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Signal Processing, IEEE Transactions on

Issue 2 • Date Jan.15, 2013

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Displaying Results 1 - 25 of 33
  • Front Cover

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

    Page(s): C2
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  • Table of Contents

    Page(s): 219 - 220
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  • Table of Contents

    Page(s): 221 - 222
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  • Gaussian Process Regression for Sensor Networks Under Localization Uncertainty

    Page(s): 223 - 237
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4207 KB) |  | HTML iconHTML  

    In this paper, we formulate Gaussian process regression with observations under the localization uncertainty due to the resource-constrained sensor networks. In our formulation, effects of observations, measurement noise, localization uncertainty, and prior distributions are all correctly incorporated in the posterior predictive statistics. The analytically intractable posterior predictive statistics are proposed to be approximated by two techniques, viz., Monte Carlo sampling and Laplace's method. Such approximation techniques have been carefully tailored to our problems and their approximation error and complexity are analyzed. Simulation study demonstrates that the proposed approaches perform much better than approaches without considering the localization uncertainty properly. Finally, we have applied the proposed approaches on the experimentally collected real data from a dye concentration field over a section of a river and a temperature field of an outdoor swimming pool to provide proof of concept tests and evaluate the proposed schemes in real situations. In both simulation and experimental results, the proposed methods outperform the quick-and-dirty solutions often used in practice. View full abstract»

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  • The Sign-Definiteness Lemma and Its Applications to Robust Transceiver Optimization for Multiuser MIMO Systems

    Page(s): 238 - 252
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3730 KB) |  | HTML iconHTML  

    We formally generalize the sign-definiteness lemma to the case of complex-valued matrices and multiple norm-bounded uncertainties. This lemma has found many applications in the study of the stability of control systems, and in the design and optimization of robust transceivers in communications. We then present three different novel applications of this lemma in the area of multi-user multiple-input multiple-output (MIMO) robust transceiver optimization. Specifically, the scenarios of interest are: (i) robust linear beamforming in an interfering adhoc network, (ii) robust design of a general relay network, including the two-way relay channel as a special case, and (iii) a half-duplex one-way relay system with multiple relays. For these networks, we formulate the design problems of minimizing the (sum) MSE of the symbol detection subject to different average power budget constraints. We show that these design problems are non-convex (with bilinear or trilinear constraints) and semi-infinite in multiple independent uncertainty matrix-valued variables. We propose a two-stage solution where in the first step the semi-infinite constraints are converted to linear matrix inequalities using the generalized sign-definiteness lemma, and in the second step, we use an iterative algorithm based on alternating convex search (ACS). Via simulations we evaluate the performance of the proposed scheme. View full abstract»

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  • An Adaptive Conditional Zero-Forcing Decoder With Full-Diversity, Least Complexity and Essentially-ML Performance for STBCs

    Page(s): 253 - 263
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3118 KB) |  | HTML iconHTML  

    A low complexity, essentially-ML decoding technique for the Golden code and the three antenna Perfect code was introduced by Sirianunpiboon, Howard and Calderbank. Though no theoretical analysis of the decoder was given, the simulations showed that this decoding technique has almost maximum-likelihood (ML) performance. Inspired by this technique, in this paper we introduce two new low complexity decoders for Space-Time Block Codes (STBCs)-the Adaptive Conditional Zero-Forcing (ACZF) decoder and the ACZF decoder with successive interference cancellation (ACZF-SIC), which include as a special case the decoding technique of Sirianunpiboon We show that both ACZF and ACZF-SIC decoders are capable of achieving full-diversity, and we give a set of sufficient conditions for an STBC to give full-diversity with these decoders. We then show that the Golden code, the three and four antenna Perfect codes, the three antenna Threaded Algebraic Space-Time code and the four antenna rate 2 code of Srinath and Rajan are all full-diversity ACZF/ACZF-SIC decodable with complexity strictly less than that of their ML decoders. Simulations show that the proposed decoding method performs identical to ML decoding for all these five codes. These STBCs along with the proposed decoding algorithm have the least decoding complexity and best error performance among all known codes for Nt ≤ 4 transmit antennas. We further provide a lower bound on the complexity of full-diversity ACZF/ACZF-SIC decoding. All the five codes listed above achieve this lower bound and hence are optimal in terms of minimizing the ACZF/ACZF-SIC decoding complexity. Both ACZF and ACZF-SIC decoders are amenable to sphere decoding implementation. View full abstract»

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  • Particle Based Smoothed Marginal MAP Estimation for General State Space Models

    Page(s): 264 - 273
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2451 KB) |  | HTML iconHTML  

    We consider the smoothing problem for a general state space system using sequential Monte Carlo (SMC) methods. The marginal smoother is assumed to be available in the form of weighted random particles from the SMC output. New algorithms are developed to extract the smoothed marginal maximum a posteriori (MAP) estimate of the state from the existing marginal particle smoother. Our method does not need any kernel fitting to obtain the posterior density from the particle smoother. The proposed estimator is then successfully applied to find the unknown initial state of a dynamical system and to address the issue of parameter estimation problem in state space models. View full abstract»

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  • Characterization of Non-Stationary Channels Using Mismatched Wiener Filtering

    Page(s): 274 - 288
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4051 KB) |  | HTML iconHTML  

    A common simplification in the statistical treatment of linear time-varying (LTV) wireless channels is the approximation of the channel as a stationary random process inside certain time-frequency regions. We develop a methodology for the determination of local quasi-stationarity (LQS) regions, i.e., local regions in which a channel can be treated as stationary. Contrary to previous results relying on, to some extent, heuristic measures and thresholds, we consider a finite-length Wiener filter as realistic channel estimator and relate the size of LQS regions in time to the degradation of the mean square error (MSE) of the estimate due to outdated and thus mismatched channel statistics. We show that for certain power spectral densities (PSDs) of the channel a simplified but approximate evaluation of the matched MSE based on the assumption of an infinite filtering length yields a lower bound on the actual matched MSE. Moreover, for such PSDs, the actual MSE degradation is upper-bounded and the size of the actual LQS regions is lower-bounded by the approximate evaluation. Using channel measurements, we compare the evolution of the LQS regions based on the actual and the approximate MSE; they show strong similarities. View full abstract»

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  • A Semi-Parallel Successive-Cancellation Decoder for Polar Codes

    Page(s): 289 - 299
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2449 KB) |  | HTML iconHTML  

    Polar codes are a recently discovered family of capacity-achieving codes that are seen as a major breakthrough in coding theory. Motivated by the recent rapid progress in the theory of polar codes, we propose a semi-parallel architecture for the implementation of successive cancellation decoding. We take advantage of the recursive structure of polar codes to make efficient use of processing resources. The derived architecture has a very low processing complexity while the memory complexity remains similar to that of previous architectures. This drastic reduction in processing complexity allows very large polar code decoders to be implemented in hardware. An N=217 polar code successive cancellation decoder is implemented in an FPGA. We also report synthesis results for ASIC. View full abstract»

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  • Channel-Aware Decentralized Detection via Level-Triggered Sampling

    Page(s): 300 - 315
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4807 KB) |  | HTML iconHTML  

    We consider decentralized detection through distributed sensors that perform level-triggered sampling and communicate with a fusion center (FC) via noisy channels. Each sensor computes its local log-likelihood ratio (LLR), samples it using the level-triggered sampling mechanism, and at each sampling instant transmits a single bit to the FC. Upon receiving a bit from a sensor, the FC updates the global LLR and performs a sequential probability ratio test (SPRT) step. We derive the fusion rules under various types of channels. We further provide an asymptotic analysis on the average decision delay for the proposed channel-aware scheme, and show that the asymptotic decision delay is characterized by a Kullback-Leibler information number. The delay analysis facilitates the choice of the appropriate signaling schemes under different channel types for sending the 1-bit information from the sensors to the FC. View full abstract»

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  • H_{\infty } Fixed-Interval Smoothing Estimation for Time-Delay Systems

    Page(s): 316 - 326
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4428 KB) |  | HTML iconHTML  

    This paper is concerned with the H fixed-interval smoothing estimation for time-delay systems which include continuous-time case and discrete-time case. In the case of discrete-time systems, the problem can be solved by using the conventional state augmentation approach. However, this approach is not suitable for the continuous-time case. In this paper, we will propose a unified approach to study the H fixed-interval smoothing problem for both continuous-time and discrete-time systems with l output delays. By introducing a suitable stochastic linear time-delay models in an indefinite space, it is shown that the H fixed-interval smoother can be obtained by calculating H2 fixed-interval smoother for time-delay systems in an indefinite space. Therefore, based on the orthogonal projection theory in an indefinite space, the H fixed-interval smoothers for time-delay systems are designed by performing l+1 Riccati equations with the same dimension as the original systems. Moreover, a necessary and sufficient conditions for the existence of the H fixed-interval smoother will also be given. View full abstract»

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  • Quantization and Bit Allocation for Channel State Feedback in Relay-Assisted Wireless Networks

    Page(s): 327 - 339
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3182 KB) |  | HTML iconHTML  

    This paper investigates quantization of channel state information (CSI) and bit allocation across wireless links in a multi-source, single-relay cooperative cellular network. Our goal is to minimize the loss in performance, measured as the achievable sum rate, due to limited-rate quantization of CSI. We develop both a channel quantization scheme and allocation of limited feedback bits to the various wireless links. We assume that the quantized CSI is reported to a central node responsible for optimal resource allocation. We first derive tight lower and upper bounds on the difference in rates between the perfect CSI and quantized CSI scenarios. These bounds are then used to derive an effective quantizer for arbitrary channel distributions. Next, we use these bounds to optimize the allocation of bits across the links subject to a budget on total available quantization bits. In particular, we show that the optimal bit allocation algorithm allocates more bits to those links in the network that contribute the most to the sum-rate. Finally, the paper investigates the choice of the central node; we show that this choice plays a significant role in CSI bits required to achieve a target performance level. View full abstract»

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  • Efficient High-Dimensional Inference in the Multiple Measurement Vector Problem

    Page(s): 340 - 354
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3399 KB) |  | HTML iconHTML  

    In this work, a Bayesian approximate message passing algorithm is proposed for solving the multiple measurement vector (MMV) problem in compressive sensing, in which a collection of sparse signal vectors that share a common support are recovered from undersampled noisy measurements. The algorithm, AMP-MMV, is capable of exploiting temporal correlations in the amplitudes of non-zero coefficients, and provides soft estimates of the signal vectors as well as the underlying support. Central to the proposed approach is an extension of recently developed approximate message passing techniques to the amplitude-correlated MMV setting. Aided by these techniques, AMP-MMV offers a computational complexity that is linear in all problem dimensions. In order to allow for automatic parameter tuning, an expectation-maximization algorithm that complements AMP-MMV is described. Finally, a detailed numerical study demonstrates the power of the proposed approach and its particular suitability for application to high-dimensional problems. View full abstract»

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  • Joint Probability Mass Function Estimation From Asynchronous Samples

    Page(s): 355 - 364
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2749 KB) |  | HTML iconHTML  

    A common approach to study the relationship between different signals is to model them as random processes and estimate their joint probability distribution from the observed data. When synchronous samples of the random processes are available, then the empirical distribution gives a reliable estimate. However, in several situations, such as sensors spread over a vast region or a software probing smart phone sensors for readings, synchronous samples are either not available, or are difficult/costly to obtain. In such cases, we have to depend on non-periodic, asynchronous samples to obtain good estimates of the joint distribution. In this paper, we consider independent Poisson sampling of the individual random processes and we propose a kernel based estimate of the joint probability mass function. We prove that our estimate is consistent (in the mean-square sense) for strong mixing processes, which is a wide class of random processes including Markov processes. We also provide expressions for the asymptotic mean-square error (MSE), study the bias-variance tradeoff, and discuss the choice of the kernel bandwidth. By appropriately choosing the kernel, we show that we can obtain an asymptotic rate of T-4/5 for the MSE, where T is the interval of observation. We also present several numerical results to discuss the accuracy of our asymptotic approximations for finite T. View full abstract»

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  • Cramér-Rao Bound for Circular and Noncircular Complex Independent Component Analysis

    Page(s): 365 - 379
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5170 KB) |  | HTML iconHTML  

    Despite an increased interest in complex independent component analysis (ICA) during the last two decades, a closed form expression for the Cramér-Rao bound (CRB) for the demixing matrix is not known yet. In this paper, we fill this gap by deriving a closed-form expression for the CRB of the demixing matrix for instantaneous noncircular complex ICA. It contains the CRB for circular complex ICA and noncircular complex Gaussian ICA as two special cases. We also study the CRB numerically for the family of noncircular complex generalized Gaussian distributions and compare it to simulation results of two ICA estimators. Furthermore, we show how to extend the CRB to the case where the source signals are not temporally independent and identically distributed. View full abstract»

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  • On the Selection of Optimum Savitzky-Golay Filters

    Page(s): 380 - 391
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2209 KB) |  | HTML iconHTML  

    Savitzky-Golay (S-G) filters are finite impulse response lowpass filters obtained while smoothing data using a local least-squares (LS) polynomial approximation. Savitzky and Golay proved in their hallmark paper that local LS fitting of polynomials and their evaluation at the mid-point of the approximation interval is equivalent to filtering with a fixed impulse response. The problem that we address here is, “how to choose a pointwise minimum mean squared error (MMSE) S-G filter length or order for smoothing, while preserving the temporal structure of a time-varying signal.” We solve the bias-variance tradeoff involved in the MMSE optimization using Stein's unbiased risk estimator (SURE). We observe that the 3-dB cutoff frequency of the SURE-optimal S-G filter is higher where the signal varies fast locally, and vice versa, essentially enabling us to suitably trade off the bias and variance, thereby resulting in near-MMSE performance. At low signal-to-noise ratios (SNRs), it is seen that the adaptive filter length algorithm performance improves by incorporating a regularization term in the SURE objective function. We consider the algorithm performance on real-world electrocardiogram (ECG) signals. The results exhibit considerable SNR improvement. Noise performance analysis shows that the proposed algorithms are comparable, and in some cases, better than some standard denoising techniques available in the literature. View full abstract»

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  • Visual Tracking in Background Subtracted Image Sequences via Multi-Bernoulli Filtering

    Page(s): 392 - 397
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1043 KB) |  | HTML iconHTML  

    This correspondence presents a novel method for simultaneous tracking of multiple non-stationary targets in video. Our method operates directly on the video data and does not require any detection. We propose a multi-target likelihood function for the background-subtracted grey-scale image data, which admits multi-target conjugate priors. This allows the multi-target posterior to be efficiently propagated forward using the multi-Bernoulli filter. Our method does not need any training pattern or target templates and makes no prior assumptions about object types or object appearance. Case studies from the CAVIAR dataset show that our method can automatically track multiple targets and quickly finds targets entering or leaving the scene. View full abstract»

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  • Perturbation Analysis of Orthogonal Matching Pursuit

    Page(s): 398 - 410
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3813 KB) |  | HTML iconHTML  

    Orthogonal Matching Pursuit (OMP) is a canonical greedy pursuit algorithm for sparse approximation. Previous studies of OMP have considered the recovery of a sparse signal through Φ and y = Φx + b, where is a matrix with more columns than rows and denotes the measurement noise. In this paper, based on Restricted Isometry Property (RIP), the performance of OMP is analyzed under general perturbations, which means both y and Φ are perturbed. Though the exact recovery of an almost sparse signal x is no longer feasible, the main contribution reveals that the support set of the best k-term approximation of x can be recovered under reasonable conditions. The error bound between x and the estimation of OMP is also derived. By constructing an example it is also demonstrated that the sufficient conditions for support recovery of the best k-term approximation of are rather tight. When x is strong-decaying, it is proved that the sufficient conditions for support recovery of the best k-term approximation of x can be relaxed, and the support can even be recovered in the order of the entries' magnitude. Our results are also compared in detail with some related previous ones. View full abstract»

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  • Load Balanced Resampling for Real-Time Particle Filtering on Graphics Processing Units

    Page(s): 411 - 419
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1598 KB) |  | HTML iconHTML  

    The application of particle filters to real-time systems is often limited because of their computational complexity, and hence the use of graphics processing units (GPUs) that contain hundreds of processing elements on a chip is very promising. However, parallel implementations of particle filters with state-of-the-art systematic resampling on a GPU suffer from a severe workload imbalance problem, which results in fluctuation of the computation speed and hinders their application to real-time systems. We analyze the computational load imbalance of the systematic resampling method in conventional implementations, and show that the workload imbalance is proportional to the variance of weights in particle filters. Then, we propose a load balanced particle replication (LBPR) algorithm for systematic resampling, which shows almost constant execution speed and outperforms the conventional algorithm in terms of the worst-case computation time. The proposed algorithm has been implemented on an NVIDIA GTX580 GPU. View full abstract»

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  • Stable Signal Reconstruction via \ell ^1 -Minimization in Redundant, Non-Tight Frames

    Page(s): 420 - 426
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1626 KB) |  | HTML iconHTML  

    In many signal and image processing applications, a desired clean signal is distorted from blur and noise. Reconstructing the clean signal usually yields to a high dimensional ill-conditioned system of equations, where a direct solution would severely amplify the noise. Stable signal reconstruction requires the use of regularization techniques, which incorporate a priori knowledge about the signal. A particular successful property for that purpose is the sparsity of the analysis coefficients of the clean signal in a suitable frame or dictionary, which can be implemented via l1 -minimization. Most existing stable recovery results for l1-analysis minimization require the frame to be an orthonormal basis. This contrasts practical applications, where redundant frames often perform better than bases. In this paper we address this issue and derive stable recovery results for l1-analysis minimization in redundant, possibly non-tight frames. View full abstract»

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  • Compressed Sensing With Prior Information: Information-Theoretic Limits and Practical Decoders

    Page(s): 427 - 439
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3504 KB) |  | HTML iconHTML  

    This paper considers the problem of sparse signal recovery when the decoder has prior information on the sparsity pattern of the data. The data vector x=[x1,...,xN]T has a randomly generated sparsity pattern, where the i-th entry is non-zero with probability pi. Given knowledge of these probabilities, the decoder attempts to recover x based on M random noisy projections. Information-theoretic limits on the number of measurements needed to recover the support set of x perfectly are given, and it is shown that significantly fewer measurements can be used if the prior distribution is sufficiently non-uniform. Furthermore, extensions of Basis Pursuit, LASSO, and Orthogonal Matching Pursuit which exploit the prior information are presented. The improved performance of these methods over their standard counterparts is demonstrated using simulations. View full abstract»

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  • Estimation of NAND Flash Memory Threshold Voltage Distribution for Optimum Soft-Decision Error Correction

    Page(s): 440 - 449
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1803 KB) |  | HTML iconHTML  

    As the feature size of NAND flash memory decreases, the threshold voltage signal becomes less reliable, and its distribution varies significantly with the number of program-erase (PE) cycles and the data retention time. We have developed parameter estimation algorithms to find the means and variances of the threshold voltage distribution that is modeled as a Gaussian mixture. The proposed methods find the best-fit parameters by minimizing the squared Euclidean distance between the measured threshold voltage values and those obtained from the Gaussian mixture model. For the parameter estimation, the gradient descent (GD) and the Levenberg-Marquardt (LM) based methods are employed. The developed algorithms are applied to both simulated and real NAND flash memory. It is also demonstrated that error correction with the estimated mean and variance values yields much better performance when compared to the method that only updates the mean. View full abstract»

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  • D-MAP: Distributed Maximum a Posteriori Probability Estimation of Dynamic Systems

    Page(s): 450 - 466
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5313 KB) |  | HTML iconHTML  

    This paper develops a framework for the estimation of a time-varying random signal using a distributed sensor network. Given a continuous time model sensors collect noisy observations and produce local estimates according to the discrete time equivalent system defined by the sampling period of observations. Estimation is performed using a maximum a posteriori probability estimator (MAP) within a given window of interest. To mediate the incorporation of information from other sensors we introduce Lagrange multipliers to penalize the disagreement between neighboring estimates. We show that the resulting distributed (D)-MAP algorithm is able to track dynamical signals with a small error. This error is characterized in terms of problem constants and vanishes with the sampling time as long as the log-likelihood function which is assumed to be log-concave satisfies a smoothness condition. We implement the D-MAP algorithm for a linear and a nonlinear system model to show that the performance corroborates with theoretical findings. View full abstract»

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  • Optimization of Cooperative Beamforming for SC-FDMA Multi-User Multi-Relay Networks by Tractable D.C. Programming

    Page(s): 467 - 479
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4405 KB) |  | HTML iconHTML  

    This paper addresses the optimal cooperative beamforming design for multi-user multi-relay wireless networks in which the single-carrier frequency division multiple access (SC-FDMA) technique is employed at the terminals. The problem of interest is to find the beamforming weights across relays to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among source users subject to individual power constraints at each relay. Such a beamforming design is shown to be a hard nonconvex optimization problem and therefore it is mathematically challenging to find the optimal solution. By exploring its partial convex structures, we recast the design problem as minimization of a d.c. (difference of two convex) objective function subject to convex constraints and develop an effective iterative algorithm of low complexity to solve it. Simulation results show that our optimal cooperative beamforming scheme realizes the inherent diversity order of the relay network and it performs significantly better than the equal-power beamforming weights. 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