algorithm in the adaptation of the decision feedback equalizer, but theoretical analysis always showed to be difficult specially due to the analytical difficulties presented by the constant modulus criterion. In this paper, we surmount such obstacle by using a recent result concerning the CM analysis, first obtained in a linear finite impulse response context with the objective of comparing its solutions to the ones obtained through the Wiener criterion. The theoretical analysis presented here confirms the robustness of the CMA when applied to the adaptation of the decision feedback equalizer and also defines a class of channels for which the algorithm will suffer from ill-convergence when initialized at the origin. View full abstract»

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  • An Optimal Basis of Band-Limited Functions for Signal Analysis and Design

    Page(s): 5744 - 5755
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (631 KB) |  | HTML iconHTML  

    This paper studies signal concentration in the time and frequency domains using the general constrained variational method of Franks. The minimum k th (k = 0, 2, 4,...) moment time-duration measure for band-limited signals is formulated. A complete, orthonormal set of band-limited functions in L2([-W,W]) with the minimum fourth-moment time-duration measure is obtained. Numerical investigations of our optimal 4th moment functions show: 1) less energy concentration in the main lobe than the prolate spheroidal wave functions (PSWF); 2) higher energy concentration in the main lobe than Gabor's function; and 3) a larger main lobe than both PSWF and Gabor. Applications for our basis functions include: 1) radar systems and high resolution communication systems, and 2) representation and approximation to any band-limited signal in a given time interval. View full abstract»

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  • A Monte Carlo Implementation of the SAGE Algorithm for Joint Soft-Multiuser Decoding, Channel Parameter Estimation, and Code Acquisition

    Page(s): 5756 - 5766
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (822 KB) |  | HTML iconHTML  

    This paper presents an iterative scheme for joint timing acquisition, multi-channel parameter estimation, and multiuser soft-data decoding. As an example, an asynchronous convolutionally coded direct-sequence code-division multiple-access system is considered. The proposed receiver is derived within the space-alternating generalized expectation-maximization framework, implying that convergence in likelihood is guaranteed under appropriate conditions in contrast to many other iterative receiver architectures. The proposed receiver iterates between joint posterior data estimation, interference cancellation, and single-user channel estimation and timing acquisition. A Markov Chain Monte Carlo technique, namely Gibbs sampling, is employed to compute the a posteriori probabilities of data symbols in a computationally efficient way. Computer simulations in flat Rayleigh fading show that the proposed algorithm is able to handle high system loads unlike many other iterative receivers. View full abstract»

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  • A Nondata-Aided SNR Estimation Technique for Multilevel Modulations Exploiting Signal Cyclostationarity

    Page(s): 5767 - 5778
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1103 KB) |  | HTML iconHTML  

    Signal-to-noise ratio (SNR) estimators of linear modulation schemes usually operate at one sample per symbol at the matched filter output. In this paper we propose a new method for estimating the SNR in the complex additive white Gaussian noise (AWGN) channel that operates directly on the oversampled cyclostationary signal at the matched filter input. Exploiting cyclostationarity proves to be advantageous due to the fact that a signal-free Euclidean noise subspace can be identified such that only second order moments of the received waveform need to be computed. The proposed method is nondata-aided (NDA), as well as constellation and phase independent, and only requires prior timing synchronization to fully exploit the cyclostationarity property. The estimator can also be applied to nonconstant modulus constellations without requiring any tuning, which is a feature not found in existing approaches. Implementation aspects and simpler suboptimal solutions are also provided. 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