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Signal Processing, IEEE Transactions on

Issue 12 • Date Dec. 2005

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Displaying Results 1 - 25 of 38
  • 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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  • Group decorrelation enhanced subspace method for identifying FIR MIMO channels driven by unknown uncorrelated colored sources

    Page(s): 4429 - 4441
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (560 KB) |  | HTML iconHTML  

    Identification of finite-impulse-response (FIR) and multiple-input multiple-output (MIMO) channels driven by unknown uncorrelated colored sources is a challenging problem. In this paper, a group decorrelation enhanced subspace (GDES) method is presented. The GDES method uses the idea of subspace decomposition and signal decorrelation more effectively than the joint diagonalization enhanced subspace (JDES) method previously reported in the literature. The GDES method has a much better performance than the JDES method. The correctness of the GDES method is proved assuming that 1) the channel matrix is irreducible and column reduced and 2) the source spectral matrix has distinct diagonal functions. However, the GDES method has an inherent ability to trade off between the required condition on the channel matrix and that on the source spectral matrix. Simulations show that the GDES method yields good results even when the channel matrix is not irreducible, which is not possible at all for the JDES method. View full abstract»

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  • Continuous-time tracking algorithms involving two-time-scale Markov chains

    Page(s): 4442 - 4452
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (528 KB) |  | HTML iconHTML  

    This work is concerned with least-mean-squares (LMS) algorithms in continuous time for tracking a time-varying parameter process. A distinctive feature is that the true parameter process is changing at a fast pace driven by a finite-state Markov chain. The states of the Markov chain are divisible into a number of groups. Within each group, the transitions take place rapidly; among different groups, the transitions are infrequent. Introducing a small parameter into the generator of the Markov chain leads to a two-time-scale formulation. The tracking objective is difficult to achieve. Nevertheless, a limit result is derived yielding algorithms for limit systems. Moreover, the rates of variation of the tracking error sequence are analyzed. Under simple conditions, it is shown that a scaled sequence of the tracking errors converges weakly to a switching diffusion. In addition, a numerical example is provided and an adaptive step-size algorithm developed. View full abstract»

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  • Matched direction detectors and estimators for array processing with subspace steering vector uncertainties

    Page(s): 4453 - 4463
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (480 KB) |  | HTML iconHTML  

    In this paper, we consider the problem of estimating and detecting a signal whose associated spatial signature is known to lie in a given linear subspace but whose coordinates in this subspace are otherwise unknown, in the presence of subspace interference and broad-band noise. This situation arises when, on one hand, there exist uncertainties about the steering vector but, on the other hand, some knowledge about the steering vector errors is available. First, we derive the maximum-likelihood estimator (MLE) for the problem and compute the corresponding Crame´r-Rao bound. Next, the maximum-likelihood estimates are used to derive a generalized likelihood ratio test (GLRT). The GLRT is compared and contrasted with the standard matched subspace detectors. The performances of the estimators and detectors are illustrated by means of numerical simulations. View full abstract»

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  • Array interpolation and DOA MSE reduction

    Page(s): 4464 - 4471
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    Interpolation or mapping of data from a given real array to data from a virtual array of more suitable geometry is well known in array signal processing. This operation allows arrays of any geometry to be used with fast direction-of-arrival (DOA) estimators designed for linear arrays. In an earlier companion paper , a first-order condition for zero DOA bias under such mapping was derived and was also used to construct a design algorithm for the mapping matrix that minimized the DOA estimate bias. This bias-minimizing theory is now extended to minimize not only bias, but also to consider finite sample effects due to noise and reduce the DOA mean-square error (MSE). An analytical first-order expression for mapped DOA MSE is derived, and a design algorithm for the transformation matrix that minimizes this MSE is proposed. Generally, DOA MSE is not reduced by minimizing the size of the mapping errors but instead by rotating these errors and the associated noise subspace into optimal directions relative to a certain gradient of the DOA estimator criterion function. The analytical MSE expression and the design algorithm are supported by simulations that show not only conspicuous MSE improvements in relevant scenarios, but also a more robust preprocessing for low signal-to-noise ratios (SNRs) as compared with the pure bias-minimizing design developed in the previous paper. View full abstract»

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  • Detection of particle sources with directional detector arrays and a mean-difference test

    Page(s): 4472 - 4484
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    In this paper, the problem of detecting far-field particle sources, such as nuclear, radioactive, optical, or cosmic, is considered. This problem arises in applications including security, surveillance, visual systems, and astronomy. The authors propose a mean-difference test (MDT) with cubic and spherical detector arrays, assuming Poisson distributed measurement models. Through performance analysis, including computing the probability of detection for a given probability of false alarm, the authors show that the MDT has a number of advantages over the generalized likelihood-ratio test (GLRT), such as computational efficiency, higher probability of detection, asymptotic constant false-alarm rate (CFAR), and applicability to low signal-to-noise ratio (SNR). For each array, the authors also present an estimator to find the source direction. Finally, Monte Carlo numerical examples are conducted that confirm the analytical results. View full abstract»

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  • Efficient subspace-based algorithm for adaptive bearing estimation and tracking

    Page(s): 4485 - 4505
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (888 KB) |  | HTML iconHTML  

    In some practical applications of array processing, the directions of the incident signals should be estimated adaptively, and/or the time-varying directions should be tracked promptly. In this paper, an adaptive bearing estimation and tracking (ABEST) algorithm is investigated for estimating and tracking the uncorrelated and correlated narrow-band signals impinging on a uniform linear array (ULA) based on the subspace-based method without eigendecomposition (SUMWE), where a linear operator is obtained from the array data to form a basis for the space by exploiting the array geometry and its shift invariance property. Specifically, the space is estimated using the least-mean-square (LMS) or normalized LMS (NLMS) algorithm, and the directions are updated using the approximate Newton method. The transient analyses of the LMS and NLMS algorithms are studied, where the "weight" (i.e., the linear operator) is in the form of a matrix and there is a correlation between the "additive noise" and "input data" that involve the instantaneous correlations of the received array data in the updating equation, and the step-size stability conditions are derived explicitly. In addition, the analytical expressions for the mean-square error (MSE) and mean-square deviation (MSD) learning curves of the LMS algorithm are clarified. The effectiveness of the ABEST algorithm is verified, and the theoretical analyses are corroborated through numerical examples. Simulation results show that the ABEST algorithm is computationally simple and has good adaptation and tracking abilities. View full abstract»

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  • Closed-form blind MIMO channel estimation for orthogonal space-time block codes

    Page(s): 4506 - 4517
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (648 KB) |  | HTML iconHTML  

    In this paper, a new computationally simple approach to blind decoding of orthogonal space-time block codes (OSTBCs) is proposed. Using specific properties of OSTBCs, the authors' approach estimates the channel matrix in a closed form and in a fully blind fashion. This channel estimate is then used in the maximum-likelihood (ML) receiver to decode the information symbols. The proposed estimation technique provides consistent channel estimates, and, as a result, the performance of the authors' blind ML receiver approaches that of the coherent ML receiver, which exploits the exact channel state information (CSI). Simulation results demonstrate the performance improvements achieved by the proposed blind decoding algorithm relative to the popular differential space-time modulation scheme. View full abstract»

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  • Tree pruning with subadditive penalties

    Page(s): 4518 - 4525
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (312 KB) |  | HTML iconHTML  

    In this paper we study the problem of pruning a binary tree by minimizing, over all pruned subtrees of the given tree, an objective function that combines an additive cost term with a penalty term that depends only on tree size. We present algorithms for general size-based penalties, although our focus is on subadditive penalties (roughly, penalties that grow more slowly than linear penalties with increasing tree size). Such penalties are motivated by recent results in statistical learning theory for decision trees, but may have wider application as well. We show that the family of pruned subtrees induced by a subadditive penalty is a subset of the family induced by an additive penalty. This implies (by known results about additive penalties) that the family induced by a subadditive penalty 1) is nested; 2) is unique; and 3) can be computed efficiently. It also implies that, when a single tree is to be selected by cross-validation from the family of prunings, subadditive penalties will never present a richer set of options than an additive penalty. View full abstract»

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  • Joint estimation of symbol timing and carrier frequency offset of OFDM signals over fast time-varying multipath channels

    Page(s): 4526 - 4535
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (480 KB) |  | HTML iconHTML  

    In this paper, we present a novel joint algorithm to estimate the symbol timing and carrier frequency offsets of wireless orthogonal frequency division multiplexing (OFDM) signals. To jointly estimate synchronization parameters using the maximum likelihood (ML) criterion, researchers have derived conventional models only from additive white Gaussian noise (AWGN) or single-path fading channels. We develop a general ML estimation algorithm that can accurately calculate symbol timing and carrier frequency offsets over a fast time-varying multipath channel. To reduce overall estimation complexity, the proposed scheme consists of two estimation stages: coarse and fine synchronizations. A low complexity coarse synchronization based on the least-squares (LS) method can rapidly estimate the rough symbol timing and carrier frequency offsets over a fast time-varying multipath channel. The subsequent ML fine synchronization can then obtain accurate final results based on the previous coarse synchronization. Simulations demonstrate that the coarse-to-fine method provides a good tradeoff between estimation accuracy and computational complexity. View full abstract»

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  • A block floating-point treatment to the LMS algorithm: efficient realization and a roundoff error analysis

    Page(s): 4536 - 4544
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (456 KB) |  | HTML iconHTML  

    An efficient scheme is presented for implementing the LMS-based transversal adaptive filter in block floating-point (BFP) format, which permits processing of data over a wide dynamic range, at temporal and hardware complexities significantly less than that of a floating-point processor. Appropriate BFP formats for both the data and the filter coefficients are adopted, taking care so that they remain invariant to interblock transition and weight updating operation, respectively. Care is also taken to prevent overflow during filtering, as well as weight updating processes jointly, by using a dynamic scaling of the data and a slightly reduced range for the step size, with the latter having only marginal effect on convergence speed. Extensions of the proposed scheme to the sign-sign LMS and the signed regressor LMS algorithms are taken up next, in order to reduce the processing time further. Finally, a roundoff error analysis of the proposed scheme under finite precision is carried out. It is shown that in the steady state, the quantization noise component in the output mean-square error depends on the step size both linearly and inversely. An optimum step size that minimizes this error is also found out. View full abstract»

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  • Low-complexity constrained affine-projection algorithms

    Page(s): 4545 - 4555
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (744 KB) |  | HTML iconHTML  

    This paper proposes low-complexity constrained affine-projection (CAP) algorithms. The algorithms are suitable for linearly constrained filtering problems often encountered in communications systems. The CAP algorithms derived in this paper trade convergence speed and computational complexity in the same way as the conventional affine-projection (AP) algorithm. In addition, data-selective versions of the CAP algorithm are derived based on the concept of set-membership filtering. The set-membership constrained affine-projection (SM-CAP) algorithms include several constraint sets in order to construct a space of feasible solutions for the coefficient updates. The SM-CAP algorithms include a data-dependent step size that provides fast convergence and low mean-squared error. The paper also discusses important aspects of convergence and stability of constrained normalized adaptation algorithms and shows that normalization may introduce bias in the final solution. View full abstract»

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  • The total variance of a periodogram-based spectral estimate of a stochastic process with spectral uncertainty and its application to classifier design

    Page(s): 4556 - 4567
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    The variance of a spectral estimate of a stochastic process is essential to the formulation and performance of a spectral classifier. The overall variance of a spectral estimate originates from two sources: the within-class spectral uncertainty and the variance introduced in the spectral estimation procedure. In this paper, we derive the total variance of a periodogram-based spectral estimate under some assumptions. Using this result, we formulate a linear spectral classifier based on Fisher's separability metric. The classifier is used to classify two oceanographic processes: ocean convection versus internal waves. View full abstract»

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  • Iterative receivers with bit-level cancellation and detection for MIMO-BICM systems

    Page(s): 4568 - 4577
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (416 KB) |  | HTML iconHTML  

    The multiple-input multiple-output (MIMO) detection in the iterative receiver is investigated for a modulation of square constellation. For computationally efficient MIMO detection, we employ the hard-decision bit-level cancellation (BLC) to reduce the dimension of the MIMO detection as it has advantages over the symbol-level cancellation: 1) it can provide better performance and 2) the extrinsic bit information can be directly used for the BLC. A set of reliably decoded bits from the channel decoder in iterations is used for hard-decision BLC to approximate the maximum a posteriori probability (MAP) detection under the partial MAP detection principle. For the bits that are not canceled, the ing using projection is employed to approximate the MAP detection. This approach is applicable for a lower order modulation. For a higher order modulation, the minimum mean square error (MMSE) filtering with soft-decision cancellation (SC) is applied to further mitigate the bits that are not canceled. View full abstract»

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  • Carrier phase and frequency estimation for pilot-symbol assisted transmission: bounds and algorithms

    Page(s): 4578 - 4587
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (584 KB) |  | HTML iconHTML  

    In this paper we consider the Cramer-Rao lower bound (CRB) for the joint estimation of the carrier phase and the frequency offset from a noisy linearly modulated burst signal containing random data symbols (DSs) as well as known pilot symbols (PSs). We point out that the CRB depends on the location of the PSs in the burst, the number of PSs, the number of DSs, the signal-to-noise ratio (SNR), and the data modulation scheme. Distributing the PSs symmetrically about the center of the burst and estimating the carrier phase in the center of the burst interval decouples the frequency and phase estimation, making the CRB for phase estimation independent of the specific location of the PSs. At low and moderate SNR, the CRBs for both phase and frequency estimation decrease as the fraction of the PSs in the burst increases. In addition, the CRB for frequency estimation decreases as the PSs are separated with more DSs. Numerical evaluation of the CRB indicates that the carrier phase and frequency of a "hybrid" burst (i.e., containing PSs and DSs) can be estimated more accurately when exploiting both the presence of the DSs and the a priori knowledge about the PSs, instead of using only the knowledge about the PSs (and ignoring the DSs), or considering all the received symbols (PSs and DSs) as unknown (and ignoring the knowledge about the PSs). Comparison of the CRB with the performance of existing carrier synchronizers shows that the iterative soft-decision-directed (sDD) estimator with data-aided (DA) initialization performs very closely to the CRB and provides a large improvement over the classical non-data-aided (NDA) estimator at lower SNR. View full abstract»

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  • Identification of quasi-periodically varying systems using the combined nonparametric/parametric approach

    Page(s): 4588 - 4598
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (440 KB) |  | HTML iconHTML  

    The problem of identification of quasi-periodically varying finite impulse response systems is considered. Neither the number of system frequency modes nor the initial frequency values are assumed to be known a priori. The proposed solution is a blend of the parametric (model based) and nonparametric (discrete Fourier transform based) approach to system identification. It is shown that the results of nonparametric analysis can be used to identify the number of frequency modes and to determine initial conditions needed to smoothly start (or restart) the model-based tracking algorithm. Such a combined nonparametric/parametric approach allows one to preserve advantages of both frameworks, leading to an estimation procedure which guarantees global frequency search, high-frequency resolution, fast initial convergence, and good steady-state tracking capabilities. View full abstract»

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  • Generalized adaptive notch and comb filters for identification of quasi-periodically varying systems

    Page(s): 4599 - 4609
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (464 KB) |  | HTML iconHTML  

    The problem of identification/tracking of quasi-periodically varying real-valued systems is considered. This problem is a generalization, to the system case, of a classical signal processing task of either elimination or extraction of nonstationary sinusoidal signals buried in noise. The solution is based on the exponentially weighted basis function (EWBF) approach. The proposed algorithms are capable of tracking slow changes in system frequencies, which means that not only the expansion coefficients in the basis function description of the analyzed system but also the basis functions themselves are adjusted in an adaptive manner. First, assuming that the system frequencies are known and constant, the running basis and fixed basis variants of the EWBF algorithm are derived, and their relationship to the classical notch filter with constrained poles and zeros is established. Next, the frequency-adaptive versions of both algorithms are obtained using the gradient search and recursive prediction error principles, respectively. Finally, the interrelated frequencies case is analyzed and two additional parameter tracking algorithms (generalized adaptive comb filters) are derived. View full abstract»

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  • Gaussian Cramer-Rao bound for direction estimation of noncircular signals in unknown noise fields

    Page(s): 4610 - 4618
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (448 KB) |  | HTML iconHTML  

    This paper focuses on the stochastic Cramer-Rao bound (CRB) on direction of arrival (DOA) estimation accuracy for noncircular Gaussian sources in the general case of an arbitrary unknown Gaussian noise field parameterized by a vector of unknowns. Explicit closed-form expressions of the stochastic CRB for DOA parameters alone are obtained directly from the Slepian-Bangs formula for general noncircular complex Gaussian distributions. As a special case, the CRB under the nonuniform white noise assumption is derived. Our expressions can be viewed as extensions of the well-known results by Stoica and Nehorai, Ottersten et al., Weiss and Friedlander, Pesavento and Gershman, and Gershman et al. Some properties of these CRBs are proved and finally, these bounds are numerically compared with the conventional CRBs under the circular complex Gaussian distribution for different unknown noise field models. View full abstract»

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  • Oversampled filter banks as error correcting codes: theory and impulse noise correction

    Page(s): 4619 - 4630
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    Oversampled filter banks (OFBs) provide an overcomplete representation of their input signal. This paper describes how OFBs can be considered as error-correcting codes acting on real or complex sequences, very much like classical binary convolutional codes act on binary sequences. The structured redundancy introduced by OFBs in subband signals can be used to increase robustness to noise. In this paper, we define the notions of code subspace, syndrome, and parity-check polynomial matrix for OFBs. Furthermore, we derive generic expressions for projection-based decoding, suitable for the case when a simple second-order model completely characterizes the noise incurred by subband signals. We also develop a nonlinear hypotheses-test based decoding algorithm for the case when the noise in subbands is constituted by a Gaussian background noise and impulsive errors (a model that adequately describes the action of both quantization noise and transmission errors). Simulation results show that the algorithm effectively removes the effect of impulsive errors occurring with a probability of 10-3. View full abstract»

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  • High-resolution biosensor spectral peak shift estimation

    Page(s): 4631 - 4639
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (376 KB) |  | HTML iconHTML  

    In this paper, we present a maximum likelihood (ML) approach to high-resolution estimation of the shifts of a spectral signal. This spectral signal arises in application of optically based resonant biosensors, where high resolution in the estimation of signal shift is synonymous with high sensitivity to biological interactions. For the particular sensor of interest, the underlying signal is nonuniformly sampled and exhibits Poisson amplitude statistics. Shift estimation accuracies orders of magnitude finer than the sample spacing are sought. The new ML-based formulation leads to a solution approach different from typical resonance shift estimation methods based on polynomial fitting and peak (or ) estimation and tracking. View full abstract»

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  • Computationally efficient systolic architecture for computing the discrete Fourier transform

    Page(s): 4640 - 4651
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1048 KB)  

    A new high-performance systolic architecture for calculating the discrete Fourier transform (DFT) is described which is based on two levels of transform factorization. One level uses an index remapping that converts the direct transform into structured sets of arithmetically simple four-point transforms. Another level adds a row/column decomposition of the DFT. The architecture supports transform lengths that are not powers of two or based on products of coprime numbers. Compared to previous systolic implementations, the architecture is computationally more efficient and uses less hardware. It provides low latency as well as high throughput, and can do both one- and two-dimensional DFTs. An automated computer-aided design tool was used to find latency and throughput optimal designs that matched the target field programmable gate array structure and functionality. View full abstract»

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  • Low-complexity selected mapping schemes for peak-to-average power ratio reduction in OFDM systems

    Page(s): 4652 - 4660
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (448 KB) |  | HTML iconHTML  

    Orthogonal frequency-division multiplexing (OFDM) is an attractive transmission technique for high-bit-rate communication systems. One major drawback of OFDM is the high peak-to-average power ratio (PAPR) of the transmitter's output signal. The selected mapping (SLM) approach provides good performance for PAPR reduction, but it requires a bank of inverse fast Fourier transforms (IFFTs) to generate a set of candidate transmission signals, and this requirement usually results in high computational complexity. In this paper, we propose a kind of low-complexity conversions to replace the IFFT blocks in the conventional SLM method. Based on the proposed conversions, we develop two novel SLM schemes with much lower complexity than the conventional one; the first method uses only one IFFT block to generate the set of candidate signals, while the second one uses two IFFT blocks. Computer simulation results show that, as compared to the conventional SLM scheme, the first proposed approach has slightly worse PAPR reduction performance and the second proposed one reaches almost the same PAPR reduction performance. View full abstract»

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  • Convex primal decomposition for multicarrier linear MIMO transceivers

    Page(s): 4661 - 4674
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (488 KB) |  | HTML iconHTML  

    The design of linear transceivers for multiple-input-multiple-output (MIMO) communication systems with channel state information is particularly challenging for two main reasons. First, since several substreams are established through the MIMO channel, it is not even clear how the quality of the system should be measured. Second, once a cost function has been chosen to measure the quality, the optimization of the system according to such criterion is generally difficult due to the nonconvexity of the problem. Recent results have solved the problem for the wide family of Schur-concave/convex functions, resulting in simple closed-form solutions when the system is modeled as a single MIMO channel. However, with several MIMO channels (such as in multi-antenna multicarrier systems), the solution is generally more involved, leading in some cases to the need to employ general-purpose interior-point methods. This problem is specifically addressed in this paper by combining the closed-form solutions for single MIMO channels with a primal decomposition approach, resulting in a simple and efficient method for multiple MIMO channels. The extension to functions that are not Schur-concave/convex is also briefly considered, relating the present work with a recently proposed method to minimize the average bit error rate (BER) of the system. View full abstract»

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  • A new wireless network medium access protocol based on cooperation

    Page(s): 4675 - 4684
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (424 KB) |  | HTML iconHTML  

    In this paper, we propose a new media access protocol for wireless networks, that due to its ability to resolve collisions can achieve high throughput. We view the wireless network as a spatially distributed antenna with antenna elements linked via the wireless channel. When there is a collision, the collided packets are saved in a buffer. In the slots following the collision, a set of nodes designated as nonregenerative relays retransmit the signal that they received during the collision slot. By processing the originally collided packets and the signals forwarded by the relays, the destination node can recover the original packets. The proposed scheme maintains the benefits of ALOHA systems, i.e., needs no scheduling overhead and is suitable for bursty sources, such as multimedia sources. It also offers the benefits of multi-antenna systems, i.e., spatial diversity while employing a single transmit/receive antenna at each node. Spatial diversity enables it to be robust to the wireless channel. The proposed approach achieves higher throughput and energy savings than existing techniques that allow for multiple packet reception. 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