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Signal Processing, IEEE Transactions on

Issue 12 • Date Dec. 2007

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Displaying Results 1 - 25 of 37
  • 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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  • State and Disturbance Estimator for Time-Delay Systems With Application to Fault Estimation and Signal Compensation

    Page(s): 5541 - 5551
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (744 KB) |  | HTML iconHTML  

    For a state-space time-delay system with linearly coupled input and output disturbances, a simultaneous state and disturbance estimation technique is developed. For a nonlinear state-space time-delay system with dependent input and output disturbances, a nonlinear estimator is also proposed to estimate system state and disturbance at the same time. The proposed estimator techniques are applied next to estimate system state and fault signal. Via actuator and/or sensor signal compensation, a simple and efficient fault-tolerant operation can be realized. In the developed design, no limitations and prior knowledge are required on the considered faults. Moreover, identical actuator and/or sensor switches and control gain reconstruction are not necessary. Therefore, the proposed estimation and fault-tolerant scheme is economical and convenient in practical applications. After that, the design techniques are extended to the case of systems with a class of uncoupled input and output faults. Examples and simulations given show excellent signal estimation and fault-tolerant performance. View full abstract»

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  • Order Statistics Correlation Coefficient as a Novel Association Measurement With Applications to Biosignal Analysis

    Page(s): 5552 - 5563
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1028 KB) |  | HTML iconHTML  

    In this paper, we propose a novel correlation coefficient based on order statistics and rearrangement inequality. The proposed coefficient represents a compromise between the Pearson's linear coefficient and the two rank-based coefficients, namely Spearman's rho and Kendall's tau. Theoretical derivations show that our coefficient possesses the same basic properties as the three classical coefficients. Experimental studies based on four models and six biosignals show that our coefficient performs better than the two rank-based coefficients when measuring linear associations; whereas it is well able to detect monotone nonlinear associations like the two rank-based coefficients. Extensive statistical analyses also suggest that our new coefficient has superior anti-noise robustness, small biasedness, high sensitivity to changes in association, accurate time-delay detection ability, fast computational speed, and robustness under monotone nonlinear transformations. View full abstract»

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  • Speech Enhancement Combining Optimal Smoothing and Errors-In-Variables Identification of Noisy AR Processes

    Page(s): 5564 - 5578
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1592 KB) |  | HTML iconHTML  

    In the framework of speech enhancement, several parametric approaches based on an a priori model for a speech signal have been proposed. When using an autoregressive (AR) model, three issues must be addressed. (1) How to deal with AR parameter estimation? Indeed, due to additive noise, the standard least squares criterion leads to biased estimates of AR parameters. (2) Can an estimation of the variance of the additive noise for each speech frame be obtained? A voice activity detector is often used for its estimation. (3) Which estimation rules and techniques (filtering, smoothing, etc.) can be considered to retrieve the speech signal? Our contribution in this paper is threefold. First, we propose to view the identification of the noisy AR process as an errors-in-variables problem. This blind method has the advantage of providing accurate estimations of both the AR parameters and the variance of the additive noise. Second, we propose an alternative algorithm to standard Kalman smoothing, based on a constrained minimum variance estimation procedure with a lower computational cost. Third, the combination of these two steps is investigated. It provides better results than some existing speech enhancement approaches in terms of signal-to-noise-ratio (SNR), segmental SNR, and informal subjective tests. View full abstract»

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  • Rank Estimation and Redundancy Reduction of High-Dimensional Noisy Signals With Preservation of Rare Vectors

    Page(s): 5579 - 5592
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1675 KB) |  | HTML iconHTML  

    In this paper, we address the problem of redundancy-reduction of high-dimensional noisy signals that may contain anomaly (rare) vectors, which we wish to preserve. For example, when applying redundancy reduction techniques to hyperspectral images, it is essential to preserve anomaly pixels for target detection purposes. Since rare-vectors contribute weakly to the -norm of the signal as compared to the noise, -based criteria are unsatisfactory for obtaining a good representation of these vectors. The proposed approach combines and norms for both signal-subspace and rank determination and considers two aspects: One aspect deals with signal-subspace estimation aiming to minimize the maximum of data-residual -norms, denoted as , for a given rank conjecture. The other determines whether the rank conjecture is valid for the obtained signal-subspace by applying Extreme Value Theory results to model the distribution of the noise -norm. These two operations are performed alternately using a suboptimal greedy algorithm, which makes the proposed approach practically plausible. The algorithm was applied on both synthetically simulated data and on a real hyperspectral image producing better results than common -based methods. View full abstract»

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  • The Averaged, Overdetermined, and Generalized LMS Algorithm

    Page(s): 5593 - 5603
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (688 KB) |  | HTML iconHTML  

    This paper provides and exploits one possible formal framework in which to compare and contrast the two most important families of adaptive algorithms: the least-mean square (LMS) family and the recursive least squares (RLS) family. Existing and well-known algorithms, belonging to any of these two families, like the LMS algorithm and the RLS algorithm, have a natural position within the proposed formal framework. The proposed formal framework also includes - among others - the LMS/overdetermined recursive instrumental variable (ORIV) algorithm and the generalized LMS (GLMS) algorithm, which is an instrumental variable (IV) enable LMS algorithm. Furthermore, this formal framework allows a straightforward derivation of new algorithms, with enhanced properties respect to the existing ones: specifically, the ORIV algorithm is exported to the LMS family, resulting in the derivation of the averaged, overdetermined, and generalized LMS (AOGLMS) algorithm, an overdetermined LMS algorithm able to work with an IV. The proposed AOGLMS algorithm overcomes - as we analytically show here - the limitations of GLMS and possesses a much lower computational burden than LMS/ORIV, being in this way a better alternative to both algorithms. Simulations verify the analysis. View full abstract»

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  • Design Of 2-D Recursive Digital Filters Using Nonsymmetric Half-Plane Allpass Filters

    Page(s): 5604 - 5618
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1244 KB) |  | HTML iconHTML  

    A novel structure using recursive nonsymmetric half-plane (NSHP) digital allpass filters (DAFs) is presented for designing 2-D recursive digital filters. First, several important properties of 2-D recursive DAFs with NSHP support regions for filter coefficients are investigated. The stability of the 2-D recursive NSHP DAFs is guaranteed by using a spectral factorization-based algorithm. Then, the important characteristics regarding the proposed novel structure are discussed. The design problem of 2-D recursive digital filters using the novel structure is considered. We formulate the problem by forming an objective function consisting of the weighted sum of magnitude, group delay, and stability-related errors. A design technique using a trust-region Newton-conjugate gradient method in conjunction with the analytic derivatives of the objective function is presented to efficiently solve the resulting optimization problem. The novelty of the presented 2-D structure is that it possesses the advantage of better performance in designing a variety of 2-D recursive digital filters over existing 2-D filter structures. Finally, several design examples are provided for conducting illustration and comparison. View full abstract»

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  • Generalized Discrete Multiwavelet Transform With Embedded Orthogonal Symmetric Prefilter Bank

    Page(s): 5619 - 5629
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5050 KB) |  | HTML iconHTML  

    Prefilters are generally applied to the discrete multiwavelet transform (DMWT) for processing scalar signals. To fully utilize the benefit offered by DMWT, it is important to have the prefilter designed appropriately so as to preserve the important properties of multiwavelets. To this end, we have recently shown that it is possible to have the prefilter designed to be maximally decimated, yet preserve the linear phase and orthogonal properties as well as the approximation power of multiwavelets. However, such design requires the point of symmetry of each channel of the prefilter to match with the scaling functions of the target multiwavelet system. It can be very difficult to find a compatible filter bank structure; and in some cases, such filter structure simply does not exist, e.g., for multiwavelets of multiplicity 2. In this paper, we suggest a new DMWT structure in which the prefilter is combined with the first stage of DMWT. The advantage of the new structure is twofold. First, since the prefiltering stage is embedded into DMWT, the computational complexity can be greatly reduced. Experimental results show that an over 20% saving in arithmetic operations can be achieved comparing with the traditional DMWT realizations. Second, the new structure provides additional design freedom that allows the resulting prefilters to be maximally decimated, orthogonal and symmetric even for multiwavelets of low multiplicity. We evaluated the new DMWT structure in terms of computational complexity, energy compaction ratio as well as the compression performance when applying to a VQ based image coding system. Satisfactory results are obtained in all cases comparing with the traditional approaches. View full abstract»

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  • Minimum BER Block-Based Precoder Design for Zero-Forcing Equalization: An Oblique Projection Framework

    Page(s): 5630 - 5642
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (722 KB) |  | HTML iconHTML  

    This work devises a minimum bit error rate (BER) block-based precoder used in block transmission systems with the proposed cascaded zero-forcing (ZF) equalizer. The study framework is developed as follows. For a block-based precoder, a received signal model is formulated for the two redundancy schemes, viz., trailing-zeros (TZ) and cyclic-prefix (CP). By exploiting the property of oblique projection, a cascaded equalizer for block transmission systems is proposed and implemented with a scheme, in which the inter-block interference (IBI) is completely eliminated by the oblique projection and followed by a matrix degree-of-freedom for inter-symbol interference (ISI) equalization. With the available channel state information at the transmitter side, the matrix for ISI equalization of the cascaded equalizer is utilized to design an optimum block-based precoder, such that the BER is minimized, subject to the ISI-free and the transmission power constraints. Accordingly, the cascaded equalizer with the ISI-free constraint yields a cascaded ZF equalizer. Theoretical derivations and simulation results confirm that the proposed framework not only retains identical BER performance to previous works for cases with sufficient redundancy, but also allows their results to be extended to the cases of insufficient redundancy. View full abstract»

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  • Beampattern Synthesis via a Matrix Approach for Signal Power Estimation

    Page(s): 5643 - 5657
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1726 KB) |  | HTML iconHTML  

    We present new beampattern synthesis approaches based on semidefinite relaxation (SDR) for signal power estimation. The conventional approaches use weight vectors at the array output for beampattern synthesis, which we refer to as the vector approaches (VA). Instead of this, we use weight matrices at the array output, which leads to matrix approaches (MA). We consider several versions of MA, including a (data) adaptive MA (AMA), as well as several data-independent MA designs. For all of these MA designs, globally optimal solutions can be determined efficiently due to the convex optimization formulations obtained by SDR. Numerical examples as well as theoretical evidence are presented to show that the optimal weight matrix obtained via SDR has few dominant eigenvalues, and often only one. When the number of dominant eigenvalues of the optimal weight matrix is equal to one, MA reduces to VA, and the main advantage offered by SDR in this case is to determine the globally optimal solution efficiently. Moreover, we show that the AMA allows for strict control of main-beam shape and peak sidelobe level while retaining the capability of adaptively nulling strong interferences and jammers. Numerical examples are also used to demonstrate that better beampattern designs can be achieved via the data-independent MA than via its VA counterpart. View full abstract»

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  • Reduced-Rank MDL Method for Source Enumeration in High-Resolution Array Processing

    Page(s): 5658 - 5667
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (467 KB) |  | HTML iconHTML  

    This paper proposes a reduced-rank minimum description length (MDL) method to enumerate the incident waves impinging on a uniform linear array (ULA). First, a new observation data and a reference signal are formed from sensor data by means of the shift-invariance property of the ULA. A cross-correlation between them is calculated, which is able to capture signal information and efficiently suppress additive noise. Second, the normalized cross-correlation is used as initial information for a recursion procedure to quickly partition the observation data into two orthogonal components in a signal subspace and a reduced-rank noise subspace. The components in the noise subspace are employed to calculate the total code length that is required to encode the observation data. Finally, the model with the shortest code length, namely the minimum description length, is chosen as the best model. Unlike the traditional MDL methods, this method partitions the observation data into the cleaner signal and noise subspace components by means of the recursion procedure, avoiding the estimation of a covariance matrix and its eigendecomposition. Thus, the method has the advantage of computational simplicity. Its performance is demonstrated via numerical results. View full abstract»

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  • Low-Complexity LMMSE-Based MIMO-OFDM Channel Estimation Via Angle-Domain Processing

    Page(s): 5668 - 5680
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1260 KB) |  | HTML iconHTML  

    In many situations, multiple-input-multiple-output with orthogonal frequency division multiplexing (MIMO-OFDM) channels tend to be spatially correlated due, for example, to limited scattering. Prior knowledge of this channel spatial correlation and the channel frequency correlation can be exploited by using the linear minimum-mean-square-error (LMMSE) technique. However, the complexity of the 2-D LMMSE technique, which fully utilizes both the channel spatial and frequency correlation is quite high. To solve this problem, this paper presents and analyzes several low-complexity, suboptimal, approximated LMMSE channel estimation techniques in the angle domain, where the channel model lends itself to a physical interpretation. The choice of angle-domain techniques is largely dependent on the extent of channel stochastic information (e.g., channel correlation or power) that is available to the receiver. Nevertheless, all the proposed angle-domain techniques have much lower complexity compared to the 2-D LMMSE technique. Further, all the angle-domain techniques improve over the conventional least square (LS) technique for all the typical MIMO-OFDM models under consideration. More importantly, our simulation results show that the angle-domain quasi 1-D (Ql-D) LMMSE technique can achieve similar performance compared to the 2-D LMMSE technique for all typical MIMO-OFDM models with significantly lower complexity. View full abstract»

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  • Filterbank Decompositions for (Non)-Systematic Reed–Solomon Codes With Applications to Soft Decoding

    Page(s): 5681 - 5694
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1339 KB) |  | HTML iconHTML  

    This paper focuses on Reed-Solomon (RS) codes, which are the most widespread classical error correcting codes. Recently, we have shown that an finite-impulse response (FIR) critically subsampled filterbank representation can be derived for some RS codes. However, this work only addresses RS codes with a non-coprime codeword and dataword length, seriously limiting its practical usability. In this paper, an alternative purely algebraic method is presented to construct such a filterbank. Apart from providing additional insight into the algebraic structure of (non-systematic) RS codes, this method is suited to eliminate the non-coprimeness constraint mentioned before. Using this filterbank decomposition, a RS code is broken into smaller subcodes that can subsequently be used to build a soft-in soft-out (SISO) RS decoder. It is shown how any RS code, written as an FIR filterbank, can be SISO decoded using the filterbank based decoder. Owing to the importance of systematic RS codes, it is shown that any systematic RS code can be decoded using the FIR filterbank decomposition. This leads to better decoding performance in addition with a slightly lower complexity. A further extension towards systematic RS codes is also presented in this paper resulting in an infinite-impulse response critically subsampled filterbank representation. View full abstract»

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  • Optimized Projections for Compressed Sensing

    Page(s): 5695 - 5702
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (984 KB) |  | HTML iconHTML  

    Compressed sensing (CS) offers a joint compression and sensing processes, based on the existence of a sparse representation of the treated signal and a set of projected measurements. Work on CS thus far typically assumes that the projections are drawn at random. In this paper, we consider the optimization of these projections. Since such a direct optimization is prohibitive, we target an average measure of the mutual coherence of the effective dictionary, and demonstrate that this leads to better CS reconstruction performance. Both the basis pursuit (BP) and the orthogonal matching pursuit (OMP) are shown to benefit from the newly designed projections, with a reduction of the error rate by a factor of 10 and beyond. View full abstract»

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  • Noncoherent Communication in Multiple-Antenna Systems: Receiver Design and Codebook Construction

    Page(s): 5703 - 5715
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (657 KB) |  | HTML iconHTML  

    In this paper, we address the problem of space-time codebook design for noncoherent communications in multiple-antenna wireless systems. In contrast with other approaches, the channel matrix is modeled as an unknown deterministic parameter at both the receiver and the transmitter, and the Gaussian observation noise is allowed to have an arbitrary correlation structure, known by the transmitter and the receiver. In order to handle the unknown deterministic space-time channel, a generalized likelihood ratio test (GLRT) receiver is considered. A new methodology for space-time codebook design under this noncoherent setup is proposed. It optimizes the probability of error of the GLRT receiver's detector in the high signal-to-noise ratio (SNR) regime by solving a high-dimensional nonlinear nonsmooth optimization problem in a two-step approach. First, a convex semidefinite programming (SDP) relaxation of the codebook design problem yields a rough estimate of the optimal codebook. This is then refined through a geodesic descent optimization algorithm that exploits the Riemannian geometry imposed by the power constraints on the space-time codewords. The results obtained through computer simulations illustrate the advantages of our method. For the specific case of spatio-temporal white observation noise, our codebook constructions replicate the performance of state-of-the-art known solutions. The main point here is that our methodology permits extending the codebook construction to any given correlated noise environment. The simulation results show good performance of these new designed codes in colored noise setups. View full abstract»

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  • Successive Interference Cancellation Using Constellation Structure

    Page(s): 5716 - 5730
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (644 KB) |  | HTML iconHTML  

    An approach to successive interference cancellation is presented that exploits the structure of the combined signal constellation in a multiuser system. The asymptotic conditional efficiency of a successive detector is defined, based on the conditional probability of error at high signal-to-noise ratio (SNR), as a quantitative measure for evaluating detector performance at each stage of successive detection. The joint successive interference canceller (JSIC) that jointly detects consecutive users in an ordered set is proposed as an improvement over the conventional successive interference canceller (SIC). The maximal asymptotic conditional efficiency successive interference canceller (MACE-SIC) and its JSIC equivalent (MACE-JSIC) are also derived as the multiuser detectors that achieve the highest asymptotic conditional multiuser efficiency at each stage of successive detection among all possible SIC and JSIC detectors, respectively, given any particular ordering of user signals. The ordering of users achieving the highest asymptotic conditional efficiency at each stage of successive detection is derived. Performance bounds based on the signal constellation structure are derived to quantify the gain of the MACE-JSIC detector compared to the MACE-SIC detector. View full abstract»

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  • Precise Positioning by Phase Processing of Sound Waves

    Page(s): 5731 - 5738
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1325 KB) |  | HTML iconHTML  

    This paper addresses the problem of position determination based on coherence of phases of a set of received sinusoidal acoustic waves. It describes an easily implementable test bed to assess the feasibility of carrier-phase-based differential ranging techiques for precise positioning applications in indoor environments. Futhermore, the use of a discrete set of frequencies fits the scope of using signals of opportunity, such as broadcast stations in RF. Acoustic waves are, themselves, relevant for positioning in specific environments, such as underwater. The main subject of the paper is the formulation and solution of the position determination goal as an -dimensional integer optimization problem. This -dimensional problem can be expressed as a weighted least-squares minimization problem with real and integer variables. The associated integer least-squares problem can be solved efficiently in practice as shown. This work is illustrated with some experimental data and a critical analysis of the obtained results. View full abstract»

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  • Complex Noise Analysis of DMT

    Page(s): 5739 - 5754
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (534 KB) |  | HTML iconHTML  

    In this paper, we consider discrete multitone (DMT) or baseband orthogonal frequency-division multiplexing (OFDM) modulation and perform a detailed noise analysis which takes into account dependencies and power (variance) differences of real and imaginary part after the complex-valued discrete Fourier transform (DFT). The derivation is based on the so-called pseudocovariance matrix of a complex random vector, which was introduced by Neeser and Massey (1993). We show that the relevant pseudocovariance matrix is not the zero matrix in general, in contrast to pass-band OFDM, for which it can be proven (under certain assumptions) that all occurring pseudocovariance matrices are vanishing. We show that for colored noise rotated rectangular symbol constellations are more appropriate than the common quadratic quadrature amplitude modulation (QAM) symbol constellations with respect to capacity and symbol error probability, and we derive formulas for the rotation angles and constellation sizes/densities. Finally, we extend the results to a multitransceiver [multiple-input-multiple-output (MIMO)] scenario, for which we assume a very general noise model at the inputs of the receivers, allowing correlations between the noise signals of different receivers. This requires the introduction of pseudocross-covariance matrices of complex random vectors, which are the important objects (together with cross-covariance matrices) in the MIMO situation. View full abstract»

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  • Minimum-Mean-Output-Energy Blind Adaptive Channel Shortening for Multicarrier SIMO Transceivers

    Page(s): 5755 - 5771
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (840 KB) |  | HTML iconHTML  

    In this paper, we propose a blind adaptive channel-shortening method for designing finite-impulse response time-domain equalizers (TEQs) in single-input multiple-output systems employing multicarrier modulations. The proposed algorithm, which relies on a constrained minimization of the mean-output-energy at the TEQ output, does not require a priori knowledge of the channel impulse response or transmission of training sequences, and admits an effective and computationally efficient adaptive implementation. Moreover, the proposed TEQ is narrowband-interference resistant and its synthesis only requires an upper bound (rather than the exact knowledge) of the channel order. Numerical simulations are provided to illustrate the advantages of the proposed technique over a recently developed blind channel shortener. View full abstract»

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  • Multidimensional Subcarrier Mapping for Bit-Interleaved Coded OFDM With Iterative Decoding

    Page(s): 5772 - 5781
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (619 KB) |  | HTML iconHTML  

    A power and bandwidth-efficient bit-interleaved coded modulation (BICM) with orthogonal frequency-division multiplexing (OFDM) and iterative decoding (BI-COFDM-ID) using combined multidimensional mapping and subcarrier grouping is proposed for broadband transmission in a frequency-selective fading environment. A tight bound on the asymptotic error performance is developed, which shows that subcarrier mapping and grouping have independent impacts on the overall error performance, and hence, they can be independently optimized. Specifically, it is demonstrated that the optimal subcarrier mapping is similar to the optimal multidimensional mapping for bit-interleaved coded modulation with iterative decoding (BICM-ID) in frequency-flat Rayleigh fading environment, whereas the optimal subcarrier grouping is the same with that of OFDM with linear constellation preceding (LCP). Furthermore, analytical and simulation results show that the proposed system with the combined optimal subcarrier mapping and grouping can achieve the full channel diversity without using LCP and provide significant coding gains as compared to the previously studied BI-COFDM-ID with the same power, bandwidth, and receiver complexity. View full abstract»

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  • Max-SINR ISI/ICI-Shaping Multicarrier Communication Over the Doubly Dispersive Channel

    Page(s): 5782 - 5795
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1208 KB) |  | HTML iconHTML  

    For communication over doubly dispersive channels, we consider the design of multicarrier modulation (MCM) schemes based on time-frequency shifts of prototype pulses. We consider the case where the receiver knows the channel state and the transmitter knows the channel statistics (e.g., delay spread and Doppler spread) but not the channel state. Previous work has examined MCM pulses designed for suppression of inter-symbol/inter-carrier interference (ISI/ICI) subject to orthogonal or biorthogonal constraints. In doubly dispersive channels, however, complete suppression of ISI/ICI is impossible, and the ISI/ICI pattern generated by these (bi)orthogonal schemes can be difficult to equalize, especially when operating at high bandwidth efficiency. We propose a different approach to MCM pulse design, whereby a limited expanse of ISI/ICI is tolerated in modulation/demodulation and treated near-optimally by a downstream equalizer. Specifically, we propose MCM pulse designs that maximize a signal-to-interference-plus-noise ratio (SINR) which suppresses ISI/ICI outside a target pattern. In addition, we propose two low-complexity turbo equalizers, based on minimum mean-squared error and maximum likelihood criteria, respectively, that leverage the structure of the target ISI/ICI pattern. The resulting system exhibits an excellent combination of low complexity, low bit-error rate, and high spectral efficiency. View full abstract»

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  • Evidence Filtering

    Page(s): 5796 - 5805
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (704 KB) |  | HTML iconHTML  

    A novel framework named evidence filtering for processing information from multiple sensor modalities is presented. This approach is based on conditional belief notions in Dempster-Shafer (DS) evidence theory and enables one to directly process temporally and spatially distributed sensor data and infer on the ldquofrequencyrdquo characteristics of various events of interest. The method can accommodate partial and incomplete information from multiple sensor modalities during the process. Certain restrictions on the coefficients impose several challenges in the design of evidence filters suggesting that arbitrary frequency shaping is not possible. A design procedure and the analysis of nonrecursive evidence filters is presented. A threat assessment scenario is simulated and the results are presented to illustrate the applications of evidence filtering. View full abstract»

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  • Information Retrieval and Processing in Sensor Networks: Deterministic Scheduling Versus Random Access

    Page(s): 5806 - 5820
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1316 KB) |  | HTML iconHTML  

    We investigate the effect of medium-access control (MAC) used in information retrieval by a mobile access point (AP) on information processing in large-scale sensor network, where sensors are unreliable and subject to outage. We focus on a 1-D sensor network and assume that the location information is available locally at each sensor and unavailable to the AP. For a fixed collection interval, two types of MAC schemes are considered: the deterministic scheduling, which collects data from predetermined sensors locations, and random access, which collects data from random locations. We compare the signal estimation performance of the two MACs, using the expected maximum distortion as the performance measure. For large sensor networks with fixed density, we show that there is a critical threshold on the sensor outage probability Pout-For _Pout < e-lambda(1+o(1)), where lambda is the throughput of the random access protocol, the deterministic scheduling provides better reconstruction performance. However, for Pout > e-lambda(1+o(1)), the performance degradation from missing data samples due to sensor outage does not justify the effort of scheduling; simple random access outperforms the optimal scheduling. View full abstract»

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  • Hybrid ALOHA: A Novel MAC Protocol

    Page(s): 5821 - 5832
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (763 KB) |  | HTML iconHTML  

    This paper considers cross-layer medium access control (MAC) protocol design in wireless networks. Taking a mutually interactive MAC-PHY perspective, we aim to design an MAC protocol that is in favor of the physical (PHY) layer information transmission, and the improved PHY, in turn, can improve the MAC performance. More specifically, we propose a novel MAC protocol, named hybrid ALOHA, which makes it possible for collision-free channel estimation and simultaneous multiuser transmission. The underlying argument is as follows: As long as good channel estimation can be achieved, advanced signal processing does allow effective signal separation given that the multiuser interference is limited to a certain degree. Comparing with traditional ALOHA, there are more than one pilot subslots in each hybrid ALOHA slot. Each user randomly selects a pilot subslot for training sequence transmission. Therefore, it is possible for different users to transmit their training sequences over nonoverlapping pilot subslots and achieving collision-free channel estimation. Relying mainly on the general multipacket reception (MPR) model, in this paper, quantitative analysis is conducted for the proposed hybrid ALOHA protocol in terms of throughput, stability, as well as delay behavior. It is observed that significant performance improvement can be achieved in comparison with the traditional ALOHA protocol based either on the collision model or the MPR model. 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

Full Aims & Scope

Meet Our Editors

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