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Signal Processing, IEEE Transactions on

Issue 7 • Date July 2005

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Displaying Results 1 - 25 of 40
  • Table of contents

    Page(s): c1 - c4
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    Freely Available from IEEE
  • IEEE Transactions on Signal Processing publication information

    Page(s): c2
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    Freely Available from IEEE
  • Best Paper Award Recipients: A Message From the Editor-in-Chief

    Page(s): 2277 - 2278
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (320 KB) |  | HTML iconHTML  

    The IEEE TRANSACTIONS ON SIGNAL PROCESSING is fortunate to attract submissions of the highest quality and to publish articles that deal with topics that are at the forefront of what is happening in the field of signal processing and its adjacent areas. In this column, we are pleased to announce the TRANSACTIONS articles that have been selected to receive 2004 best paper awards. We would also like to encourage our readers to nominate TRANSACTIONS papers for awards. Nominations can be submitted online through the T RANSACTIONS website. View full abstract»

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  • Marginalized particle filters for mixed linear/nonlinear state-space models

    Page(s): 2279 - 2289
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (536 KB) |  | HTML iconHTML  

    The particle filter offers a general numerical tool to approximate the posterior density function for the state in nonlinear and non-Gaussian filtering problems. While the particle filter is fairly easy to implement and tune, its main drawback is that it is quite computer intensive, with the computational complexity increasing quickly with the state dimension. One remedy to this problem is to marginalize out the states appearing linearly in the dynamics. The result is that one Kalman filter is associated with each particle. The main contribution in this paper is the derivation of the details for the marginalized particle filter for a general nonlinear state-space model. Several important special cases occurring in typical signal processing applications will also be discussed. The marginalized particle filter is applied to an integrated navigation system for aircraft. It is demonstrated that the complete high-dimensional system can be based on a particle filter using marginalization for all but three states. Excellent performance on real flight data is reported. View full abstract»

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  • Linear prediction approach for efficient frequency estimation of multiple real sinusoids: algorithms and analyses

    Page(s): 2290 - 2305
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (839 KB) |  | HTML iconHTML  

    Based on the linear prediction property of sinusoidal signals, two constrained weighted least squares frequency estimators for multiple real sinusoids embedded in white noise are proposed. In order to achieve accurate frequency estimation, the first algorithm uses a generalized unit-norm constraint, while the second method employs a monic constraint. The weighting matrices in both methods are a function of the frequency parameters and are obtained in an iterative manner. For the case of a single real tone with sufficiently large data samples, both estimators provide nearly identical frequency estimates and their performance approaches Crame/spl acute/r-Rao lower bound (CRLB) for white Gaussian noise before the threshold effect occurs. Algorithms for closed-form single-tone frequency estimation are also devised. Computer simulations are included to corroborate the theoretical development and to contrast the estimator performance with the CRLB for different frequencies, observation lengths and signal-to-noise ratio (SNR) conditions. View full abstract»

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  • Semiparametric estimation of the frequency of unknown periodic functions and its application to laser vibrometry signals

    Page(s): 2306 - 2314
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    We propose a semiparametric approach to fundamental frequency estimation of an unknown periodic signal in additive white noise based on model selection. Our estimator maximizes a penalized version of the cumulated periodogram and is proved to be consistent and asymptotically efficient under very general conditions. When the number of observations is fixed, an implementation of this estimation method is proposed and illustrated on specific synthetic signals which arise in laser vibrometry. We extend this method for estimating the fundamental frequencies of two periodic functions having different fundamental frequencies when the data consist of their sum and additive white noise. We also compare the performances of our procedure with the so-called microdoppler technique, which is commonly used for laser vibrometry signals analysis. We show on simulated data that the penalized cumulated periodogram yields an accurate estimation of the frequencies at very low signal-to-noise ratios. View full abstract»

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  • Blind source separation/channel equalization of nonlinear channels with binary inputs

    Page(s): 2315 - 2323
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    In this paper, we consider blind source separation/channel equalization problems for an unknown nonlinear channel. By using the property of the binary alphabets, we show that blind nonlinear source separation/channel equalization problems can be easily solved by existing linear methods. View full abstract»

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  • Iterative multiuser uplink and downlink beamforming under SINR constraints

    Page(s): 2324 - 2334
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (440 KB) |  | HTML iconHTML  

    We study the problem of power efficient multiuser beamforming transmission for both uplink and downlink. The base station is equipped with multiple antennas, whereas the mobile units have single antennas. In the uplink, interference is canceled by successive decoding. In the downlink, ideal "dirty paper" precoding is assumed. The design goal is to minimize the total transmit power while maintaining individual SINR constraints. In the uplink, the optimization problem is solved by a recursive formula with low computational complexity. The downlink problem is solved by exploiting the duality between uplink and downlink; thus, the uplink solution carries over to the downlink. In the second part of the paper, we show how the solution can be applied to the problem of rate balancing in Gaussian multiuser channels. We propose a strategy for throughput-wise optimal transmission for broadcast and multiple access channels under a sum power constraint. Finally, we show that single-user transmission achieves the sum capacity in the low-SNR regime. We completely characterize the SNR-range where single-user transmission is optimal. View full abstract»

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  • On the information throughput and optimized power allocation for MIMO wireless systems with imperfect channel estimation

    Page(s): 2335 - 2347
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (856 KB) |  | HTML iconHTML  

    In this paper, we focus on the throughput analysis, outage evaluation and optimized power allocation for Multiple-Input Multiple-Output (MIMO) pilot-based wireless systems subject to short-term constraints on the radiated power and equipped with a feedback-path for communicating back to the transmitter the imperfect MIMO channel estimates available at the receiver. The case of the ergodic throughput for Gaussian distributed input signals is analyzed, and the conditions for the (asymptotical) achievement of the Shannon capacity are pointed out. The main contributions of this work may be so summarized. First, we develop closed-form analytical expressions for the computation of the ergodic information throughput conveyed by the considered MIMO system for the case of ideal feedback link. Second, we present an iterative algorithm for the optimized power allocation over the transmit antennas that explicitly accounts for the imperfect MIMO channel estimates available at the receiver. Third, after relaxing the assumption of ideal feedback link, we test the sensitivity of the proposed power allocation algorithm on errors possibly introduced by the feedback channel, and then, we numerically evaluate the resulting throughput loss. Finally, we develop closed-form upper and lower bounds on the outage probability that are asymptotically tight. View full abstract»

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  • MMSE multiuser detection for array multicarrier DS-CDMA in fading channels

    Page(s): 2348 - 2358
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (552 KB) |  | HTML iconHTML  

    Reception of asynchronous, multicarrier direct-sequence-code division multiple access (DS-CDMA) in time-varying, multipath radio channels with use of a receiving antenna array is investigated. Interference reducing minimum mean squared error (MMSE) receivers are discussed, and by considering the time-variation of the channel, a modified structure is derived which is efficient for channels experiencing small-scale fading. A blind implementation of this receiver is then proposed. Subspace concepts are applied to formulate a tracking, composite channel vector estimator which operates effectively in fading situations, even when high levels of interference are present. Both the modified MMSE weight matrix and diversity combining weights are generated from these channel estimates. Simulations of the proposed receiver show it to have superior performance over a standard MMSE receiver which is periodically re-evaluated to permit it to follow the channel variations due to small-scale fading. Furthermore, a hybrid MMSE receiver is proposed which applies different processing methods depending on each transmitters mobility, resulting in improved performance. View full abstract»

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  • Radar HRRP target recognition based on higher order spectra

    Page(s): 2359 - 2368
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (360 KB)  

    Radar high-resolution range profile (HRRP) is very sensitive to time-shift and target-aspect variation; therefore, HRRP-based radar automatic target recognition (RATR) requires efficient time-shift invariant features and robust feature templates. Although higher order spectra are a set of well-known time-shift invariant features, direct use of them (except for power spectrum) is impractical due to their complexity. A method for calculating the Euclidean distance in higher order spectra feature space is proposed in this paper, which avoids calculating the higher order spectra, effectively reducing the computation complexity and storage requirement. Moreover, according to the widely used scattering center model, theoretical analysis and experimental results in this paper show that the feature vector extracted from the average profile in a small target-aspect sector has better generalization performance than the average feature vector in the same sector when both of them are used as feature templates in HRRP-based RATR. The proposed Euclidean distance calculation method and average profile-based template database are applied to two classification algorithms [the template matching method (TMM) and the radial basis function network (RBFN)] to evaluate the recognition performances of higher order spectra features. Experimental results for measured data show that the power spectrum has the best recognition performance among higher order spectra. View full abstract»

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  • Decorrelating multiuser code-timing estimation for long-code CDMA with bandlimited chip waveforms

    Page(s): 2369 - 2381
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (568 KB) |  | HTML iconHTML  

    This paper addresses the problem of multiuser code-timing estimation for asynchronous uplink code-division multiple-access (CDMA) systems with aperiodic spreading codes and bandlimited chip waveforms. Two decorrelating code-timing estimation schemes, namely the frequency-domain least-squares (FLS) and frequency-domain weighted least-squares (FWLS) estimators, are developed. The two proposed estimators offer different tradeoffs between complexity and estimation accuracy. A critical step for decorrelating-based estimation is to decompose the received signal into subsignals of shorter duration. We discuss how to perform the decomposition to ensure improved identifiability and statistical stability of the proposed schemes. Due to a unique signal structure in the frequency domain, both the FLS and FWLS estimators admit efficient implementations that result in significant complexity reductions. The Crame´r-Rao bound for the estimation problem under study is derived and used as an assessment tool for the proposed estimators. Numerical results show that both of the proposed estimators can support overloaded systems (with more users than the processing gain) in multipath fading environments and significantly outperform a conventional technique based on matched-filter processing. View full abstract»

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  • Partial update LMS algorithms

    Page(s): 2382 - 2399
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (600 KB) |  | HTML iconHTML  

    Partial updating of LMS filter coefficients is an effective method for reducing computational load and power consumption in adaptive filter implementations. This paper presents an analysis of convergence of the class of Sequential Partial Update LMS algorithms (S-LMS) under various assumptions and shows that divergence can be prevented by scheduling coefficient updates at random, which we call the Stochastic Partial Update LMS algorithm (SPU-LMS). Specifically, under the standard independence assumptions, for wide sense stationary signals, the S-LMS algorithm converges in the mean if the step-size parameter μ is in the convergent range of ordinary LMS. Relaxing the independence assumption, it is shown that S-LMS and LMS algorithms have the same sufficient conditions for exponential stability. However, there exist nonstationary signals for which the existing algorithms, S-LMS included, are unstable and do not converge for any value of μ. On the other hand, under broad conditions, the SPU-LMS algorithm remains stable for nonstationary signals. Expressions for convergence rate and steady-state mean-square error of SPU-LMS are derived. The theoretical results of this paper are validated and compared by simulation through numerical examples. View full abstract»

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  • An LMS style variable tap-length algorithm for structure adaptation

    Page(s): 2400 - 2407
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (496 KB) |  | HTML iconHTML  

    Searching for the optimum tap-length that best balances the complexity and steady-state performance of an adaptive filter has attracted attention recently. Among existing algorithms that can be found in the literature, two of which, namely the segmented filter (SF) and gradient descent (GD) algorithms, are of particular interest as they can search for the optimum tap-length quickly. In this paper, at first, we carefully compare the SF and GD algorithms and show that the two algorithms are equivalent in performance under some constraints, but each has advantages/disadvantages relative to the other. Then, we propose an improved variable tap-length algorithm using the concept of the pseudo fractional tap-length (FT). Updating the tap-length with instantaneous errors in a style similar to that used in the stochastic gradient [or least mean squares (LMS)] algorithm, the proposed FT algorithm not only retains the advantages from both the SF and the GD algorithms but also has significantly less complexity than existing algorithms. Both performance analysis and numerical simulations are given to verify the new proposed algorithm. View full abstract»

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  • Second-order parameter estimation

    Page(s): 2408 - 2420
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (488 KB) |  | HTML iconHTML  

    This work provides a general framework for the design of second-order blind estimators without adopting any approximation about the observation statistics or the a priori distribution of the parameters. The proposed solution is obtained minimizing the estimator variance subject to some constraints on the estimator bias. The resulting optimal estimator is found to depend on the observation fourth-order moments that can be calculated analytically from the known signal model. Unfortunately, in most cases, the performance of this estimator is severely limited by the residual bias inherent to nonlinear estimation problems. To overcome this limitation, the second-order minimum variance unbiased estimator is deduced from the general solution by assuming accurate prior information on the vector of parameters. This small-error approximation is adopted to design iterative estimators or trackers. It is shown that the associated variance constitutes the lower bound for the variance of any unbiased estimator based on the sample covariance matrix. The paper formulation is then applied to track the angle-of-arrival (AoA) of multiple digitally-modulated sources by means of a uniform linear array. The optimal second-order tracker is compared with the classical maximum likelihood (ML) blind methods that are shown to be quadratic in the observed data as well. Simulations have confirmed that the discrete nature of the transmitted symbols can be exploited to improve considerably the discrimination of near sources in medium-to-high SNR scenarios. View full abstract»

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  • On spectral theory of cyclostationary signals in multirate systems

    Page(s): 2421 - 2431
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (456 KB) |  | HTML iconHTML  

    This paper studies two problems in the spectral theory of discrete-time cyclostationary signals: the cyclospectrum representation and the cyclospectrum transformation by linear multirate systems. Four types of cyclospectra are presented, and their interrelationships are explored. In the literature, the problem of cyclospectrum transformation by linear systems was investigated only for some specific configurations and was usually developed with inordinate complexities due to lack of a systematic approach. A general multirate system that encompasses most common systems-linear time-invariant systems and linear periodically time-varying systems-is proposed as the unifying framework; more importantly, it also includes many configurations that have not been investigated before, e.g., fractional sample-rate changers with cyclostationary inputs. The blocking technique provides a systematic solution as it associates a multirate system with an equivalent linear time-invariant system and cyclostationary signals with stationary signals; thus, the original problem is elegantly converted into a relatively simple one, which is solved in the form of matrix multiplication. View full abstract»

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  • On the residual variance and the prediction error for the LSF estimation method and new modified finite sample criteria for autoregressive model order selection

    Page(s): 2432 - 2441
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (352 KB) |  | HTML iconHTML  

    The case where the data sample size is finite and the least-squares-forward (LSF) method is used for autoregressive (AR) parameter estimation is considered. New formulas describing the residual variance and the prediction error behaviors in AR parameter estimation are derived, and the relation between the residual variance and the prediction error is determined. Based on this relation, the existing finite sample criteria for AR model order selection are modified, and it is shown that these modified criteria have better performance. View full abstract»

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  • Resampling algorithms and architectures for distributed particle filters

    Page(s): 2442 - 2450
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (472 KB) |  | HTML iconHTML  

    In this paper, we propose novel resampling algorithms with architectures for efficient distributed implementation of particle filters. The proposed algorithms improve the scalability of the filter architectures affected by the resampling process. Problems in the particle filter implementation due to resampling are described, and appropriate modifications of the resampling algorithms are proposed so that distributed implementations are developed and studied. Distributed resampling algorithms with proportional allocation (RPA) and nonproportional allocation (RNA) of particles are considered. The components of the filter architectures are the processing elements (PEs), a central unit (CU), and an interconnection network. One of the main advantages of the new resampling algorithms is that communication through the interconnection network is reduced and made deterministic, which results in simpler network structure and increased sampling frequency. Particle filter performances are estimated for the bearings-only tracking applications. In the architectural part of the analysis, the area and speed of the particle filter implementation are estimated for a different number of particles and a different level of parallelism with field programmable gate array (FPGA) implementation. In this paper, only sampling importance resampling (SIR) particle filters are considered, but the analysis can be extended to any particle filters with resampling. View full abstract»

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  • Efficient wavelet prefilters with optimal time-shifts

    Page(s): 2451 - 2461
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (752 KB) |  | HTML iconHTML  

    A wavelet prefilter maps sample values of an analyzed signal to the scaling function coefficient input of standard discrete wavelet transform (DWT) algorithms. The prefilter is the inverse of a certain postfilter convolution matrix consisting of integer sample values of a noninteger-shifted wavelet scaling function. For the prefilter and the DWT algorithms to have similar computational complexity, it is often necessary to use a "short enough" approximation of the prefilter. In addition to well-known quadrature formula and identity matrix prefilter approximations, we propose a Neumann series approximation, which is a band matrix truncation of the optimal prefilter, and derive simple formulas for the operator norm approximation error. This error shows a dramatic dependence on how the postfilter noninteger shift is chosen. We explain the meaning of this shift in practical applications, describe how to choose it, and plot optimally shifted prefilter approximation errors for 95 different Daubechies, Symlet, and B-spline wavelets. Whereas the truncated inverse is overall superior, the Neumann filters are by far the easiest ones to compute, and for some short support wavelets, they also give the smallest approximation error. For example, for Daubechies 1-5 wavelets, the simplest Neumann prefilter provide an approximation error reduction corresponding to 100-10 000 times oversampling in a nonprefiltered system. View full abstract»

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  • Performance limit of finite wordlength FIR digital filters

    Page(s): 2462 - 2469
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (344 KB) |  | HTML iconHTML  

    In many practical situations, it is necessary to represent the coefficients of a finite impulse response (FIR) digital filter by a finite number of bits. This not only degrades the filter frequency response but also introduces a theoretical limit on the performance of the filter. Derivation of a lower bound on filter degradation is the purpose of this paper. We consider a general case of a length N filter with a discrete set of allowable coefficients. A theorem that gives the lower bound on the increase in minimax approximation error that is caused by the finite wordlength restriction is presented. Its extension and application to filter design cases is demonstrated. The importance of this bound is not only theoretical. Its practical effectiveness is shown in the algorithm for optimal finite wordlength FIR filter design where it significantly reduces the amount of computation. View full abstract»

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  • On quantifying the benefits of SSPA linearization in UWC-136 systems

    Page(s): 2470 - 2476
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (344 KB) |  | HTML iconHTML  

    A novel comparative analysis of the benefits brought by different degrees of linearization to offset the modulation fidelity (MF) and spectrum regrowth impairments caused by solid-state power amplifier (SSPA) nonlinearity, as measured by error vector magnitude (EVM) and adjacent channel power ratio (ACPR) performance for TIA/EIA Universal Wireless Communication standard UWC-136 signals, are quantified. New results are presented showing the benefits of even modest levels of linearization but also that such benefits may be easily eroded at a receiver by the adjacent channel interference (ACI) in certain circumstances. An equation expressing the incremental MF deterioration experienced by a wanted channel (WC) signal, at its receiver, due to ACI arising from signals in the immediate upper and lower frequency channels, and as a function of adjacent channel (AC) to WC power differential, where signals are subject to different degrees of linearization, is presented. Typical SSPA characteristic values for the equation constants in the cases of one and two immediate AC signals are derived from simulation results. Interesting new results and conclusions relevant to the drafting of harmonious ACPR-EVM specifications and on the advisability of the inclusion of linearization schemes in transmitters, in the context of the UWC-136 system, are presented. View full abstract»

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  • Sparse solutions to linear inverse problems with multiple measurement vectors

    Page(s): 2477 - 2488
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    We address the problem of finding sparse solutions to an underdetermined system of equations when there are multiple measurement vectors having the same, but unknown, sparsity structure. The single measurement sparse solution problem has been extensively studied in the past. Although known to be NP-hard, many single-measurement suboptimal algorithms have been formulated that have found utility in many different applications. Here, we consider in depth the extension of two classes of algorithms-Matching Pursuit (MP) and FOCal Underdetermined System Solver (FOCUSS)-to the multiple measurement case so that they may be used in applications such as neuromagnetic imaging, where multiple measurement vectors are available, and solutions with a common sparsity structure must be computed. Cost functions appropriate to the multiple measurement problem are developed, and algorithms are derived based on their minimization. A simulation study is conducted on a test-case dictionary to show how the utilization of more than one measurement vector improves the performance of the MP and FOCUSS classes of algorithm, and their performances are compared. View full abstract»

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  • Fourier transform representation by frequency-time wavelets

    Page(s): 2489 - 2497
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (768 KB) |  | HTML iconHTML  

    A new concept of the A-wavelet transform is introduced, and the representation of the Fourier transform by the A-wavelet transform is described. Such a wavelet transform uses a fully scalable modulated window but not all possible shifts. A geometrical locus of frequency-time points for the A-wavelet transform is derived, and examples are given. The locus is considered "optimal" for the Fourier transform when a signal can be recovered by using only values of its wavelet transform defined on the locus. The inverse Fourier transform is also represented by the A*-wavelet transform defined on specific points in the time-frequency plane. The concept of the A-wavelet transform can be extended for representation of other unitary transforms. Such an example for the Hartley transform is described, and the reconstruction formula is given. View full abstract»

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  • The algebraic structure of frequency-selective MIMO channels

    Page(s): 2498 - 2512
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (568 KB) |  | HTML iconHTML  

    The theory of homogenous matrix polynomials provides a clear and powerful framework for the characterization of frequency selective multiple-input multiple-output (MIMO) channels. The concept proposed in this paper is a natural unification of methods, known from flat fading MIMO channels and frequency selective single-input single-output (SISO) channels. From the Kronecker canonical form of the channel equation, several subchannels can be identified. Each of them is related to an elementary divisor or minimal index of the channel. The elementary divisors are equivalent to the roots of the characteristic polynomial for SISO channels, whereas the minimal indices characterize the possible transmit or receive diversity in such channels. The knowledge of these values allows us to determine the necessary filter order, the minimal redundancy, and the conditions on the precoder such that a finite impulse response filter can suppress all intersymbol and interchannel interference completely. View full abstract»

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  • Fast and low complexity blind equalization via subgradient projections

    Page(s): 2513 - 2524
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (520 KB) |  | HTML iconHTML  

    We propose a novel blind equalization method based on subgradient search over a convex cost surface. This is an alternative to the existing iterative blind equalization approaches such as the Constant Modulus Algorithm (CMA), which often suffer from the convergence problems caused by their nonconvex cost functions. The proposed method is an iterative algorithm called SubGradient based Blind Algorithm (SGBA) for both real and complex constellations, with a very simple update rule. It is based on the minimization of the l norm of the equalizer output under a linear constraint on the equalizer coefficients using subgradient iterations. The algorithm has a nice convergence behavior attributed to the convex l cost surface as well as the step size selection rules associated with the subgradient search. We illustrate the performance of the algorithm using examples with both complex and real constellations, where we show that the proposed algorithm's convergence is less sensitive to initial point selection, and a fast convergence behavior can be achieved with a judicious selection of step sizes. Furthermore, the amount of data required for the training of the equalizer is significantly lower than most of the existing schemes. 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